Success Stories from the CLASlite Community
With more than 5,000 people from 137 countries signed up for CLASlite training and free software, there is a growing number of success stories among the user community. This section highlights a few of the many successes reported by you, our CLASlite Community!
Gold mining in Amazonia involves forest removal, soil excavation, and the use of liquid mercury, which together pose a major threat to biodiversity, water quality, forest carbon stocks, and human health. Within the global biodiversity hotspot of Madre de Dios, Peru, gold mining has continued despite numerous 2012 government decrees and enforcement actions against it. Mining is now also thought to have entered federally protected areas, but the rates of miner encroachment are unknown. Here, we utilize high-resolution remote sensing to assess annual changes in gold mining extent from 1999 to 2016 throughout the Madre de Dios region, including the high-diversity Tambopata National Reserve and buffer zone. Regionally, gold mining-related losses of forest averaged 4437 ha yr-1.
A temporary downward inflection in the annual growth rate of mining-related forest loss following 2012 government action was followed by a near doubling of the deforestation rate from mining in 2013-2014. The total estimated area of gold mining throughout the region increased about 40% between 2012 and 2016, including in the Tambopata National Reserve. Our results reveal an urgent need for more socio-environmental effort and law enforcement action to combat illegal gold mining in the Peruvian Amazon.
Institution: Department of Global Ecology, Carnegie Institution for Science
Forests and woodlands are a very important part of the global ecosystem through their provision of ecosystem goods and services. However, conversion to other land uses is one of the biggest threats to their existence. Remote sensing presents opportunities for monitoring such changes over wide and inaccessible areas including those areas that have no field data. In this study, we use the Carnegie Landsat Analysis System-lite (CLASlite) software and Landsat imagery to make the first spatially explicit national estimate deforestation in Swaziland. This was compared with deforestation data derived from the Global Forest Change (GFC) dataset for the period 2000-2014. The CLASlite analysis identified an estimated 46,620ha of forest and woodland lost between 1990 and 2015 resulting in a mean deforestation rate of 1704 ha yr-1. The GFC dataset, on the other hand, indicates a mean deforestation rate 1563 ha yr-1 when excluding forest regrowth. Validation of the results based on multi-year Google Earth and Landsat imagery indicated that both approaches are feasible for monitoring deforestation. The GFC data captured more forest loss within the dense plantation and wattle forests whilst underestimating deforestation within natural forests and woodlands. Although there are inter-annual variations, the rate of deforestation is generally increasing and widespread in many parts of the country mainly concentrated in the eastern half of the country and a few western parts where agriculture (particularly sugarcane), human settlements and other infrastructure developments are dominant land uses. Acacia and broadleaf savanna are being depleted at higher rates with up to 8.1% of forest area lost since the year 2000. Forest policies and legislation need to be reviewed to respond to the observed trends and patterns with a focus on forest conservation, climate change mitigation and adaptation.
Effects of drought on deforestation estimates from different classification methodologies: Implications for REDD+ and other payments for environmental services programs
Payments for ecosystem services (PES) programs depend on consistent environmental monitoring methodologies for measuring, reporting and verification. The case of carbon PES programs is instructive: land cover estimates are crucial for environmental monitoring efforts, but they require a consistent estimation method. Such consistency is however likely to be affected by climatic variability such as that seen during severe droughts. This raises questions as to whether distinct methodologies for land cover monitoring yield the same estimates of land- cover change over time in the presence of climatic variability. This study compares deforestation estimates from four methodologies during a normal year (2008) with those during a period of extreme drought (2010) in the Madre de Dios region of Peru in the Southwestern Amazon. The four methodologies compared are the automated classification with CLASlite 2.2, Tasseled Cap classification with ERDAS Imagine 9.2, Bhattacharya classification with SPRING 5.1, and Spectral Angle Mapper classification with ENVI 4.7. The results show differences in the forest and non-forest estimates derived from using ERDAS and CLASlite compared with those from using the SPRING and ENVI. These findings have implications for forest monitoring efforts for PES programs such as Reduced Emissions from Deforestation and Forest Degradation (REDD+) in the context of climate change.
Regional distribution of large blowdown patches across Amazonia in 2005 caused by a single convective squall line
In mid-January 2005 a convective squall line traversed 4.5 × 106 km2 of Amazonia from southwest to northeast. As seen in Landsat images, this atypical convective storm left blowdown imprints with diffuse geometry, unlike the fan-shaped wind disturbance of much more frequent east-to-west propagating squall lines. Previous work reported 0.2% of the forest area damaged by this one relatively rare event within one Landsat image and assumed similar disturbance across the entire traverse. We mapped convective wind damage impact to the region in 2005 by identifying large-scale (>4 ha) blowdown imprints in 30 Landsat images. The diffuse-type imprints associated with this single squall line contributed up to 60-72% of total 2005 wind-disturbed area detected across the region, but damage was highly concentrated in central Amazonia. Consequently, the distribution of large wind damage patches in 2005 across Amazonia was very different from long-term average. Regional distribution of wind-driven tree mortality for smaller patch sizes remains unknown.
Assessing and Monitoring Forest Degradation in a Deciduous Tropical Forest in Mexico via Remote Sensing Indicators
Assessing and monitoring forest degradation under national Monitoring, Verification and Reporting (MRV) systems in developing countries have been difficult to implement due to the lack of adequate technical and operational capacities. This study aims at providing methodological options for monitoring forest degradation in developing countries by using freely available remote sensing, forest inventory and ancillary data. We propose using Canopy Cover to separate, through a time series analysis approach using Landsat Imagery, forest areas with changes over time from sectors that report a "stable condition". Above ground Biomass and Net Primary Productivity derived from remote sensing data were used to define thresholds for areas considered degraded. The approach was tested in a semi-deciduous tropical forest in the Southeast of Mexico. The results showed that higher rates of forest degradation, 1596 to 2865 ha year-1, occur in areas with high population densities. The results also showed that 43% of the forests of the study area remain with no evident signs of degradation, as determined by the indicators used. The approach and procedures followed allowed for the identification and mapping of the temporal and spatial distribution of forest degradation, based on the indicators selected, and they are expected to serve as the basis for operations of the Reduction of Emissions from Deforestation and Forest Degradation (REDD+) initiative in Mexico and other developing countries, provided appropriate adaptations of the methodology are made to the conditions of the area in turn.
Remote sensing-based landscape indicators for the evaluation of threatened-bird habitats in a tropical forest
Avian species persistence in a forest patch is strongly related to the degree of isolation and size of a forest patch and the vegetation structure within a patch and its matrix are important predictors of bird habitat suitability. A combination of space-borne optical (Landsat), ALOS-PALSAR (radar), and airborne Light Detection and Ranging (LiDAR) data was used for assessing variation in forest structure across forest patches that had undergone different levels of forest degradation in a logged forest-agricultural landscape in Southern Laos. The efficacy of different remote sensing (RS) data sources in distinguishing forest patches that had different seizes, configurations, and vegetation structure was examined. These data were found to be sensitive to the varying levels of degradation of the different patch categories. Additionally, the role of local scale forest structure variables (characterized using the different RS data and patch area) and landscape variables (characterized by distance from different forest patches) in influencing habitat preferences of International Union for Conservation of Nature (IUCN) Red listed birds found in the study area was examined. A machine learning algorithm, MaxEnt, was used in conjunction with these data and field collected geographical locations of the avian species to identify the factors influencing habitat preference of the different bird species and their suitable habitats. Results show that distance from different forest patches played a more important role in influencing habitat suitability for the different avian species than local scale factors related to vegetation structure and health. In addition to distance from forest patches, LiDAR-derived forest structure and Landsat-derived spectral variables were important determinants of avian habitat preference. The models derived using MaxEnt were used to create an overall habitat suitability map (HSM) which mapped the most suitable habitat patches for sustaining all the avian species. This work also provides insight that retention of forest patches, including degraded and isolated forest patches in addition to large contiguous forest patches, can facilitate bird species retention within tropical agricultural landscapes. It also demonstrates the effective use of RS data in distinguishing between forests that have undergone varying levels of degradation and identifying the habitat preferences of different bird species. Practical conservation management planning endeavors can use such data for both landscape scale monitoring and habitat mapping.
Using spatial metrics and surveys for the assessment of trans-boundary deforestation in protected areas of the Maya Mountain Massif: Belize-Guatemala border
Understanding the trans-boundary deforestation history and patterns in protected areas along the Belize-Guatemala border is of regional and global importance. To assess deforestation history and patterns in our study area along a section of the Belize-Guatemala border, we incorporated multi-temporal deforestation rate analysis and spatial metrics with survey results. This multi-faceted approach provides spatial analysis with relevant insights from local stakeholders to better understand historic deforestation dynamics, spatial characteristics and human perspectives regarding the underlying causes thereof. During the study period 1991–2014, forest cover declined in Belize's protected areas: Vaca Forest Reserve 97.88%–87.62%, Chiquibul National Park 99.36%–92.12%, Caracol Archeological Reserve 99.47%–78.10% and Colombia River Forest Reserve 89.22%–78.38% respectively. A comparison of deforestation rates and spatial metrics indices indicated that between time periods 1991–1995 and 2012–2014 deforestation and fragmentation increased in protected areas. The major underlying causes, drivers, impacts, and barriers to bi-national collaboration and solutions of deforestation along the Belize-Guatemala border were identified by community leaders and stakeholders. The Mann-Whitney U test identified significant differences between leaders and stakeholders regarding the ranking of challenges faced by management organizations in the Maya Mountain Massif, except for the lack of assessment and quantification of deforestation (LD, SH: 18.67, 23.25, U = 148, p > 0.05). The survey results indicated that failure to integrate buffer communities, coordinate among managing organizations and establish strong bi-national collaboration has resulted in continued ecological and environmental degradation. The information provided by this research should aid managing organizations in their continued aim to implement effective deforestation mitigation strategies.
