CLASlite Publications
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.
Figure 2. Deforestation mapped by the most conservative option of 100% CLASlite v3.0 Artifact Remover. Over a 25-year period, the region lost 30% of its original 1986 forest cover at a gross deforestation rate 1.210% yr−1 .
- Center for Development Research (ZEF), Group Börner, Rheinische Friedrich-Wilhelm University, Germany
- Department of Global Ecology, Carnegie Institution for Science, USA
- Forest and Environment Program, Center for International Forestry Research, Indonesia
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.
Figure 2. The NEON Domains distributed across the continental United States, Alaska, Hawaii, and Puerto Rico. Specific information on individual domains can be found on the NEON Inc.
- The National Ecological Observatory Network, Inc., USA
- Department of Global Ecology, Carnegie Institution, USA
- Jet Propulsion Laboratory, USA
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.
- Department of Global Ecology, Carnegie Institution for Science, USA
- Potsdam Institute for Climate Impact Research (PIK), Germany
- International Max Planck Research School on Earth System Modelling, Germany
Figure 1. The estimated global footprint of deforestation (left) and selective logging operations (right) in the humid tropical forest biome.
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.
Figure 1. Comparison of GlobCover map (A) at 300 m × 300 m resolution with GLC-2000 map, (B) at 1 km × 1 km resolution over Rondonia, Brazil.
- Institute for Environment & Sustainability, Joint Research Centre of the European Commission, Italy
- Forest Assessment, Management & Conservation Division, United Nations Food & Agriculture Organization, Viale delle Terme di Caracalla,Italy
- European Space Agency, ESRIN, Via Galilleo Galilei, Italy
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.
- Department of Global Ecology, Carnegie Institution for Science, USA
Figure 6. Typical output from the AutoMCU sub-model run on the imagery shown in Fig. 2b. In each image pixel, the fractions of PV, NPV and bare substrate are expressed in percentages (0-100%). The areas in black include rivers, lakes, clouds and cloud shadows masked via the RMSE analysis immediately following the AutoMCU.
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.
- Department of Global Ecology, Carnegie Institution for Science, USA
Figure 1. Illustrated flow diagram depicting an approach to: (a) assess forest cover and disturbance with satellite imagery, (b) stratify the forested landscape, (c) sample the carbon densities of each major forest type using airborne light detection and ranging (LiDAR), and (d) estimate the baseline carbon stocks throughout the region of interest.
CLASlite Course support: (claslite_coursesupport@carnegiescience.edu)
CLASlite Technical support: (claslite_techsupport@carnegiescience.edu)
Carnegie Institution for Science
260 Panama Street • Stanford, CA 94305, USA
