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Characterizing Tropical Forests with Multispectral Imagery Helmer, E.H., Nicholas R. Goodwin, Valéry Gond, Carlos M. Souza Jr., and Gregory P. Asner. 2015. Characterizing tropical forests with multispectral imagery. Chapter 14, pages 367-396 In Prasad S. Thenkabail, Ed., Land Resources: Monitoring, Modeling and Mapping. Series: Remote Sensing Handbook. Taylor & Francis Group, Boca Raton, FL, USA. 849 p. 14 Characterizing Tropical Forests with Multispectral Imagery Acronyms and Definitions ................................................................................................................ 363 14.1 Introduction ............................................................................................................................ 364 14.2 Multispectral Imagery and REDD+ .................................................................................... 364 Greenhouse Gas Inventories and Forest Carbon Offsets • Roles of Multispectral Imagery 14.3 Characteristics of Multispectral Image Types ................................................................... 365 14.4 Preprocessing Imagery to Address Clouds ......................................................................... 368 Cloud Screening • Filling Cloud and Scan-Line Gaps 14.5 Forest Biomass, Degradation, and Regrowth Rates from Multispectral Imagery ........371 E.H. Helmer Tropical Forest Biomass from High-Resolution Multispectral Imagery • Biomass, Age, and International Institute of Tropical Rates of Biomass Accumulation in Forest Regrowth • Limitations to Mapping Forest Biomass Forestry, USDA Forest Service or Age with One Multispectral Image Epoch • Detecting Tropical Forest Degradation with Multispectral Imagery Nicholas R. Goodwin 14.6 Mapping Tropical Forest Types with Multispectral Imagery .......................................... 377 Ecosciences Precinct Forest Types as Strata for REDD+ and Other C Accounting • High-Resolution Multispectral Imagery for Mapping Finely Scaled Habitats • Remote Tree Species Identification and Forest- Valéry Gond Type Mapping • Mapping Tropical Forest Types with Medium-Resolution Imagery • Species Forest Ecosystems Richness and Multispectral Imagery • Tropical Forest-Type Mapping at Coarse Spatial Goods and Services Scale • Tropical Forest-Type Mapping and Image Spatial Resolution Carlos M. Souza, Jr. 14.7 Monitoring Effects of Global Change on Tropical Forests................................................381 Progress in Monitoring Tropical Forests at Subcontinental to Global Scales • Feedbacks Instituto do Homen e Meio between Tropical Forest Disturbance and Drought Ambiente da Amazônia 14.8 Summary and Conclusions ................................................................................................... 382 Gregory P. Asner Acknowledgments .............................................................................................................................. 383 Carnegie Institution for Science References ............................................................................................................................................ 383 Acronyms and Definitions Fmask Function of Mask GHG Greenhouse Gas AB-C Aboveground live Biomass in units of Mg C ha−1 GLAS Geoscience Laser Altimeter System ACCA Automated Cloud Cover Assessment GV Green Vegetation (unitless fraction, range 0–1) AGLB Aboveground Live Forest Biomass in Mg dry HRG High-Resolution Geometric weight ha−1 HRV High-Resolution Visible ASTER Advanced Spaceborne Thermal Emission and HRVIR High-Resolution Visible and Infrared Reflection Radiometer HRS High-Resolution Stereoscopic AVHRR Advanced Very High Resolution Radiometer IRMSS Infrared Multispectral Camera AWiFS Advanced Wide Field Sensor IRS Indian Resources Satellite BRDF Bidirectional Reflectance Distribution Function INPE Instituto Nacional de Pesquisas Espaciais BB-C Belowground live Biomass in units of Mg C ha−1 LI-C Carbon content of forest floor litter in Mg C ha−1 CBERS China–Brazil Earth Resources Satellite LISS Linear Imaging Self-Scanner CDM Clean Development Mechanism MERIS Medium-Resolution Imaging Spectrometer DEM Digital Elevation Model MSS Multispectral Scanner DW-C Dead Wood biomass in units of Mg C ha−1 MVC Maximum-Value Compositing ESTARFM Enhanced Spatial and Temporal Adaptive NDFI Normalized Difference Fraction Index (unitless, Reflectance Fusion Model range −1 to 1) ETM+ Enhanced Thematic Mapper Plus NDMI Normalized Difference Moisture Index 363 K22130_C014.indd 363 4/2/2015 2:35:43 PM 364 Land Resources Monitoring, Modeling, and Mapping with Remote Sensing NPV Non-Photosynthetic Vegetation (unitless fraction, Multispectral satellite imagery, that is, remotely sensed range 0–1) imagery with discrete bands ranging from visible to shortwave Mg Megagram = 1 × 106 g = 