CLASlite algorithms and social surveys to asses and identify deforestation and forest degradation in Toledo's protected areas and forest ecosystems, Belize
In Belize, the lack of forest degradation and socioeconomic data results in the inability of forest management organizations to make timely assessments and decisions for sustainable forest resource management. This study uses CLASlite algorithms and social surveys to identify drivers, measure, analyze and map deforestation, and forest degradation that occurred in Toledo's ecosystems and Protected Areas as a result of the increased anthropogenic activity reported in 2010–2012. The social surveys indicated that land and institutional policy, distance to markets and lack of alternative livelihoods are the main drivers of deforestation and forest degradation. Of importance are the strong significant differences that exist between communities that were less than 2 km from a protected area (CL2K) and communities that were more than 2 km from a protected area (CM2K) regarding property rights (Cramer's V = 0.562, p < 0.001), selective logging (Cramer's V = 0.499, p < 0.001) and soil quality (Cramer's V = 0.434, p < 0.001). The results of the deforestation and forest degradation analysis indicate that in 2009–2011 and 2011–2012 the annual rates of deforestation were 0.75% (2480 ha) and 1.17% (3834 ha) respectively and the annual rates of forest degradation in 2009–2011 and 2011–2012 were 0.09% (307 ha) and 0.33% (1110 ha) respectively. In 2009–2011 only 9.34% of forest loss occurred inside protected areas in comparison to 2011–2012 where 23.97% of forest loss occurred inside protected areas. In 2011–2012 out of the 1110 ha of degradation 30.38% occurred in Lowland broad-leaved wet forest and 19.39% occurred in Sub-montane broad-leaved wet forest. The maps and statistics generated in this study pinpoint in which ecosystem types and protected areas major forest change and forest disturbance occurred. By utilizing the data generated by this study, Belize's forest management organizations will be able to efficiently allocate resources to forested areas that are being threatened; thus, more effectively mitigate deforestation and forest degradation of important forest
Spatially explicit forest carbon (C) monitoring aids conservation and climate change mitigation efforts, yet few approaches have been developed specifically for the highly heterogeneous landscapes of oceanic island chains that continue to undergo rapid and extensive forest C change. We developed an approach for rapid mapping of aboveground C density (ACD; units = Mg or metric tons C ha−1) on islands at a spatial resolution of 30 m (0.09 ha) using a combination of cost-effective airborne LiDAR data and full-coverage satellite data. We used the approach to map forest ACD across the main Hawaiian Islands, comparing C stocks within and among islands, in protected and unprotected areas, and among forests dominated by native and invasive species.
Integrating LiDAR-derived tree height and Landsat satellite reflectance to estimate forest regrowth in a tropical agricultural landscape
Remotely sensed data have revealed ongoing reforestation across many tropical landscapes. However, most studies have quantified changes between discrete land cover categories that are difficult to relate to the continuous changes in forest structure that underlie reforestation. Here, we demonstrate how generalized linear models (GLMs) can predict tree height and tree canopy cover from Landsat satellite reflectance in a 109 882 ha tropical agricultural landscape of western Panama. We derived tree canopy cover and tree height from airborne Light Detection and Ranging (LiDAR) data, and related these variables to the fraction of photosynthetic vegetation (PV) in Landsat pixels. We found large gains in predictive accuracy from modeling tree canopy height with a gamma GLM and tree canopy cover with a binomial GLM, relative to modeling these variables using linear regression. Adding social and environmental covariates to our GLMs, including topography and parcel membership (representing different land owners), increased predictive accuracy, resulting in best-fit models with an R2 of 55.68% and RMSE of 23.69% for tree canopy cover, and an R2 of 51.24% and RMSE of 3.42 m for tree height. Finally, we applied the GLMs to predict tree height and tree canopy cover in Landsat images from c. 2000 to 2012, and used results to quantify changes in forest structure during this 12-year period. We found that >60% of pixels in our study area had increased in tree height and tree canopy cover, suggesting widespread forest regrowth. These increases were spatially widespread across the study area, yet subtle, with most pixels increasing <2 m in tree height. Our results suggest ecological and agricultural changes that could be overlooked if measuring land cover change with discrete forest and non-forest categories. Overall, we show the advantages of linking LiDAR and Landsat data to quantify forest regrowth in an agricultural landscape.
A Quantitative Assessment of Forest Cover Change in the Moulouya River Watershed (Morocco) by the Integration of a Subpixel-Based and Object-Based Analysis of Landsat Data
A quantitative assessment of forest cover change in the Moulouya River watershed (Morocco) was carried out by means of an innovative approach from atmospherically corrected reflectance Landsat images corresponding to 1984 (Landsat 5 Thematic Mapper) and 2013 (Landsat 8 Operational Land Imager). An object-based image analysis (OBIA) was undertaken to classify segmented objects as forested or non-forested within the 2013 Landsat orthomosaic. A Random Forest classifier was applied to a set of training data based on a features vector composed of different types of object features such as vegetation indices, mean spectral values and pixel-based fractional cover derived from probabilistic spectral mixture analysis). The very high spatial resolution image data of Google Earth 2013 were employed to train/validate the Random Forest classifier, ranking the NDVI vegetation index and the corresponding pixel-based percentages of photosynthetic vegetation and bare soil as the most statistically significant object features to extract forested and non-forested areas. Regarding classification accuracy, an overall accuracy of 92.34% was achieved. The previously developed classification scheme was applied to the 1984 Landsat data to extract the forest cover change between 1984 and 2013, showing a slight net increase of 5.3% (ca. 8800 ha) in forested areas for the whole region.
The Assessment of Land Degradation and Desertification in Mexico: Mapping Regional Trend Indicators with Satellite Data
Understanding the patterns of land degradation and desertification to develop mitigation strategies requires identification of methods for accurate and spatially explicit assessment and monitoring. Remote sensing data offer the possibility to develop strategies that outline degradation and desertification. The free access policy on satellite imagery enables a new pathway to measure, assess, and monitor land degradation using indicators derived from multispectral satellite data. This chapter seeks to explore a methodology for land degradation and desertification assessment and monitoring, based on freely available multispectral satellite data. The method identifies net primary productivity (NPP) and canopy cover (CC) as indicators of degradation. The trajectories of these indicators show patterns and trends over time. The methodological development presented here is intended to be a tool for regional landscape monitoring and assessment, enabling the formulation of corrective action plans. This methodology was tested in a semi-deciduous ecosystem in the southeast of Mexico.
Tropical forests provide a crucial carbon sink for a sizable portion of annual global CO2emissions. Policies that incentivize tropical forest conservation by monetizing forest carbon ultimately depend on accurate estimates of national carbon stocks, which are often based on field inventory sampling. As an exercise to understand the limitations of field inventory sampling, we tested whether two common field-plot sampling approaches could accurately estimate carbon stocks across approximately 76 million ha of Perúvian forests. A 1-ha resolution LiDAR-based map of carbon stocks was used as a model of the country's carbon geography.
Ecology seeks general principles describing how the biota respond to multiple environmental factors, partly to build a more prognostic science in the face of global climate change. One such principle to emerge is the "leaf economics spectrum" (LES), which relates ecologically important plant nutrients to leaf construction and growth along simple relational axes. However, interrelationships between LES traits have not been tested at large geographic scales. Using airborne imaging spectroscopy and geospatial modeling, we discovered strong climatic and geophysical controls on LES traits and their interrelationships throughout Andean and western Amazonian forest canopies. This finding highlights the need for biogeographically explicit treatment of plant traits, afforded by imaging spectroscopy, in the next generation of biospheric models.
Over the past two decades, one of the research topics in which many works have been done is spatial modeling of biomass through synergies between remote sensing, forestry, and ecology. In order to identify satellite-derived indices that have correlation with forest structural parameters that are related with carbon storage inventories and forest monitoring, topics that are useful as environmental tools of public policies to focus areas with high environmental value. In this chapter, we present a review of different models of spatial distribution of biomass and resources based on remote sensing that are widely used. We present a case study that explores the capability of canopy fraction cover and digital canopy height model (DCHM) for modeling the spatial distribution of the aboveground biomass of two forests, dominated by Abies Religiosa and Pinus spp., located in Central Mexico. It also presents a comparison of different spatial models and products, in order to know the methods that achieved the highest accuracy through root-mean-square error. Lastly, this chapter provides concluding remarks on the case study and its perspectives in remote sensing-based biomass estimation.
Makira Natural Protected Area in northeastern Madagascar houses high levels of biodiversity, but is currently threated by encroachment of agriculture and the illegal logging of hardwoods. The Wildlife Conservation Society currently works with local communities in running a Reducing Emissions from Deforestation and Forest Degradation (REDD+) project in Makira. High cloud cover in humid forests and seasonal variations in vegetation make temporally consistent classifications difficult, affecting the estimates of deforestation needed for REDD+. Carnegie Landsat Analysis System lite, or CLASlite, is a free software tool designed to pre-process and analyze remotely sensed data, creating forest and deforestation maps, between other outputs. This study was performed in collaboration with WCS using fifteen Landsat 5, 7, and 8 images to determine the performance of CLASlite in comparison to previous mapping work using a conventional mapping approach. A combination of CLASlite and expert interpretation created four forest vs. non-forest maps from 1994, 2001, 2010, and 2014 with 92% overall accuracy in the 26,956 km² study area. Over the twenty year period, 7.9% of the 11, 464 km² of original forest was lost. Visual comparisons with the previous mapping work revealed that CLASlite was better at detecting small patches of forest as well as connectivity in riparian areas. Various cloud masking settings within CLASlite allowed for adjustments specific to cloud and sensor types. Cloud contaminations were minimized by utilizing Landsat tiles from the dry season. CLASlite is well designed for conservation practitioners and performed well in this forest type.
