Linear mixture model classi cation of burned forests in the Eastern Amazon
|
|
- Stewart Jennings
- 5 years ago
- Views:
Transcription
1 int. j. remote sensing, 1998, vol. 19, no. 17, 3433± 3440 Linear mixture model classi cation of burned forests in the Eastern Amazon M. A. COCHRANE² and C. M. SOUZA Jr² ² Instituto do Homem e Meio Ambiente da Amazoà nia (IMAZON), Caixa Postal 1015, Bele m, Para Brazil Biology Department, Pennsylvania State University, University Park, Pennsylvania 16802, USA (Received 7 October 1997; in nal form 26 June 1998 ) Abstract. A methodology is described for detecting and classifying burned forests in Amazonia. Linear mixture models using three image endmembers (vegetation, soil, shade) were used to separate forest from non-forest. Forested areas were unmixed using vegetation, non-photosynthetic vegetation (NPV) and shade endmembers and reclassi ed as unburned, recently burned and older burned forests. The NPV fraction provided the greatest separability of the forest classes and has potential for subclassi cation of burned areas into damage classes. For 184 km 2 of burned forest, a conservative estimate of 9% (22 metric tons haõ 1 ) of living biomass was lost due to forest res between 1991± Introduction Currently, it is estimated that km 2 of forest in the Brazilian Amazon are selectively logged each year (Verõ  ssimo and Amaral 1996). Evidence of re in selectively logged forests of the eastern Amazon is already common and widespread. In 1995 alone, it is estimated that 21% of land holdings in this region burned, with the area of standing forest a ected by res exceeding new deforestation by three-fold (Alencar et al ). However, despite the prevalence of res in these forests, the use of satellite sensor imagery for classi cation of burned forests has met with limited success. The detection of re damage is complicated by the rapid regrowth of ground vegetation after res (Lefebvre and Stone 1994) and the heterogeneity of burned forests (Cochrane and Schulze, in press). Furthermore, classi cation of burned forests has been di cult due to the low temporal (Landsat-TM) or spatial (AVHRR) resolution of current sensors (Setzer et al. 1994). Given the large amount of previously burned forest and logged forest which already exist and the obvious fact that these forests are becoming a larger element of the landscape, the time has come to study these `new landscape elements. In this letter, we provide preliminary results of our use of linear mixture models for classifying burned forests and the potential for quantifying damage levels within these forests. 2. Methods 2.1. Study area This study was conducted within a 1406 km 2 area just south of the logging town of Tailaà ndia, ParaÂ, in Brazil. The landscape in the study region is a mosaic of 0143± 1161/ 98 $12.00 Ñ 1998 Taylor & Francis Ltd
2 3434 M. A. Cochrane and C. M. Souza Jr pastures, small agricultural plots, natural forest, second growth forest, unburned logged and burned logged forests. The forest of this region is tropical moist evergreen on latosol soils. The region is subject to a strong dry season from June through November and averages between 1500 and 1800 mm annual rainfall (Cochrane and Schulze, in press) Ground data and remotely sensed data Bands 1± 5, 7 of two Landsat TM images (18 August 1991, 18 June 1993, path 223/062) with sensor radiometric corrections were acquired from INPE (Brazilian Space Agency). Nearest neighbor resampling was used to register the subsets (1406 km 2 ) of the two images (rms= 0.75 pixels) used in this research. The images were geometrically corrected using di erentially corrected GPS points strati ed throughout the imaged region (12 GCPs; rms= 0.71 pixels). Atmospheric e ects were ignored since no atmospheric calibration data are available for this region. The 1991 image was used as a reference for evidence of pre-existing burned forests. Descriptive ground data of the region s forests were collected from ten 0.5 ha plots within a 100 km 2 subsection of the study area in October 1996 (table 1) and included a full inventory of all trees> 10 cm dbh as well as interviews with landowners (Cochrane and Schulze, in press). In addition, forests along all roads within the study area were visited and investigated for evidence of previous burning Mixing models The images were processed as follows ( gure 1). Endmembers were extracted from the image and selected using principal component axes 1 and 2 (Smith et al. 1985); corresponding to 98% of the multi-spectral data variance. A three-endmember mixing model was then run to separate the proportion of landscape materials (i.e. vegetation and soil) and illumination variability (i.e. shade) found in the study area. Table 1. Field data from the study area describing physical characteristics of classes of burned forests. Unburned Lightly Moderately Heavily (control) burned burned burned forest forest forest forest Ô Ô Ô Sample size (ha) Live stem (> 10 cm dbh) Density (#haõ 1 ) Dead stem density (#haõ 1 ) Average canopy cover (%) 87.1Ô 2.9# # # # Living biomass (metric tons haõ 1 )* Dead biomass (metric tons haõ 1 )* Total biomass (metric tons haõ 1 )* #Standard deviation. *Above ground biomass calculated based on diameter of all trees > (Source: Cochrane and Schulze in press). 10 cm dbh.