1 metric ton infrared (SWIR) wavelengths, is the timeliest and most acces- MAIAC Multi-Angle Implementation of Atmospheric sible remotely sensed data for monitoring these forests. Given Correction for MODIS this relevance, we summarize here how multispectral imagery MISR Multi-angle Imaging SpectroRadiometer can help characterize tropical forest attributes of widespread MODIS Moderate Resolution Imaging Spectroradiometer interest, particularly attributes that are relevant to GHG emis- MVC Maximum-Value Compositing sion inventories and other forest C accounting: forest type, age, NDVI Normalized Difference Vegetation Index structure, and disturbance type or intensity; the storage, degra- NIR Near-Infrared dation, and accumulation of C in aboveground live tree biomass SMA Spectral Mixture Analysis (AGLB, in Mg dry weight ha−1); the feedbacks between tropical STARFM Spatial and Temporal Adaptive Reflectance Fusion forest degradation and climate; and cloud screening and gap fill- Model ing in imagery. In this chapter, the term biomass without further REDD+ Reducing Emissions from Deforestation and specification is referring to AGLB. Degradation, conservation of forest carbon stocks, sustainable management of forests, or enhance- ment of forest carbon stocks in developing 14.2 Multispectral Imagery and REDD+ countries −1 14.2.1 Greenhouse Gas Inventories SO-C Soil organic carbon in Mg C ha and Forest Carbon Offsets SPOT Satellite Pour l’Observation de la Terre SWIR Shortwave Infrared Multispectral satellite imagery can provide crucial data to inven- TM Thematic Mapper tories of forest GHG sinks and sources. Inventories of GHGs that UNFCCC United Nations Framework Convention on have forest components include national inventories for nego- Climate Change tiations related to the United Nations Framework Convention WiFS Wide Field Sensor on Climate Change (UNFCCC). The UNFCCC now includes a vision of compensating countries for reducing greenhouse gas 14.1 Introduction emissions to the atmosphere from deforestation, degradation, sustainable management of forests, or conservation or enhance- Tropical forests abound with regional and local endemic species ment of forest C stocks in developing countries (known as and house at least half of the species on earth, while covering REDD+). Inventories of GHG emissions for the UNFCCC Clean less than 7% of its land (Gentry, 1988; Wilson, 1988; as cited in Development Mechanism (CDM) may also include forests, and Skole and Tucker, 1993). Their clearing, burning, draining, and there are other forest carbon offset programs. harvesting can make slopes dangerously unstable, degrade water Programs like REDD+ could help moderate earth’s climate. resources, change local climate, or release to the atmosphere the They could also help conserve tropical forests and raise local organic carbon (C) that they store in their biomass and soils as incomes, as long as countries make these latter goals a priority greenhouse gases (GHGs). These forest disturbances accounted in REDD+ planning. Compensation in REDD+ is for organic for 19% or more of annual human-caused emissions of CO2 to carbon (C) stored in forest AGLB, dead wood, belowground live the atmosphere from the years 2000 to 2010, and that level is biomass, soil organic matter, or litter, as long as the stored C is more than the global transportation sector, which accounted “produced” by avoided GHG emissions, such as avoided defores- for 14% of these emissions. Forest regrowth from disturbances tation or avoided degradation of forest C stores. removes about half of the CO2 emissions coming from the for- In forest C offsets, avoided emissions are estimated as the dif- est disturbances (Houghton, 2013; IPCC 2014). Another GHG of ference between net GHG emissions that would have occurred concern when considering tropical forests is N2O released from without implementing change (the baseline case or business-as- forest fires. usual scenario) and actual net emissions that are reduced from Tropical forests (including subtropical forests) occur where what they would have been without the management change (the hard frosts are absent at sea level (Holdridge, 1967), which project case). Logging, burning, and fragmentation are examples means low latitudes, and where the dominant plants are trees, of disturbances that degrade forest C stores. Replacing conven- including palm trees, tall woody bamboos, and tree ferns. They tional logging with reduced impact logging reduces associated C include former agricultural or other lands that are now under- emissions and is an example of avoided C emissions. For subna- going forest succession (Faber-Langendoen et al., 2012). They tional projects such as those developed under voluntary carbon receive from <1000 mm year−1 of precipitation to more
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