Expansion of oil palm plantations and forest cover changes in Bungo and Merangin Districts, Jambi Province, Indonesia
It is often cited that large scale oil palm plantation were responsible for forest cover changes in Sumatra and Kalimantan. Objective of the research was to identify whether oil palm concessions were the direct cause of intact forest cover changes in study area. The study areas are situated at Jambi Province, Indonesia and are experiencing rapid expansion of oil palm plantation. We used Landsat temporal images from year 1988, 1990, 2000, 2007, and 2013 to detect forest cover change. We also made use Carnegie Landsat Analysis System-Lite (CLASlite) fractional cover module to differentiate undisturbed (intact), disturbed (logged) forest and also oil palm growing stages on Landsat images. Our study showed that, there were only 8% of oil palm plantation development occurred by direct clearing of intact forest in the study area in the last 25 years. Oil palm concessions in the last 25 years were mostly developed on logged forest, agroforests, and shrub lands.
Tropical forests are vast and scientifically underexplored places. Biotic losses, gains, and reorganization within these systems go undetected due to a lack of access to technologies needed to monitor forest cover, composition, and carbon content. Provision of forest cover-monitoring tools for non-scientists has, thus, become a focus of innovation in the remote-sensing community, while advances in high-resolution forest carbon and biodiversity mapping science has progressed more slowly. This paper focuses on high-resolution remote-sensing developments to measure and monitor tropical forest canopies at the "organismic scale," which is the resolution that resolves individual canopies and species throughout the forest landscape. Emphasis is placed on how forest carbon stocks can be mapped with precision and accuracy comparable to that of field-based estimates. Biodiversity mapping poses the greatest challenge, but recent advances in three-dimensional functional and structural trait imaging can reveal variation in species richness, abundance, and functional diversity over large geographic regions. The pay-off in pursuing these studies will be a vastly improved understanding of tropical biodiversity patterns and their underlying ecological and evolutionary drivers, which will have positive cascading effects on conservation decision-making and resource policy development.
Historical and potential extinction of shrub and tree species through deforestation in the department of Antioquia, Colombia
We assessed the expected historical and current species richness of shrubs and trees in the Department of Antioquia, northwest region of Colombia. We used the Fisher's alpha value associated with the pooled dataset of identified species in 16 1-ha plots that were used to extrapolate the scaled species richness of the Antioquia Province under three different scenarios: 1) the entire region before deforestation began, assuming an original forest cover of around 92% of the entire province (excluding paramos, rivers, and lakes). 2) The forest cover in 2010. 3) The expected forest cover in 2100 assuming the observed deforestation rate between 2000 and 2010 as a constant. We found that, despite relatively low local and global losses of species, global extinctions in terms of number of species could be dramatically high due to the high endemism and deforestation rates.
Comparison of data gap-filling methods for Landsat ETM+ SLC-off imagery for monitoring forest degradation in a semi-deciduous tropical forest in Mexico
A number of methods to overcome the 2003 failure of the Landsat 7 Enhanced Thematic Mapper (ETM+) scan-line corrector (SLC) are compared in this article in a forest-monitoring application in the Yucatan Peninsula, Mexico. The objective of this comparison is to determine the best approach to accomplish SLC-off image gap-filling for the particular landscape in this region, and thereby provide continuity in the Landsat data sensor archive for forest-monitoring purposes. Four methods were tested: (1) local linear histogram matching (LLHM); (2) neighbourhood similar pixel interpolator (NSPI); (3) geostatistical neighbourhood similar pixel interpolator (GNSPI); and (4) weighted linear regression (WLR). All methods generated reasonable SLC-off gap-filling data that were visually consistent and could be employed in subsequent digital image analysis. Overall accuracy, kappa coefficients (κ), and quantity and allocation disagreement indices were used to evaluate unsupervised Iterative Self-Organizing Data Analysis (ISODATA) land-cover classification maps. In addition, Pearson correlation coefficients (r) and root mean squares of the error (RMSEs) were employed for estimates agreement with fractional land cover. The best results visually (overall accuracy > 85%, κ < 9%, quantity disagreement index < 5.5%, and allocation disagreement index < 12.5%) and statistically (r > 0.84 and RMSE < 7%) were obtained from the GNSPI method. These results suggest that the GNSPI method is suitable for routine use in reconstructing the imagery stack of Landsat ETM+ SLC-off gap-filled data for use in forest-monitoring applications in this type of heterogeneous landscape.
Remote sensing is gaining considerable traction in forest monitoring efforts, with the Carnegie Landsat Analysis System lite (CLASlite) software package and the Global Forest Change dataset (GFCD) being two of the most recently developed optical remote sensing-based tools for analysing forest cover and change. Due to the relatively nascent state of these technologies, their abilities to classify land cover and monitor forest dynamics have yet to be evaluated against more established approaches. Here, we compared maps of forest cover and change produced by the more traditional supervised classification approach with those produced by CLASlite and the GFCD, working with imagery collected over Sierra Leone, West Africa. CLASlite maps of forest change from 2001-2007 and 2007-2014 exhibited the highest overall accuracies (79.1% and 89.6%, respectively) and, importantly, the greatest capacity to discriminate natural from planted mature forest growth. CLASlite's comparative advantage likely derived from its more robust sub-pixel classification logic and numerous user-defined parameters, which resulted in classified products with greater site relevance than those of the two other classification approaches. In light of today's continuously growing body of analytical toolsets for remotely sensed data, our study importantly elucidates the ways in which methodological processes and limitations inherent in certain classification tools can impact the maps they are capable of producing, and demonstrates the need to understand and weigh such factors before any one tool is selected for a given application.
Forest canopy structure is strongly influenced by environmental factors and disturbance, and in turn influences key ecosystem processes including productivity, evapotranspiration and habitat availability. In tropical forests increasingly modified by human activities, the interplay between environmental factors and disturbance legacies on forest canopy structure across landscapes is practically unexplored. We used airborne laser scanning (ALS) data to measure the canopy of old-growth and selectively logged peat swamp forest across a peat dome in Central Kalimantan, Indonesia, and quantified how canopy structure metrics varied with peat depth and under logging. Several million canopy gaps in different height cross-sections of the canopy were measured in 100 plots of 1 km2 spanning the peat dome, allowing us to describe canopy structure with seven metrics. Old-growth forest became shorter and had simpler vertical canopy pro- files on deeper peat, consistent with previous work linking deep peat to stunted tree growth. Gap size frequency distributions (GSFDs) indicated fewer and smaller canopy gaps on the deeper peat (i.e. the scaling exponent of Pareto functions increased from 1.76 to 3.76 with peat depth). Areas subjected to concessionary logging until 2000, and illegal logging since then, had the same canopy top height as old-growth forest, indicating the persistence of some large trees, but mean canopy height was significantly reduced. With logging, the total area of canopy gaps increased and the GSFD scaling exponent was reduced. Logging effects were most evident on the deepest peat, where nutrient depletion and waterlogged conditions restrain tree growth and recovery. A tight relationship exists between canopy structure and peat depth gradient within the old-growth tropical peat swamp forest. This relationship breaks down after selective logging, with canopy structural recovery, as observed by ALS, modulated by environmental conditions. These findings improve our understanding of tropical peat swamp ecology and provide important insights for managers aiming to restore degraded forests.
Spatiotemporal patterns of tropical deforestation and forest degradation in response to the operation of the Tucuruí hydroelectric dam in the Amazon basin
The planned construction of hundreds of hydroelectric dams in the Amazon basin has the potential to provide invaluable 'clean' energy resources for aiding in securing future regional energy needs and continued economic growth. These mega-structures, however, directly and indirectly interfere with natural ecosystem dynamics, and can cause noticeable tree loss. To improve our understanding of how hydroelectric dams affect the surrounding spatiotemporal patterns of forest disturbances, this case study integrated remote sensing spectral mixture analysis, GIS proximity analysis and statistical hypothesis testing to extract and evaluate spatially-explicit patterns of deforestation (clearing of entire forest patch) and forest degradation (reduced tree density) in the 80,000 km2 neighborhoods of the Brazil's Tucuruí Dam, the first large-scale hydroelectric project in the Amazon region, over a period of 25 years from 1988 to 2013. Results show that the average rates of deforestation were consistent during the first three time periods 1988-1995 (620 km2 per year), 1995-2001 (591 km2 per year), and 2001-2008 (660 km2 per year). However, such rate dramatically fell to half of historical levels after 2008, possibly reflecting the 2008 global economic crisis and enforcement of the Brazilian Law of Environmental Crimes. The rate of forest degradation was relatively stable from 1988 to 2013 and, on average, was 17.8% of the rate of deforestation. Deforestation and forest degradation were found to follow similar spatial patterns across the dam neighborhoods, upstream reaches or downstream reaches at the distances of 5 km-80 km, suggesting that small and large-scale forest disturbances may have been influencing each other in the vicinity of the dam. We further found that the neighborhoods of the Tucuruí Dam and the upstream region experienced similar degrees of canopy loss. Such loss was mainly attributed to the fast expansion of the Tucuruí town, and the intensive logging activities alongside major roads in the upstream reservoir region. In contrast, a significantly lower level of forest disturbance was discovered in the downstream region.
Forests are changing faster today than at any time since the Ice Age. The human enterprise has driven vast forest losses and a mounting wake of forest regrowth that defies historical biogeography. As the pace of change accelerates, so does the demand to monitor forests for conservation and resource policy.
When I was a graduate student in the 1990s, mapping Amazon deforestation was science at the bleeding edge. New reports of rainforest losses were trickling in, creating shock waves around the world. Although the problem of deforestation was known at the time, new satellite-based maps provided a strong propellant for rainforest action. The satellite of choice for deforestation monitoring was the National Aeronautics and Space Administration's Landsat, and images sold for more than $2,000. I vividly remember that breathtaking moment when my professor purchased three images for my thesis project. However, at least 250 cloud-free Landsat images are required to make one decent map of the Amazon each year. Back then only a few elite groups could afford such a trove of satellite data, and there were even fewer experts with their secret methods to convert impenetrable Landsat pixels into user-friendly maps.