3 Remote Sensing L etters 3435 Figure 1. Flow chart of the image processing methodology. The mixing model was solved by applying a least-square estimator (Shimabukuro and Smith 1991), with an unconstrained solution (Schanzer 1993), given by for n DNb= F i DN i,b +e b (1) i= 1 n F i = 1 i= 1 where DNb is the Digital Number in band b; Fi the fraction of endmember i; DNi,b the relative Digital Number of endmember i, in band b; and e b is the error in band b. The shade fraction image was then classi ed to create a binary image of forest (pixel value= 1) and non-forest classes (pixel value= 0) as suggested by Shimabukuro et al. (1997). The binary image was ltered using a median lter to remove `saltand-pepper noise (Gonzales and Woods 1993) and the resulting image mask was multiplied by the original image to remove unforested areas while retaining the pixel values of forested areas. A second mixing model was created using a band 4/band 5 scatterplot of the forest masked image to select forest endmembers (vegetation, shade and nonphotosynthetic vegetation (NPV, (Roberts et al. 1993)) and unmix the forested pixels. For this model, the shade endmember was set to zero in all bands in order to create an envelope for the majority of pixels in the data set. Classi cation of the 1993 image was performed to separate unburned forest, recently burned forest (<1 year-old), and older burned forest (1 to 2 years-oldð as determined by comparison with the 1991 image and ground information), using the NPV fraction image. Pixel values of known areas were extracted from the fraction image for each of the above classes and were plotted in a vegetation-npv-shade mixing space diagram to evaluate the degree of separability of these forest classes.
4 3436 M. A. Cochrane and C. M. Souza Jr 3. Results Analyses of the fraction images derived from the vegetation-npv-shade mixing model showed that neither vegetation nor shade fractions allowed for separation of unburned, recently burned, and older burned forests. These forest classes concentrate between 20% and 40% in the shade fraction and show considerable overlap (40% to 60%) in the vegetation fraction. However, the NPV fraction image ( gure 2 (a)) provided good separability of unburned forest (0± 20%) and recently burned forest (20± 60%). As expected, older burned forest overlaps with both unburned forest and recently burned forest, concentrating between 15 and 25% of NPV ( gure 3). Classi cation of the image ( gure 2 (b)) shows that 371 km 2, of the 1406 km 2 comprising the study area, were cleared by Within the remaining 1035 km 2 of forest, at least 184 km 2 (18%) burned during the time interval between the 1991 and 1993 images. Burned forests were subclassed into recent burns (22 km 2 ) and older burns (162 km 2 ). 4. Classi cation evaluation Variability in re spread and intensity leads to the formation of a heterogeneous landscape consisting of unburned `forest islands and burned forest patches impacted to di ering degrees (Cochrane and Schulze, in press). Burn variability and vegetation regrowth a ected the classi cation of burned forests. Estimated accuracy for the classi cation of unburned, recently burned and older burned forests, was 92%, 93% and 71% respectively (table 2). Burned forest classi cation accuracy may be arti - cially low due to the inclusion of small unburned forest patches in the test elds. 5. Discussion This methodology shows the potential of mixture models for classi cation of re impacted forests in Landsat-TM images of Amazonia. With this methodology it is possible to detect both recent and older burns (>1 year-old) using the forest NPV fraction image. Extreme site heterogeneity made unsupervised classi cations untenable due to spectral mixture of the land-cover classes. Maximum likelihood classi cation confused much of the recently burned forest with non-forest classes and was unable to classify older burned forests. Though the information content present in older burns is limited, the information present in more recent burns ( gure 3 (a)) is rich and may be su cient for subclassi cation into various levels of re damage. Our initial results show that there are three cluster regions, obtained using a k-means algorithm, in the vegetation- NPV-shade mixing space which may correlate with re intensity ( gure 3 (c) table 3). We postulate that cluster 1 can be associated with lightly burned forests (58% Table 2. Error matrix of the forest classi cation. Older Class Unburned burned Recently accuracy Classes forest forest burned forest Non-forest (%) Unburned forest Older burned forest Recently burned forest Overall accuracy=4566/5446=84%
5 Remote Sensing L etters 3437 Figure 2. (a) NPV fraction image obtained with the mixing model of the forested portions of the study area (1406 km 2 ); and (b) classi cation of the forested areas based on the NPV fraction image.