Carbon-centric conservation strategies such as the United Nation's program to Reduce CO2Emissions from Deforestation and Degradation (REDD+), are expected to simultaneously reduce net global CO2 emissions and mitigate species extinctions in regions with high endemism and diversity, such as the Tropical Andes Biodiversity Hotspot. Using data from the northern Andes, we show, however, that carbon-focused conservation strategies may potentially lead to increased risks of species extinctions if there is displacement (i.e., "leakage") of land-use changes from forests with large aboveground biomass stocks but relatively poor species richness and low levels of endemism, to forests with lower biomass stocks but higher species richness and endemism, as are found in the Andean highlands (especially low-biomass non-tree growth forms such as herbs and epiphytes that are often overlooked in biological inventories). We conclude that despite the considerable potential benefits of REDD+ and other carbon-centric conservation strategies, there is still a need to develop mechanisms to safeguard against possible negative effects on biodiversity in situations where carbon stocks do not covary positively with species diversity and endemism.
Accurate and spatially-explicit maps of tropical forest carbon stocks are needed to implement carbon offset mechanisms such as REDD+ (Reduced Deforestation and Degradation Plus). The Random Forest machine learning algorithm may aid carbon mapping applications using remotely-sensed data. However, Random Forest has never been compared to traditional and potentially more reliable techniques such as regionally stratified sampling and upscaling, and it has rarely been employed with spatial data. Here, we evaluated the performance of Random Forest in upscaling airborne LiDAR (Light Detection and Ranging)-based carbon estimates compared to the stratification approach over a 16-million hectare focal area of the Western Amazon. We considered two runs of Random Forest, both with and without spatial contextual modeling by including-in the latter case-x, and y position directly in the model. In each case, we set aside 8 million hectares (i.e., half of the focal area) for validation; this rigorous test of Random Forest went above and beyond the internal validation normally compiled by the algorithm (i.e., called "out-of-bag"), which proved insufficient for this spatial application. In this heterogeneous region of Northern Peru, the model with spatial context was the best preforming run of Random Forest, and explained 59% of LiDAR-based carbon estimates within the validation area, compared to 37% for stratification or 43% by Random Forest without spatial context. With the 60% improvement in explained variation, RMSE against validation LiDAR samples improved from 33 to 26 Mg C ha-1 when using Random Forest with spatial context. Our results suggest that spatial context should be considered when using Random Forest, and that doing so may result in substantially improved carbon stock modeling for purposes of climate change mitigation.
To implement the REDD+ mechanism (Reducing Emissions for Deforestation and Forest Degradation, countries need to prioritize areas to combat future deforestation CO2 emissions, identify the drivers of deforestation around which to develop mitigation actions, and quantify and value carbon for financial mechanisms. Each comes with its own methodological challenges, and existing approaches and tools to do so can be costly to implement or require considerable technical knowledge and skill. Here, we present an approach utilizing a machine learning technique known as Maximum Entropy Modeling (Maxent) to identify areas at high deforestation risk in the study area in Madre de Dios, Peru under a business-as-usual scenario in which historic deforestation rates continue. We link deforestation risk area to carbon density values to estimate future carbon emissions. We quantified area deforested and carbon emissions between 2000 and 2009 as the basis of the scenario.
Influence of watershed-climate interactions on stream temperature, sediment yield, and metabolism along a land use intensity gradient in Indonesian Borneo
Oil palm plantation expansion into tropical forests may alter physical and biogeochemical inputs to streams, thereby changing hydrological function. In West Kalimantan, Indonesia, we assessed streams draining watersheds characterized by five land uses: intact forest, logged forest, mixed agroforest, and young (<3 years) and mature (>10 years) oil palm plantation. We quantified suspended sediments, stream temperature, and metabolism using high-frequency submersible sonde measurements during month-long intervals between 2009 and 2012. Streams draining oil palm plantations had markedly higher sediment concentrations and yields, and stream temperatures, compared to other streams. Mean sediment concentrations were fourfold to 550-fold greater in young oil palm than in all other streams and remained elevated even under base flow conditions. After controlling for precipitation, the mature oil palm stream exhibited significantly greater sediment yield than other streams. Young and mature oil palm streams were 3.9°C and 3.0°C warmer than the intact forest stream (25°C). Across all streams, base flow periods were significantly warmer than times of stormflow, and these differences were especially large in oil palm catchments. Ecosystem respiration rates were also influenced by low precipitation. During an El Niño-Southern Oscillation-associated drought, the mature oil palm stream consumed a maximum 21 g O2 m-2 d-1 in ecosystem respiration, in contrast with 2.8 ± 3.1 g O2m-2 d-1 during nondrought sampling. Given that 23% of Kalimantan's land area is occupied by watersheds similar to those studied here, our findings inform potential hydrologic outcomes of regional periodic drought coupled with continued oil palm plantation expansion.
We use social ecological systems theory (SES) to analyse change in forest communities in the northern Bolivian Amazon. SES characterizes interdependent dynamics of social and ecological systems and we hypothesized it to be a useful frame to grasp dynamics of forest communities affected by changes in forest policies, regulations and institutions, as well as economic demands and conservation objectives. We analysed the long-term historical changes since the region became incorporated in the global tropical forest product value chain since the late 19th century and quantitatively analysed changes in 85 forest communities between 1997 and 2009. We collected information on 16 variables related to demographic, productive, and socio-economic characteristics. Results show that forest communities have experienced major changes and have adapted to these changes. Social thresholds, a key concept in SES, are consistent with multiple social economic forces experienced by forest communities. Detrimental feed-back effects of SES can be confronted when innovative exploration mechanisms, such as new productive chains are developed, or the agro-extractive cycles of current productive system are expanded. Competition among households, population growth and more profitable economic opportunities may threaten benign forms of forest products extraction that have persisted through various cycles of internal and external changes.
An Assessment of the Forest Allowance Programme in the Juma Sustainable Development Reserve in Brazil
This paper evaluates the introduction of the Bolsa Floresta Programme (BFP) in the Juma Sustain able Develop ment Reserve in Brazil. The BFP in Juma is a validated REDD+ project emphasizing payments for ecosystem services. The analyses are based on interviews with about 25% of the households in Juma, local leaders and representatives of the organiser of the BFP - the Fundação Amazonas Sustentável (FAS). The strategy of FAS is to avoid deforestation by providing support to local communities to improve their liveli hoods. The paper analyses the influence of the BFP and the rules of the reserve on people's livelihoods, the local perception of the programme and the interactions between FAS and the local communities. It appears that the BFP is more of a development programme than a standard payment for ecosystem services initiative. As such it seems to have good potential, while we note that the main environmental effects are expected to materialize mainly in the future.
We conducted an analysis of deforestation and forest disturbance from 2005-2011 in Masoala National Park, the largest federal protected area in Madagascar. We found that the annual rate of forest change in 2010-2011 within the park (1.27%) was considerably higher than in 2005-2008 (0.99%), and was higher than the most recently published deforestation rate for all of Madagascar. Although deforestation and disturbance immediately following the 2009 coup d'état were lower than in the other time periods analyzed, the longer-term increase in forest change over the study period corroborates recent ground-based accounts of increased illegal activities in national parks, including logging of precious hardwoods. We also analyzed forest disturbance patterns in relation to rivers and travel distance from permanent villages. Forest disturbances were significantly closer to rivers than expected by chance, and 82% of disturbance was within the mean maximum travel distance to villages surrounding the park. Both results strongly indicate that most of the mapped disturbance in the study area is anthropogenic, despite two cyclones during the study period. High-resolution forest monitoring ensures that forest change statistics accurately reflect anthropogenic disturbances and are not inflated by forest losses resulting from natural processes.
Gold mining has rapidly increased in western Amazonia, but the rates and ecological impacts of mining remain poorly known and potentially underestimated. We combined field surveys, airborne mapping, and high-resolution satellite imaging to assess road- and river-based gold mining in the Madre de Dios region of the Peruvian Amazon from 1999 to 2012. In this period, the geographic extent of gold mining increased 400%. The average annual rate of forest loss as a result of gold mining tripled in 2008 following the global economic recession, closely associated with increased gold prices. Small clandestine operations now comprise more than half of all gold mining activities throughout the region. These rates of gold mining are far higher than previous estimates that were based on traditional satellite mapping techniques. Our results prove that gold mining is growing more rapidly than previously thought, and that high-resolution monitoring approaches are required to accurately quantify human impacts on tropical forests.
Extreme Differences in Forest Degradation in Borneo: Comparing Practices in Sarawak, Sabah, and Brunei
The Malaysian states of Sabah and Sarawak are global hotspots of forest loss and degradation due to timber and oil palm industries; however, the rates and patterns of change have remained poorly measured by conventional field or satellite approaches. Using 30 m resolution optical imagery acquired since 1990, forest cover and logging roads were mapped throughout Malaysian Borneo and Brunei using the Carnegie Landsat Analysis System. We uncovered ∼364,000 km of roads constructed through the forests of this region. We estimated that in 2009 there were at most 45,400 km2 of intact forest ecosystems in Malaysian Borneo and Brunei. Critically, we found that nearly 80% of the land surface of Sabah and Sarawak was impacted by previously undocumented, high-impact logging or clearing operations from 1990 to 2009. This contrasted strongly with neighbouring Brunei, where 54% of the land area remained covered by unlogged forest. Overall, only 8% and 3% of land area in Sabah and Sarawak, respectively, was covered by intact forests under designated protected areas. Our assessment shows that very few forest ecosystems remain intact in Sabah or Sarawak, but that Brunei, by largely excluding industrial logging from its borders, has been comparatively successful in protecting its forests.
Oil palm supplies >30% of world vegetable oil production1 . Plantation expansion is occurring throughout the tropics, predominantly in Indonesia, where forests with heterogeneous carbon stocks undergo high conversion rates2-4 . Quantifying oil palm's contribution to global carbon budgets therefore requires refined spatio-temporal assessments of land cover converted to plantations5,6 . Here, we report oil palm development across Kalimantan (538,346 km2 ) from 1990 to 2010, and project expansion to 2020 within government-allocated leases. Using Landsat satellite analyses to discern multiple land covers, coupled with above- and below-ground carbon accounting, we develop the first high-resolution carbon flux estimates from Kalimantan plantations. From 1990 to 2010, 90% of lands converted to oil palm were forested (47% intact, 22% logged, 21% agroforests). By 2010, 87% of total oil palm area (31,640 km2 ) occurred on mineral soils, and these plantations contributed 61-73% of 1990-2010 net oil palm emissions (0.020-0.024 GtC yr-1 ). Although oil palm expanded 278% from 2000 to 2010, 79% of allocated leases remained undeveloped. By 2020, full lease development would convert 93,844 km2 (∼90% forested lands, including 41% intact forests). Oil palm would then occupy 34% of lowlands outside protected areas. Plantation expansion in Kalimantan alone is projected to contribute 18-22% (0.12-0.15 GtC yr-1 ) of Indonesia's 2020 CO2-equivalent emissions. Allocated oil palm leases represent a critical yet undocumented source of deforestation and carbon emissions.
High fidelity carbon mapping has the potential to greatly advance national resource management and to encourage international action toward climate change mitigation. However, carbon inventories based on field plots alone cannot capture the heterogeneity of carbon stocks, and thus remote sensing-assisted approaches are critically important to carbon mapping at regional to global scales. We advanced a high-resolution, national-scale carbon mapping approach applied to the Republic of Panama - one of the first UN REDD + partner countries.
Bamboo-Dominated Forests of the Southwest Amazon: Detection, Spatial Extent, Life Cycle Length and Flowering Waves
We map the extent, infer the life-cycle length and describe spatial and temporal patterns of flowering of sarmentose bamboos (Guadua spp) in upland forests of the southwest Amazon. We first examine the spectra and the spectral separation of forests with different bamboo life stages. False-color composites from orbital sensors going back to 1975 are capable of distinguishing life stages. These woody bamboos flower produce massive quantities of seeds and then die. Life stage is synchronized, forming a single cohort within each population. Bamboo dominates at least 161,500 km2 of forest, coincident with an area of recent or ongoing tectonic uplift, rapid mechanical erosion and poorly drained soils rich in exchangeable cations. Each bamboo population is confined to a single spatially continuous patch or to a core patch with small outliers. Using spatial congruence between pairs of mature-stage maps from different years, we estimate an average life cycle of 27-28 y. It is now possible to predict exactly where and approximately when new bamboo mortality events will occur. We also map 74 bamboo populations that flowered between 2001 and 2008 over the entire domain of bamboo-dominated forest. Population size averaged 330 km2. Flowering events of these populations are temporally and/or spatially separated, restricting or preventing gene exchange. Nonetheless, adjacent populations flower closer in time than expected by chance, forming flowering waves. This may be a consequence of allochronic divergence from fewer ancestral populations and suggests a long history of widespread bamboo in the southwest Amazon.
Bamboo-Dominated Forests of the Southwest Amazon: Detection, Spatial Extent, Life Cycle Length and Flowering Waves
We map the extent, infer the life-cycle length and describe spatial and temporal patterns of flowering of sarmentose bamboos (Guadua spp) in upland forests of the southwest Amazon. We first examine the spectra and the spectral separation of forests with different bamboo life stages. False-color composites from orbital sensors going back to 1975 are capable of distinguishing life stages. These woody bamboos flower produce massive quantities of seeds and then die. Life stage is synchronized, forming a single cohort within each population. Bamboo dominates at least 161,500 km2 of forest, coincident with an area of recent or ongoing tectonic uplift, rapid mechanical erosion and poorly drained soils rich in exchangeable cations. Each bamboo population is confined to a single spatially continuous patch or to a core patch with small outliers. Using spatial congruence between pairs of mature-stage maps from different years, we estimate an average life cycle of 27-28 y. It is now possible to predict exactly where and approximately when new bamboo mortality events will occur. We also map 74 bamboo populations that flowered between 2001 and 2008 over the entire domain of bamboodominated forest. Population size averaged 330 km2 . Flowering events of these populations are temporally and/or spatially separated, restricting or preventing gene exchange. Nonetheless, adjacent populations flower closer in time than expected by chance, forming flowering waves. This may be a consequence of allochronic divergence from fewer ancestral populations and suggests a long history of widespread bamboo in the southwest Amazon.
Global tropical deforestation continues to occur at high rates despite political attention. National-level forest baselines are being established all over the world to guide the implementation of several policy mechanisms. However, identifying the direct and indirect drivers of deforestation and understanding the complexity of their interlinkages are often difficult. We first analyzed deforestation between 1990 and 2005 at the national level and found an annual deforestation rate of 0.62 %. Next, we performed separate analyses for four natural regions in Colombia and found annual deforestation rates between 0.42 and 1.92 %. Using general linear models, we identified several direct causes and underlying factors influencing deforestation at the national level: rural population density, cattle, protected areas, and slope. Significant differences in deforestation rates and causes were found across regions. In the Caribbean region, drivers of loss are urban population, unsatisfied basic needs, slope, and precipitation and four land use variables (illicit crops, pastures, cattle, and fires). In the Orinoco region, crops are the main driver of forest loss, and in the Amazonian region, deforestation is primarily due to fires related to the colonization front. Policy mechanisms will have to take into account regional patterns to successfully balance development and forest preservation in Colombia.
The Carnegie Landsat Analysis System-lite (CLASlite) was used to map and monitor tropical forest change in two large tropical watersheds in Peru: Greater Marañón and Ucayali. CLASlite uses radiometric and atmospheric correction algorithms as well as an Automated Monte Carlo Unmixing (AutoMCU) to obtain consistent fractional land cover per-pixel at high spatial resolution. Fractional land cover is automatically extracted from universal spectral libraries which allow for a differentiation between live photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV) and bare substrate (S). Fractional cover information is directly translated to maps of forest cover based in the physical characteristics of the forest canopy. Rates of deforestation and disturbance are estimated through analysis of change in fractional land cover over time. The Greater Marañón and Ucayali watersheds were studied over the period 1985 to 2012, through analysis of 1900 multi-spectral images from Landsat 4, 5 and 7. These images were processed and analyzed using CLASlite to obtain fractional cover and forest cover information for each year within the period. Annualization of the collected maps provided detailed information on the gross rates of disturbance and deforestation throughout the region. Further, net deforestation and disturbance maps were used to show the general forest change in these watersheds over the past 25 years. We found that deforestation accounts for just ~50% of the total forest losses, and that forest disturbance (degradation) is critically important to consider when making forest change estimates associated with losses in habitat and carbon in the region. These results also provide spatially-detailed, temporally-specific information on forest change for nearly three decades. Information provided by this study will assist decision-makers in Peru to improve their regional environmental management. The results, unprecedented in spatial and temporal scope, are another example showing the fidelity of tropical deforestation and forest degradation monitoring made routine using the CLASlite system.
Accurate, high-resolution mapping of aboveground carbon density (ACD, Mg C ha-1) could provide insight into human and environmental controls over ecosystem state and functioning, and could support conservation and climate policy development. However, mapping ACD has proven challenging, particularly in spatially complex regions harboring a mosaic of land use activities, or in remote montane areas that are difficult to access and poorly understood ecologically. Using a combination of field measurements, airborne Light Detection and Ranging (LiDAR) and satellite data, we present the first large-scale, high-resolution estimates of aboveground carbon stocks in Madagascar.
High-resolution mapping of tropical forest carbon stocks can assist forest management and improve implementation of large-scale carbon retention and enhancement programs. Previous high-resolution approaches have relied on field plot and/or light detection and ranging (LiDAR) samples of aboveground carbon density, which are typically upscaled to larger geographic areas using stratification maps. Such efforts often rely on detailed vegetation maps to stratify the region for sampling, but existing tropical forest maps are often too coarse and field plots too sparse for highresolution carbon assessments. We developed a top-down approach for high-resolution carbon mapping in a 16.5 million ha region (> 40 %) of the Colombian Amazon - a remote landscape seldom documented. We report on three advances for large-scale carbon mapping: (i) employing a universal approach to airborne LiDAR-calibration with limited field data; (ii) quantifying environmental controls over carbon densities; and (iii) developing stratification- and regression-based approaches for scaling up to regions outside of LiDAR coverage. We found that carbon stocks are predicted by a combination of satellite-derived elevation, fractional canopy cover and terrain ruggedness, allowing upscaling of the LiDAR samples to the full 16.5 million ha region. LiDAR-derived carbon maps have 14 % uncertainty at 1 ha resolution, and the regional map based on stratification has 28 % uncertainty in any given hectare. High-resolution approaches with quantifiable pixel-scale uncertainties will provide the most confidence for monitoring changes in tropical forest carbon stocks. Improved confidence will allow resource managers and decision makers to more rapidly and effectively implement actions that better conserve and utilize forests in tropical regions.
Current markets and international agreements for reducing emissions from deforestation and forest degradation (REDD) rely on carbon (C) monitoring techniques. Combining field measurements, airborne light detection and ranging (LiDAR)-based observations, and satellite-based imagery, we developed a 30-meter-resolution map of aboveground C density spanning 40 vegetation types found on the million-hectare Island of Hawaii. We estimate a total of 28.3 teragrams of C sequestered in aboveground woody vegetation on the island, which is 56% lower than Intergovernmental Panel on Climate Change estimates that do not resolve C variation at fine spatial scales. The approach reveals fundamental ecological controls over C storage, including climate, introduced species, and land-use change, and provides a fourfold decrease in regional costs of C measurement over field sampling alone.
Experience with forest management interventions has shown that the design, strategic context and implementation of projects at the local level are key determinants of intervention success. Gaining a strategic understanding of local REDD+ initiatives is therefore important for the further development and governance of the international REDD+ regime. This article reports on an exploratory comparative analysis of 12 REDD+ projects in the Madre de Dios watershed of south eastern Peru. Using a framework drawn from innovation strategy, we focus on the founding and organizational strategies of the different initiatives, thus allowing us to compare across the 12 cases and to explore how these local initiatives link with the emerging national REDD+ architecture in Peru.