6 3438 M. A. Cochrane and C. M. Souza Jr Figure 3. NPV-vegetation-shade mixing space diagram showing the distribution of forest classes; (a) Recently Burned Forest and Unburned Forest, (b) includes Older Burned Forest, and (c) cluster centroid location of recently burned forest classes.
7 Remote Sensing L etters 3439 Table 3. Clusters de ned for recently burned forests using fraction values of NPV and vegetation; cluster 1 may be associated with lightly burned forest, cluster 2 with moderately burned forest and cluster 3 with heavily burned forest. Normalized Endmember percent endmember percent Cluster number NPV Vegetation NPV Vegetation vegetation), cluster 2 with moderately burned forest (37%) and cluster 3 with heavily burned forest (5%). The vegetated percentage of these clusters roughly correlates with canopy cover in the ground data (table 1). In older burns, post- re vegetative regrowth reduces exposed NPV ( gure 3 (b)) thereby making subclassi cation of these burned forests impossible. Based on data (table 1) from Cochrane and Schulze (in press), we estimate that the 184 km 2 of forest a ected by re in the study area lost a minimum of 9% (22 metric tons haõ 1 ) of its living biomass in the period between the 1991 and 1993 images. This estimate assumes that all forests in the study area are tropical moist forest of roughly similar stature (borne out by extensive eld work) and that only light burns occurred. This is a very conservative estimate since, due to post- re forest regrowth, it underestimates the total area burned. If the structural assumption is not valid (i.e. burned forests had greater logging damage), then regrowth would result in many smaller trees (greater mortality rate) and more available fuels (greater re intensity) resulting in the removal of an even greater percentage of the remaining biomass (Cochrane and Schulze in press). Estimates of damage levels are very crucial since, in areas of severe burning or recurrent re, loss of living biomass can exceed 75%. It is hoped that the new generation of higher spatial resolution satellites will allow for better subclassi cation of recent burns, therefore yielding more accurate estimates of re-induced reductions in living biomass. In principle, the methodology described here makes it possible to create a time series of images that will allow multi-temporal analyses of re impact in Amazonian forests. Furthermore, burned regions may be tracked over time to determine how rapidly vegetative regrowth obscures re damage and whether or not these areas are frequently reburning as reported by Cochrane and Schulze (in press). Our results indicate that a two-year time interval between Landsat-TM image acquisitions would be su cient to detect burned forests. Therefore, this methodology may bene t agencies working with monitoring and law enforcement issues. 6. Conclusion The methodology described in this letter can be used to digitally classify burned forests in Landsat-TM images of Amazonian forests. The NPV fraction present in a forested pixel provides a physically meaningful measurement of canopy openness and has the potential to provide more information on the e ects of re upon forest structure and biomass. The potential for extending this methodology to make such estimates and other multitemporal analyses are currently being pursued.