Our results point to the importance of hybrid institutional logics, the key role played by highly networked individuals in pushing project-level REDD+ forward, and of understanding the construction of the REDD+ credit value chain as the fundamental innovation taking place; the development of standards, technologies and other norms are complementary to the basic task of defining and reconfiguring roles on this chain. We suggest that decision makers should continue to encourage the 'bottom-up' construction of REDD+ as a strategy to encourage innovation and flexibility, and facilitate research into the governance and transnational systemic nature of the emerging value chain.
Efforts to mitigate climate change through the Reduced Emissions from Deforestation and Degradation (REDD) depend on mapping and monitoring of tropical forest carbon stocks and emissions over large geographic areas. With a new integrated use of satellite imaging, airborne light detection and ranging, and field plots, we mapped aboveground carbon stocks and emissions at 0.1-ha resolution over 4.3 million ha of the Peruvian Amazon, an area twice that of all forests in Costa Rica, to reveal the determinants of forest carbon density and to demonstrate the feasibility of mapping carbon emissions for REDD. We discovered previously unknown variation in carbon storage at multiple scales based on geologic substrate and forest type. From 1999 to 2009, emissions from land use totaled 1.1% of the standing carbon throughout the region. Forest degradation, such as from selective logging, increased regional carbon emissions by 47% over deforestation alone, and secondary regrowth provided an 18% offset against total gross emissions. Very high-resolution monitoring reduces uncertainty in carbon emissions for REDD programs while uncovering fundamental environmental controls on forest carbon storage and their interactions with land-use change.
Advances in Airborne Remote Sensing of Ecosystem Processes and Properties – Toward High-Quality Measurement on a Global Scale
Airborne remote sensing provides the opportunity to quantitatively measure biochemical and biophysical properties of vegetation at regional scales, therefore complementing surface and satellite measurements. Next-generation programs are poised to advance ecological research and monitoring in the United States, the tropical regions of the globe, and to support future satellite missions. The Carnegie Institution will integrate a next generation imaging spectrometer with a waveform LiDAR into the Airborne Taxonomic Mapping System (AToMS) to identify the chemical, structural and taxonomic makeup of tropical forests at an unprecedented scale and detail. The NEON Airborne Observation Platform (AOP) is under development with similar technologies with a goal to provide long-term measurements of ecosystems across North America. The NASA Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRISng) is also under development to address the science measurement requirements for both the NASA Earth Science Research and Analysis Program and the spaceborne NASA HyspIRI Mission. Carnegie AToMS, NEON AOP, and AVIRISng are being built by the Jet Propulsion Laboratory as a suite of instruments. We discuss the synergy between these programs and anticipated benefits to ecologists and decision-makers.
New deforestation and selective logging data and climate change projectionssuggest that biodiversity refugia in humid tropical forests may change moreextensively than previously reported. However, the relative impacts from climatechange and land use vary by region. In the Amazon, a combination ofclimate change and land use renders up to 81% of the region susceptible torapid vegetation change. In the Congo, logging and climate change could negativelyaffect the biodiversity in 35-74% of the basin. Climate-driven changesmay play a smaller role in Asia-Oceania compared to that of Latin Americaor Africa, but land use renders 60-77% of Asia-Oceania susceptible to majorbiodiversity changes. By 2100, only 18-45% of the biome will remain intact.The results provide new input on the geography of projected climate changerelative to ongoing land-use change to better determine where biological conservationmight be most effective in this century.
This article covers the very recent developments undertaken for estimating tropical deforestation from Earth observation data. For the United Nations Framework Convention on Climate Change process it is important to tackle the technical issues surrounding the ability to produce accurate and consistent estimates of GHG emissions from deforestation in developing countries. Remotely-sensed data are crucial to such efforts. Recent developments in regional to global monitoring of tropical forests from Earth observation can contribute to reducing the uncertainties in estimates of carbon emissions from deforestation. Data sources at approximately 30 m × 30 m spatial resolution already exist to determine reference historical rates of change from the early 1990s. Key requirements for implementing future monitoring programs, both at regional and pan-tropical regional scales, include international commitment of resources to ensure regular (at least yearly) pan-tropical coverage by satellite remote sensing imagery at a sufficient level of detail; access to such data at low-cost; and consensus protocols for satellite imagery analysis.
Monitoring deforestation and forest degradation is central to assessing changes in carbon storage, biodiversity, and many other ecological processes in tropical regions. Satellite remote sensing is the most accurate and cost-effective way to monitor changes in forest cover and degradation over large geographic areas, but the tools and methods have been highly manual and time consuming, often requiring expert knowledge. We present a new user-friendly, fully automated system called CLASlite, which provides desktop mapping of forest cover, deforestation and forest disturbance using advanced atmospheric correction and spectral signal processing approaches with Landsat, SPOT, and many other satellite sensors. CLASlite runs on a standard Windows-based computer, and can map more than 10,000 km2, at 30 m spatial resolution, of forest area per hour of processing time. Outputs from CLASlite include maps of the percentage of live and dead vegetation cover, bare soils and other substrates, along with quantitative measures of uncertainty in each image pixel. These maps are then interpreted in terms of forest cover, deforestation and forest disturbance using automated decision trees. CLASlite output images can be directly input to other remote sensing programs, geographic information systems (GIS), Google EarthTM serif}, or other visualization systems. Here we provide a detailed description of the CLASlite approach with example results for deforestation and forest degradation scenarios in Brazil, Peru, and other tropical forest sites worldwide.
Large-scale carbon mapping is needed to support the UNFCCC program to reduce deforestation and forest degradation (REDD). Managers of forested land can potentially increase their carbon credits via detailed monitoring of forest cover, loss and gain (hectares), and periodic estimates of changes in forest carbon density (tons ha-1). Satellites provide an opportunity to monitor changes in forest carbon caused by deforestation and degradation, but only after initial carbon densities have been assessed. New airborne approaches, especially light detection and ranging (LiDAR), provide a means to estimate forest carbon density over large areas, which greatly assists in the development of practical baselines. Here I present an integrated satellite-airborne mapping approach that supports high-resolution carbon stock assessment and monitoring in tropical forest regions. The approach yields a spatially resolved, regional state-of-the-forest carbon baseline, followed by high-resolution monitoring of forest cover and disturbance to estimate carbon emissions. Rapid advances and decreasing costs in the satellite and airborne mapping sectors are already making high-resolution carbon stock and emissions assessments viable anywhere in the world.
Advancing reference emission levels in subnational and national REDD+ initiatives: a CLASlite approach
Conservation and monitoring of tropical forests requires accurate information on their extent and change dynamics. Cloud cover, sensor errors and technical barriers associated with satellite remote sensing data continue to prevent many national and sub-national REDD+ initiatives from developing their reference deforestation and forest degradation emission levels. Here we present a framework for large-scale historical forest cover change analysis using free multispectral satellite imagery in an extremely cloudy tropical forest region. The CLASlite approach provided highly automated mapping of tropical forest cover, deforestation and degradation from Landsat satellite imagery. Critically, the fractional cover of forest photosynthetic vegetation, non-photosynthetic vegetation, and bare substrates calculated by CLASlite provided scene-invariant quantities for forest cover, allowing for systematic mosaicking of incomplete satellite data coverage. A synthesized satellite-based data set of forest cover was thereby created, reducing image incompleteness caused by clouds, shadows or sensor errors. This approach can readily be implemented by single operators with highly constrained budgets. We test this framework on tropical forests of the Colombian Pacific Coast (Chocó) - one of the cloudiest regions on Earth, with successful comparison to the Colombian government's deforestation map and a global deforestation map.
The use of Landsat time series for identification of forest degradation levels in the eastern Brazilian Amazon (Paragominas)
Forest degradation is the reduction of the capacity of a forest to provide goods and services. In the Brazilian Amazon region, degraded forests are dominant in the pioneer front landscapes where the economic growth and agricultural expansion has converted the primary forest to a mosaic of pastures, crop lands and forests in various stages of degradation. Understanding spatial structure and temporal trajectories of forest degradation is important to support forest conservation and management, aiming at reducing greenhouse gas emissions, and retrieving biomass and biological diversity. We present a method to evaluate and map different levels of forest degradation in the eastern Amazon in Paragominas district. We used Landsat time series (2000-2015) and the CLASlite software. We calculated the bands of Photosynthetic Vegetation (PV), Non-Photosynthetic Vegetation (NPV) and bare Soil (S), for each year to produce annual maps of forest degradation and we have analyzed the variance of each CLASlite band (PV, NPV, S) along the 2000-2015 period. Field observations of degraded forests, based on indicators of forest structure were used for the validation. The results showed that the CLASlite bands variances are a good indicator of the forest degradation process.
In the Brazilian Amazon, multiple logging activities are undergoing, involving different actors and interests. They shape a disturbance gradient bound to intensity and frequency of logging, and forest management techniques. However, until now, few studies were carried out at the landscape scale taking into account these multiple types of logging and this disturbance gradient. Here we address this issue of how to account for the multiple logging activities shaping the current forest landscape. We developed an inexpensive and efficient remote sensing methodology based on Landsat imagery to detect and track logging activity based on the monitoring of canopy opening. Then, we implemented a set of remote sensing indicators to follow the different trajectories of forest disturbance through time. Using these indicators, we emphasized five major spatial and temporal disturbance patterns occurring in the municipality of Paragominas (State of Pará, Brazilian Amazon), from well-managed forests to highly over-logged forests. Our disturbance indicators put in evidence a significant difference between legal and illegal patterns, with some illegal areas having suffered more than three explorations in fifteen years. They also clearly underlined the efficiency of RIL techniques applied under FSC guidelines to reduce the logging impacts in terms of canopy opening. For these reasons, we argue the need to promote legal certified logging to conserve forests, as without them, many actors mine the forest resources without any concerns for future stocks. Finally, our remote tracking methodology, which produces easy to interpret disturbance indicators, could be a real boon to forest managers including for conservationists working in protected areas and stakeholders dealing with international trade rules such as RBUE or FLEGT.