8 3440 Remote Sensing L etters Acknowledgments This research was funded by a grant from the PPG7-`Programa de Pesquisa Dirigida (MMA/MCT/ FINEP) to the Instituto do Homem e Meio Ambiente da Amazoà nia (IMAZON). References Alencar, A. A., Nepstad, D., Mendonza, E., Brown, I. F., and Lefebvre, P., 1997, Fires in Amazonia in 1994 and 1995: Four case studies along the arc of deforestation. World Bank, unpublished report. Cochrane, M. A., and Schulze, M., (in press), Fire as a recurrent event in tropical forests of the eastern Amazon: E ects on forest structure, biomass, and species composition. Biotropica. Gonzales, R. C., and Woods, R. E., 1992, Digital Image Processing (Reading, MA: Addison Wesley). Lefebvre, P. A., and Stone, T. A., 1994, Monitoring selective logging in eastern Brazilian Amazonia using multi-temporal Landsat Thematic Mapper imagery. In Proceedings of the ISPRS Commission VII Symposium: Resource and Environmental Monitoring; 1994; Rio de Janeiro, Brazil (SaÄ o Jose dos Campos SP, INPE-Instituto Nacional de Pesquisa Espaciais), 30, pp. 288± 291. Roberts, D. A., Adams, B. J., and Smith, M. O., 1993, Green vegetation, nonphotosynthetic vegetation, and soil in AVIRIS data. Remote Sensing of Environment, 14, 255± 269. Schanzer, D. L., 1993, Comments on the least-squared mixing models to generate fraction images derived for remote sensing multispectral data. IEEE T ransactions on Geoscience and Remote Sensing, 3, 747. Setzer, A. W., Pereira, M. P., and Pereira Jr., A. C., 1994, Satellite studies of biomass burning in AmazoniaÐ some practical aspects. Remote Sensing Reviews, 10, 91± 103. Shimabukuro, Y. E., Mello, E. K., Moreira, J. C., and Duarte V., 1997, SegmentacË aä o e classi cacë aä o da imagem de sombra do modelo de mistura para mapear des orestamento na Amazoà nia. SaÄ o Jose dos Campos, INPE-Instituto Nacional de Pesquisa Espaciais. Divisao de Ensino e DocumentacË aä o, (in Portuguese). Shimabukuro, Y. E., and Smith, J. A., 1991, The least-squares mixing models to generate fraction images derived from remote sensing multispectral data. IEEE T ransactions of Geoscience and Remote Sensing 29, 16± 20. Smith, M. O., Johnson, P. E., and Adams, J. B., 1985, Quantitative determination of mineral types and abundances from re ectance spectra using principal components analysis. Journal of Geophysical Research, 90, 797± 804. Veri ssimo, A., and Amaral, P., 1996, ExploracË aä o madeireira na Amazoà nia: situacë aä o atual e perspectivas. Certi cacë aä o Florestal, 3, 9± 16 (in Portuguese).
Multitemporal Analysis of Degraded Forests in the Southern Brazilian Amazon
Earth Interactions Volume 9 (2005) Paper No. 19 Page 1 Copyright 2005, Paper 09-019; 8,851 words, 5 Figures, 0 Animations, 5 Tables. http://earthinteractions.org Multitemporal Analysis of Degraded Forests
More informationMapping forest degradation in the Eastern Amazon from SPOT 4 through spectral mixture models
Remote Sensing of Environment 87 (2003) 494 506 www.elsevier.com/locate/rse Mapping forest degradation in the Eastern Amazon from SPOT 4 through spectral mixture models Carlos Souza Jr. a,b, *, Laurel
More informationCHARACTERIZATION OF THE AREAS IN SUCCESSION PROCESS (REGROWTH) IN THE AMAZON REGION
CHARACTERIZATION OF THE AREAS IN SUCCESSION PROCESS (REGROWTH) IN THE AMAZON REGION Iris de Marcelhas e Souza Iris@ltid.inpe.br Yosio Edemir Shimabukuro Yosio@ltid.inpe.br Valdete Duarte Valdte@ltid.inpe.br
More informationDetecting deforestation with multitemporal L-band SAR imagery: a case study in western Brazilian Amazônia
INT. J. REMOTE SENSING INPE eprint: sid.inpe.br/eprint@80/2006/12.08.13.17 v1 2006-12-09 2006, 1 8, PrEview article Detecting deforestation with multitemporal L-band SAR imagery: a case study in western
More informationAssessment of areas of selective logging and burned forests in Mato Grosso State, Brazil, from satellite imagery
XIV WORLD FORESTRY CONGRESS, Durban, South Africa, 7-11 September 2015 Assessment of areas of selective logging and burned forests in Mato Grosso State, Brazil, from satellite imagery Yosio Edemir Shimabukuro
More informationYosio Edemir Shimabukuro a, b René Beuchle b Rosana Cristina Grecchi b Dario Simonetti b Frédéric Achard b
Assessment of Deforestation and Forest Degradation due to Selective Logging and Fires using Time Series of Fraction Images derived from Landsat ETM+ images Yosio Edemir Shimabukuro a, b René Beuchle b
More informationProcessing Multitemporal TM Imagery to Extract Forest Cover Change Features in Cleveland National Forest, Southern California
Processing Multitemporal TM Imagery to Extract Forest Cover Change Features in Cleveland National Forest, Southern California John Rogan and Janet Franklin San Diego State University Thanks to Lisa Levien:
More informationPRODES - INPE INPE. PRODES Methodology- PRODES Methodology - INPE. Mapping and Monitoring Deforestation and Forest Degradation in the Brazilian Amazon
Mapping and Monitoring Deforestation and Forest Degradation in the Brazilian Amazon GOFC-GOLD GOLD Symposium on Forest and Land Cover Observations March 21st-26 26 th, 2006 Jena, Germany Carlos Souza Jr.
More informationSupporting Online Material for
www.sciencemag.org/cgi/content/full/318/5853/1107/dc1 Supporting Online Material for Hurricane Katrina s Carbon Footprint on U.S. Gulf Coast Forests Jeffrey Q. Chambers,* Jeremy I. Fisher, Hongcheng Zeng,
More informationThe Amazonia Information System
The Amazonia Information System Diógenes Salas Alves 1 Luiz Gylvan Meira Filho 2 Júlio Cesar Lima d Alge 1 Eliana Maria Kalil Mello 3 José Carlos Moreira 1 José Simeão de Medeiros 4 Instituto Nacional
More informationDifferent Methods To Estimate Above. Study From Nepal
Different Methods To Estimate Above Ground dbiomass: A Comparative Study From Nepal A Comparative Study from Nepal Data Comparing 4 different methods to extrapolate LiDAR estimated carbon to landscape
More informationFOREST COVER MAPPING AND GROWING STOCK ESTIMATION OF INDIA S FORESTS
FOREST COVER MAPPING AND GROWING STOCK ESTIMATION OF INDIA S FORESTS GOFC-GOLD Workshop On Reducing Emissions from Deforestations 17-19 April 2007 in Santa Cruz, Bolivia Devendra PANDEY Forest Survey of
More informationFire Along the Transition Between the Amazon Forest and the Cerrado Ecosystems 1
Fire Along the Transition Between the Amazon Forest and the Cerrado Ecosystems 1 Gustavo Hees de Negreiros 23, David Sandberg 4, Ernesto Alvarado 5, Thomas Hinckley 4, Daniel C. Nepstad 6, and Marcos Pereira
More informationIntegration of forest inventories with remotely sensed data for biomass mapping: First results for tropical Africa
Integration of forest inventories with remotely sensed data for biomass mapping: First results for tropical Africa Alessandro Baccini Nadine Laporte Scott J. Goetz Mindy Sun Wayne Walker Jared Stabach
More informationForest Transparency Legal Amazon
Sanae Hayashi; Carlos Souza Jr.; Márcio Sales & Adalberto Veríssimo (Imazon) SUMMARY In, most part (71 %) of the The degraded forests in forest area of were covered by totaled 40 square kilometers in.
More informationForest Transparency Legal Amazon
Sanae Hayashi; Carlos Souza Jr.; Márcio Sales & Adalberto Veríssimo (Imazon) SUMMARY In May 2011, SAD detected 165 square this total, 42% occurred in Mato Grosso followed by kilometers of deforestation
More informationImprovements in Landsat Pathfinder Methods for Monitoring Tropical Deforestation and Their Extension to Extra-tropical Areas
Improvements in Landsat Pathfinder Methods for Monitoring Tropical Deforestation and Their Extension to Extra-tropical Areas PI: John R. G. Townshend Department of Geography (and Institute for Advanced
More informationForest Transparency Legal Amazon
Sanae Hayashi; Carlos Souza Jr.; Márcio Sales & Adalberto Veríssimo (Imazon) SUMARY The deforestation accumulated in the period The forest degradation accumulated in the of August 2010 and April 2011,
More informationA Remote Sensing Based Urban Tree Inventory for the Mississippi State University Campus
A Remote Sensing Based Urban Tree Inventory for the Mississippi State University Campus W. H. Cooke III a and S.G. Lambert b a Geosciences Department, GeoResources Institute, Mississippi State University,
More informationOpportunities and challenges for monitoring tropical deforestation and forest degradation in dynamic landscapes using Sentinel-2!