In Belize, the lack of deforestation, forest degradation, socioeconomic and erosion data results in the inability of management organizations to make timely assessments and decisions for sustainable resource management in southern Belize. This study uses CLASlite algorithms, statistical analysis, social surveys and the Revised Universal Soil Loss Equation to identify erosion hotspots, drivers, measure, analyze and map deforestation, and forest degradation that occurred in southern Belize. In Toledo, land and institutional policy, distance to markets and lack of alternative livelihoods are the main drivers of deforestation and forest degradation. The results of the deforestation and forest degradation analysis indicate that in 2009-2011 and 2011-2012 the annual rates of deforestation were 0.75% (2,480 ha) and 1.17% (3,834 ha) respectively and the annual rates of forest degradation in 2009-2011 and 2011-2012 were 0.09% (307 ha) and 0.33% (1,110 ha) respectively. Moreover, along the Belize-Guatemala border, forest declined from 96.9% to 85.7% in Belize and from 83.15% to 31.52% in Guatemala. The Mann-Whitney U test identified significant differences between leaders and stakeholders regarding the ranking of challenges faced by management organizations in the Belize-Guatemala border, except for the lack of assessment and quantification of deforestation (LD, SH: 18.67, 23.25, U = 148, p > .05). Finally, in Toledo's Rio Grande watershed erosion hotspots were located in the upper-mid reaches of the watershed near the communties of Crique Jute, Naluum Ca, San Pedro Columbia and San Miguel. The Mann-Whitney U test identified significant difference in the ranking of erosion drivers (cattle ranching, logging, and clearing of slopes) between communities. This research provides significant information on the drivers, deforestation, forest degradation and erosion that will aid stakeholders to garner community support, develop and implement sustainable management practices in southern Belize.
Recently, several remote sensing methods have been developed to quantify the degradation of tropical forests. However, it still lacks finest spatial and temporal analysis to define trajectories of forest degradation i.e. a temporal analysis of the impacts on forest integrity. This communication aims to explore this issue and proposes a set of operational indicators to monitor forest degradation, which can constitutes a decision tool to support forestry managers and policy makers. We studied the trajectories of forest degradation in the municipality of Paragominas - PA in the eastern Brazilian Amazon between 1995 and 2009, with a focus on the forestry company Cikel (400 000 ha certified by FSC since 2001). First, we developed a semi-automatic remote sensing methodology to detect forest degradation using multi-temporal Landsat images (spatial resolution of 30m) covering the 1995-2009 period. This method included two steps: 1) Identification of logging tracks and log landings using an algorithm of Bourbier et al. (2013). This algorithm uses spectral indices and morphological filters to strengthen the spectral contrasts between bare soil and forest cover. 2) Identification of logging gaps - which are characterised by senescent vegetation due to trees fall - using a Spectral Mixture Analysis carried out in CLASlite (Asner et al., 2009) and a fraction index (Souza et al., 2013). So, we obtained annual maps identifying these three major impacts. Secondly, we calculated annual landscape metrics of forest degradation using the R package "SpatialEco". Then, we calculated indicators which synthetize information about logging impacts and logging frequencies over the period from these annual degradation metrics. Finally, we selected a set of 6 indicators and statistically analysed the trajectories of degradation occurring in Paragominas using ACP and CAH. Our results emphasize four major degradation trajectories from well managed forests to highly-logged forests. They clearly show a difference between legal and illegal logging in terms of forest degradation. Moreover, they indicate that impacts of FSC certification on forest degradation was positive. Degradation was statistically lower in the certified logged plots compared to the uncertified plots. These set of indicators are adequate to monitor forest degradation through space and provide guidance to policy-makers for a better management of forest resources.
Climate change projections have predicted more frequent and severe droughts that may lead to major loss of trees and subsequent range shifts. Drought-induced tree mortality leaves both dead & live trees intermixed that remain standing rather than leaving clearings that result from acute disturbances such as fire. Thus this disturbance is difficult to detect at a regional scale, but is a harbinger of range shifts so its detection is high priority. During the summer of 2011, the southwestern US including Texas was impacted by an extreme drought. Statewide tree mortality was observed and thus provided an opportunity to test the efficacy of moderate to coarse resolution remotely- sensed indicators to detect, map and enumerate drought-induced tree mortality. Calibration models of 250-m ΔNDVI and 1-km VegDRI with 599 field data plots of tree mortality were developed to produce predictive maps. ΔNDVI, ΔPV, and NPV mortality indices were derived from 30-m Landsat 7 and compared to each other and 250-m ΔNDVI. ΔNDVI predicted tree mortality best (Khat = 0.15), with an estimate of 9% mortality that was primarily concentrated in East and Central Texas. However at 30-m resolution for East Texas, ΔPV matched the validation data best (Khat = 0.21).
Maximum entropy models were used with the field data to test the relative importance of 2011 drought conditions versus historical climate drivers of the distribution of drought-induced tree mortality. 2011 drought conditions explained 57% of the resulting model (AUC = 0.84) and bioclimate variables explained 43%. Mean annual precipitation explained 17% of tree mortality, followed by 2011 isothermality (16%). Models were run to test the contribution of edaphic, biotic, and climatic factors toward explaining dead tree distribution, and also test of effects of scale and location (East vs. Central Texas). Climate was the highest contributor at the state scale (42%) and also in Central Texas (48%). In East Texas, edaphic factors were the major driver (47%). As drought frequency and intensity increase as predicted, a refinement of detection techniques and understanding of the drivers of tree mortality are needed to understand and predict the nature of drought consequences for forests.
Estimation of Above Ground Forest Biomass by Integrating Airborne LiDAR, Satellite Imagery and In-Situ Measurement in Subtropical Mountain Forests of Nepal
Selection of robust, accurate and cost efficient forest biomass monitoring and carbon mapping methods are essential to carry out Reducing Emission from Deforestation and Degradation (REDD+) program of the United Nations Framework Convention on Climate Change (UNFCCC). There are numerous tools applied for Forest Resource Assessment (FRA) ranging from traditional intensive field measurement to sophisticated airborne Light Detection and Ranging (LiDAR) technology. The key driving force behind the advancement of FRA methods in course of time is to obtain accurate forest information at low cost.
Tropical forests provide important ecosystem functions (e.g. primary production, carbon and nitrogen cycling, the hydrogen cycle, etc.) as well as ecosystem services (e.g. pharmaceuticals, timber, air and water purification, climate stabilization, etc.) (Tallis et al. 2013; Virginia and Wall 2013). Despite their important role in providing such functions and services, tropical forests and the species within them are increasingly being lost, due in part to increasing deforestation (Hansen et al. 2013). One recent study, for instance, places 36%-57% of Amazon tree species at risk of extinction (ter Steege et al. 2015). Where forests are communities of trees, i.e. inter-species associations (Chave 2009; Duckworth et al. 2000; Helmer et al. 2012)) and not merely random collections of individuals, there is therefore a need to understand the patterns of the diversity of tree communities in the tropics. Understanding the patterns of tree diversity is ultimately also necessary for adequate management and conservation of tropical forests, and such information can also aid in evaluating how forests will respond to future environmental perturbations.
The anthropogenic use of natural resources has become a major cause ofbiodiversity loss and habitat degradation throughout the world. Deforestation - theconversion of forests to alternative land covers - has led to a decrease in localbiodiversity directly through a decrease in habitat, and indirectly through habitatfragmentation. Likewise, defaunation - the loss of animals both directly through huntingand indirectly through deforestation - has led to the empty forest syndrome andsubsequent deterioration of forest ecosystems. In many cases, areas where anthropogenicuse of natural resources is high overlap with areas of high biodiversity value. Therefore,the present series of studies aims to better understand the impacts that different types ofnatural resources use and habitat degradation have on biodiversity. This dissertationdetails the results of five studies, which aimed to: 1) examine the effects of habitatdegradation on plant-frugivore networks; 2), understand the live capture and extent ofownership of lemurs in Madagascar; 3) understand the micro- and macro-level drivers ofwild meat consumption in Madagascar; 4) describe the capture, movement, and trade ofwild meat in Madagascar; and 5) the impacts of habitat changes on the diets and verticalstratification of frugivorous bats.
Land use change is a significant factor in environmental conservation and climate change which may be positive or negative depending on how it occurred. It aimed at examining the land use changes that took place across the catchment from 1984 to date. Landsat images of the area were downloaded from USGS database and processed using CLASlite, a forest monitoring application developed by Carnegie Institute for Science (CIS) and ENVI software for land use classification and change detection. Over the study period, the area was observed to have experienced different modifications, most notable one being deforestation in the upper part of the catchment where Mount Kenya Forest extended. Most of the deforestation was understood to have taken place in the 1980s and 1990s with 2.5% forest depletion between 1984 and 1995. That may be attributed to illegal logging because up to 60% of the lost forest area during the period was taken by uncultivated land.
Optical remote sensing is used in different manners in the context of tropical forests monitoring: Land-cover characterization (forest types) Measuring deforestation (change detection) Estimation of degradation (under development). Objectives of this presentation are: (1) show the potential of optical remote sensing for land cover characterization in space and time; (2) check the deforestation information already available; (3) make a review of what is done in terms of degradation; (4) present perspectives in the scaling-up workflow we develop.
Peru has endured a long history with malaria, an infectious disease caused by the mosquito-borne transmission of the Plasmodium parasite. Throughout the 20th century, disease prevalence has varied tremendously with a number of factors including Peru's growth and development, variable support for malaria control measures, and the migration of immunologically naïve populations. However, many researchers believe that anthropogenic deforestation is at the root of a recent resurgence of malaria in the Peruvian Amazon. Deforestation creates favorable conditions for disease transmission by increasing mosquito habitat and placing humans in close proximity to more abundant disease vectors. In addition, rural communities often lack the resources to combat malaria due to the prohibitive cost of conventional technologies and lack of access to health care. Using data derived from field collections and remotely sensed images in the Loreto department of Peru, this study proposes a new method for characterizing malaria risk in the Peruvian Amazon. A variety of novel geospatial and remote sensing techniques were used to develop environmental layers from satellite imagery and produce the species distribution model. A geospatial risk model synthesized the predicted mosquito habitat and associated community risk factors into an assessment of malaria exposure risk. The threat model developed from this study can be used to create maps that will help local communities manage their malaria risk. Management efforts, such as the reduction of available mosquito breeding habitat, can be concentrated in areas identified as high-risk for malaria exposure.