Sentinel-2 For Science Workshop 20-22 May, 2014 ESA-ESRIN Frascati Opportunities and challenges for monitoring tropical deforestation and forest degradation in dynamic landscapes using Sentinel-2 Dirk
More informationOperational Monitoring of Alteration in Regional Forest Cover Using Multitemporal Remote Sensing Data ( )
Operational Monitoring of Alteration in Regional Forest Cover Using Multitemporal Remote Sensing Data (2000-2003) Janet Franklin Doug Stow John Rogan Lisa Levien Chris Fischer Curtis Woodcock Consultant:
More informationEstimation of above-ground biomass of mangrove forests using high-resolution satellite data
Estimation of above-ground biomass of mangrove forests using high-resolution satellite data Yasumasa Hirata 1, Ryuichi Tabuchi 2, Saimon Lihpai 3, Herson Anson 3*, Kiyoshi Fujimoto 4, Shigeo Kuramoto 5,
More informationExpert Meeting on Crop Monitoring for Improved Food Security, 17 February 2014, Vientiane, Lao PDR. By: Scientific Context
Satellite Based Crop Monitoring & Estimation System for Food Security Application in Bangladesh Expert Meeting on Crop Monitoring for Improved Food Security, 17 February 2014, Vientiane, Lao PDR By: Bangladesh
More informationCANOPY DAMAGE AND RECOVERY AFTER SELECTIVE LOGGING IN AMAZONIA: FIELD AND SATELLITE STUDIES
Ecological Applications, 14(4) Supplement, 2004, pp. S280 S298 2004 by the Ecological Society of America CANOPY DAMAGE AND RECOVERY AFTER SELECTIVE LOGGING IN AMAZONIA: FIELD AND SATELLITE STUDIES GREGORY
More informationMULTI-ANGULAR SATELLITE REMOTE SENSING AND FOREST INVENTORY DATA FOR CARBON STOCK AND SINK CAPACITY IN THE EASTERN UNITED STATES FOREST ECOSYSTEMS
MULTI-ANGULAR SATELLITE REMOTE SENSING AND FOREST INVENTORY DATA FOR CARBON STOCK AND SINK CAPACITY IN THE EASTERN UNITED STATES FOREST ECOSYSTEMS X. Liu, M. Kafatos, R. B. Gomez, H. Wolf Center for Earth
More information25 th ACRS 2004 Chiang Mai, Thailand 551
25 th ACRS 2004 Chiang Mai, Thailand 551 RUBBER AGROFOREST IDENTIFICATION USING OBJECT-BASED CLASSIFICATION IN BUNGO DISTRICT, JAMBI, INDONESIA Andree Ekadinata, Atiek Widayati and Grégoire Vincent World
More informationForestry Department Food and Agriculture Organization of the United Nations FRA 2000 GLOBAL FOREST COVER MAP. Rome, November 1999
Forestry Department Food and Agriculture Organization of the United Nations FRA 2000 GLOBAL FOREST COVER MAP Rome, November 1999 Forest Resources Assessment Programme Working Paper 19 Rome 1999 The Forest
More informationR. M. Linn a, S. B. A. Rolim a,*, L. S. Galvão b
ASSESSMENT OF THE MULTIPLE ENDMEMBER SPECTRAL MIXTURE ANALYSIS (MESMA) MODEL APPLIED TO THE HYPERION/EO-1 HYPERSPECTRAL DATA OF THE COASTAL PLAIN OF RIO GRANDE DO SUL, BRAZIL R. M. Linn a, S. B. A. Rolim
More informationDeforestation report for the Brazilian Amazon (September 2014) SAD
Summary SAD detected 402 square kilometers of deforestation in the Brazilian Amazon in September 2014. That represented an increase of 290% in relation to September 2013 when deforestation totaled 103
More informationA new index for delineating built-up land features in satellite imagery
International Journal of Remote Sensing Vol. 29, No. 14, 20 July 2008, 4269 4276 Letter A new index for delineating built-up land features in satellite imagery H. XU* College of Environment and Resources,
More informationAnais do XVIII Simpósio Brasileiro de Sensoriamento Remoto -SBSR
The potential of landscape metrics for assessing the impacts of selective logging in the Brazilian Amazon Rosana Cristina Grecchi 1 René Beuchle 2 Peter Vogt 2 Yosio Edemir Shimabukuro 3 Alessandra Rodrigues
More information30 Years of Tree Canopy Cover Change in Unincorporated and Incorporated Areas of Orange County,
30 Years of Tree Canopy Cover Change in Unincorporated and Incorporated Areas of Orange County, 1986-2016 Final Report to Orange County July 2017 Authors Dr. Shawn Landry, USF Water Institute, University