The conservation and development of forests are vital to the welfare of human beings. Forests management is essential to maintain social, economic and ecological services. Forrest monitoring allows to track their state of health and productivity, in order to conduct proper management, according to the state of resources, to enhance their functionality and promote conservation. Remote sensors, optical and radar, offer the possibility of locating changes in forest areas using various analysis techniques, ranging from the purely visual interpretation to the implementation of a fully automated algorithm. This report is a review of the literature on the techniques used to observe changes in forest cover and monitoring through remote sensing.
Satellite and Airborne Mapping and Data Sets for Forest Monitoring and Assessment of Carbon Stocks in the Philippines: An Expository Review
Satellite data obtained through remote sensing has been a common and widespread source of information for forest monitoring. This is, in part, due to its ability to cover large tracts of forest lands, and its accessibility, availability, and cost. However, its spatial resolution can be coarse and may not be adequate to meet certain level of accuracy and uncertainty. Airborne-generated data sets have higher spatial resolutions and better accuracy, but can be very costly to obtain. To monitor large forest areas for various purposes, including forest carbon assessment under the Reduced Emission from Deforestation and forest Degradation (REDD+) program of the UN Framework Convention for Climate Change (UNFCCC), it is imperative to develop tools and methodologies that are cost efficient and meets acceptable level of accuracy. This paper explores different tools and methodologies that combines data obtained from medium resolution imagery obtained from remote sensing and high resolution data sets obtained from airborne sensors such as LiDAR and radar. These tools will be applied and tested using data obtained from pilot REDD+ areas.
Promoting Forest Stewardship in the Bolsa Floresta Programme: Local Livelihood Strategies and Preliminary Impacts
The Bolsa Floresta Programme (BFP) is an incentive-based forest conservation initiative of the State of Amazonas (Brazil). Launched in 2007, the programme was among the first initiatives in Brazil that relied on direct and conservation-conditional incentives to protect forests at a large scale. One of the 15 sustainable development reserves (SDR) enrolled in the programme, the SDR Juma, became Brazil's first certified REDD project, and also the first worldwide to receive the "gold" status of the Climate, Community & Biodiversity Alliance (CCBA). This study characterizes the BFP intervention context and documents preliminary impacts, with the objective to identify lessons learned for this and other conservation initiatives in the Amazon, and beyond. It relies on household survey data collected in two BFP reserves, the SDR Juma and Uatumã, as well as some remote sensing-based analyses that cover the programme's total intervention area. Here we summarize key findings on (1) the predominant livelihood strategies of programme participants and non-participants, respectively, inside and outside the SDRs Juma and Uatumã, (2) recent trends in land cover change in and around the two reserves, (3) preliminary evidence on BFP impact, and (4) the main lessons from our study.
Deforestation in the Kayabi Indigenous Territory: Simulating and Predicting Land Use and Land Cover Change in the Brazilian Amazon
The Amazon basin sustains more than half of the world's remaining tropical rain-forest and plays a vital role in maintaining biodiversity, climate and terrestrial carbon storage. The Amazon has the world's highest absolute rates of deforestation. Land use/cover change (LUCC) practices in the Brazilian Amazon, such as cattle ranching, logging, agriculture, mining, and urbanization are the major contributors to deforestation and have major impacts on ecosystems and environmental processes at local, regional and global scales. Such impacts include land fragmentation and degradation, biodiversity loss, alteration in atmospheric composition and climate change. Understanding the determinants of LUCC is vital for developing sustainable environmental management policies and forest protection. Modelling provides insights on land use dynamics and the driving factors of change and allows to quantitatively predicting where future change might occur. A simulation of future landscape in 2020 in the Kayabi Indigenous Territory in the Brazilian Amazon was carried out using Geographic Information Systems (GIS), Remote Sensing and the IDRISI's Land Change Modeler following five sequential steps: (1) Creation of forest land cover maps from 2000, 2006 and 2009 derived from CLASlite's fractional cover image; (2) Land-change cover analysis by cross-tabulating forest land cover maps; (3) Calculation of transition potentials from forest to anthropogenic disturbance using a MLP neural network methodology. Afterwards, a prediction of future landscape was simulated using a Markovian process; (4) Assessment of the model performance by predicting a 2009 land cover and comparing it with an actual 2009 land cover map and (5) Predicting a 2020 land cover map. The model was able to successfully simulate deforestation expansion in the region and identify the main landscape attributes driving anthropogenic disturbance expansion in the studied area. Distance from roads and distance from existing disturbance were found as the key factors driving deforestation in the Kayabi area
Effects of oil palm plantation development on land cover, carbon flux, and streams in Indonesian Borneo
Agricultural expansion is the predominant mode of tropical land cover change, leading to profound alterations in vegetation, carbon stocks, and freshwater systems. The dynamics of these ecosystem changes depend on land cover trajectories preceding agricultural conversion. Assessing ecological outcomes from major land cover transitions is therefore critical for reducing uncertainties about how food production affects the human-natural system. This dissertation examines the influence of oil palm plantation expansion on land cover and ecosystem processes at nested regional (Kalimantan, 538,346 km2 ) and local (Ketapang, 12,038 km2 ) scales. Major regional land covers were classified from a timeseries of Landsat satellite images. Using a spatially-explicit model of oil palm expansion, future land cover change was assessed under various scenarios. Carbon emissions from plantations were estimated with a carbon bookkeeping model of above- and below-ground carbon flux from deforestation, forest degradation, vegetation regrowth, and peatland soil burning and draining. To discern the effects of plantation development on freshwater ecosystems, streams draining watersheds dominated by forests, agroforests, and oil palm were monitored from 2008-2012. From 1990-2010 across Kalimantan, -70% of oil palm expansion cleared intact and logged forests. In Ketapang, plantation land sources exhibited distinctive temporal dynamics, composing mostly forests on mineral soils from 1994-2001, shifting to peatlands from 2008-2011. If all government-allocated plantation leases are developed, oil palm will occupy 34% of Kalimantan lowlands (< 300m) outside of protected areas. Such rapid plantation expansion affects ecological processes at multiple scales. Locally, results indicate that plantation land use significantly alters stream metabolism, temperature, and sediment loads; moreover, such changes persist as oil palm matures. Regionally, Kalimantan oil palm plantations are projected to contribute 18-22% (0.12- 0.15 GtC y ') of Indonesia's 2020 CO2 equivalent emissions. Analysis of Ketapang scenario model outcomes suggests that emissions mitigation will require protection of existing carbon stocks. While prohibiting intact and logged forest and peatland conversion to oil palm reduces emissions only 4% below BAU, protecting intact and logged forests achieves 21% carbon emissions reductions. These findings demonstrate significant trade-offs between large-scale tropical agricultural production and maintenance of ecosystem processes crucial to human wellbeing across local-to-global scales.
In many tropical countries forest are destroyed to expand timber, mining and agricultural industries and are affected by infrastructure investments such roads and dams. Deforestation rates in Suriname have been historically low due to the low population pressure and relative remoteness. Suriname's status as High Forest Low Deforestation (HFLD) country is set to change if planned infrastructure investments (a hydrodam, a road to Brazil and agriculture extension with prospects for biofuels) through the heart of the country realize, moreover, if low institutional capacity and environmental regulations continue inhibiting the capacity response of governments to control the destruction of tropical forest overlapping greenstone deposits. Analytical and empirical studies have shown that an important determinant of deforestation is the improved access to previously inaccessible forested areas alongside low governance gradients with high socio-economic value. Timely information about the underlying and proximate drivers of actual and future deforestation and on the location and extent of expected deforestation is one condition to properly manage this process of forest cover destruction. Therefore, this study uses spatial deforestation models to assess the influence of environmental drivers on forest cover change and to project future deforestation trends. During the first stage of this project, forest cover maps were developed for 2005 and 2009 based on Landsat 5TM images. The resulting forest cover maps were used in a spatial explicit model which calculates forest change rates and simulates deforestation between 2009 and 2020 based on the spatial distribution of spatial variables and a historical deforestation scenario assuming that deforestation trajectories into the future will continue under the historical trend found between the period analyzed. The model demonstrates how land use, infrastructure, socio-economic aspects and biophysical features drive forest loss in Suriname. With the outcomes of this research the researchers expect to be able to demonstrate the potential of this type of studies to visualize the effects of land use decisions on forest conservation along future infrastructure developments in the country, and to inform these decisions so that they minimize undue negative impacts on forest-dependent people and forest.
A contributor to continued biodiversity loss is the absence of market mechanisms to finance the recovery and protection of globally important species and habitats. In this, the Year of Biodiversity and the Year of the Tiger, we propose a change in the status quo of undervaluing wildlife through a new model in which conservation of endangered species and habitats becomes financially attractive to investors. We describe the start-up and operation of a wildlife premium market+REDD, a market that will add value to proposed carbon payments, established under international mechanisms and located in globally important areas for biodiversity. The initial example focuses on one critical target: recovering endangered wide-ranging mammal species, specifically tigers. We explain the type of monitoring, reporting, and verification required to make such a market credible based on the best science available. The wildlife premium concept can also be applied to conserving a global portfolio of biodiversity priorities and we offer such a framework. We believe this new market mechanism has the potential to: 1) arrest the continued loss of species and erosion of global biodiversity in tropical forests; 2) create payment schemes to improve livelihoods for the rural poor who live near areas of high biodiversity value; and 3) serve as a dividend, an additive to carbon offsets to better value functional ecosystems with intact wildlife resources. The failure of moral arguments alone to persuade governments and local stakeholders to conserve endangered wildlife calls for bold new approaches to avert the great extinction crisis that looms on the horizon.