More informationForest change detection in boreal regions using
Forest change detection in boreal regions using MODIS data time series Peter Potapov, Matthew C. Hansen Geographic Information Science Center of Excellence, South Dakota State University Data from the
More informationTropical forest degradation monitoring using ETM+ and MODIS remote sensing data in the Peninsular Malaysia
IOP Conference Series: Earth and Environmental Science OPEN ACCESS Tropical forest degradation monitoring using ETM+ and MODIS remote sensing data in the Peninsular Malaysia To cite this article: M Hashim
More informationMangrove deforestation analysis in Northwestern Madagascar Stage 1 - Analysis of historical deforestation
Mangrove deforestation analysis in Northwestern Madagascar Stage 1 - Analysis of historical deforestation Frédérique Montfort, Clovis Grinand, Marie Nourtier March 2018 1. Context and study area : The
More informationIdentification of Crop Areas Using SPOT 5 Data
Identification of Crop Areas Using SPOT 5 Data Cankut ORMECI 1,2, Ugur ALGANCI 2, Elif SERTEL 1,2 1 Istanbul Technical University, Geomatics Engineering Department, Maslak, Istanbul, Turkey, 34469 2 Istanbul
More informationSummary. Deforestation report for the Brazilian Amazon (October 2014) SAD
Summary SAD detected 244 square kilometers of deforestation in the Brazilian Amazon in October 2014. That represented a 467% increase in relation to October 2013 when deforestation totaled 43 square kilometers.
More informationUsing mixture analysis for soil information extraction from an AVIRIS scene at the Walnut Gulch Experimental Watershed - Arizona
Using mixture analysis for soil information extraction from an AVIRIS scene at the Walnut Gulch Experimental Watershed - Arizona Luciano J. de O. Accioly EMBRAPA - Empresa Brasileira de Pesquisa Agropecuária,
More informationJRC future activities: TropForest and ReCaREDD projects
JRC future activities: TropForest and ReCaREDD projects Presented by Frédéric Achard www.jrc.ec.europa.eu First High level objective of the FRC unit Assessment of the state and condition (extent, health
More informationA NEW OPTIMAL INDEX FOR BURNT AREA DISCRIMINATION IN SATELLITE IMAGERY
A NEW OPTIMAL INDEX FOR BURNT AREA DISCRIMINATION IN SATELLITE IMAGERY Renata Libonati (1,), Carlos C. DaCamara (1), Jose M.C. Pereira (3), Alberto Setzer () and Fabiano Morelli () (1) University of Lisbon,
More informationLarge area mapping of land-cover change in Rondônia using multitemporal spectral mixture analysis and decision tree classifiers
JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 107, NO. D20, 8073, doi:10.1029/2001jd000374, 2002 Large area mapping of land-cover change in Rondônia using multitemporal spectral mixture analysis and decision tree
More informationRemote Sensing for Fire Management
Remote Sensing for Fire Management FOR 435/535: Remote Sensing for Fire Management 5. Severity A Source of Confusion Field Measures Remote Sensing Measures FOR 435: A Source of Confusion The terms fire
More informationMaNIAC-UAV - a methodology for automatic pavement defects detection using images obtained by Unmanned Aerial Vehicles
Journal of Physics: Conference Series PAPER OPEN ACCESS MaNIAC-UAV - a methodology for automatic pavement defects detection using images obtained by Unmanned Aerial Vehicles To cite this article: Luiz
More informationRemote sensing as a tool to detect and quantify vegetation properties in tropical forest-savanna transitions Edward Mitchard (University of Edinburgh)
Remote sensing as a tool to detect and quantify vegetation properties in tropical forest-savanna transitions Edward Mitchard (University of Edinburgh) Presentation to Geography EUBAP 10 th Oct 2008 Supervisor:
More informationForest Change Caused by Wind, Water, Ice, Fire, Insects/Disease & Harvesting
Forest Change Caused by Wind, Water, Ice, Fire, Insects/Disease & Harvesting Why use remote sensing for mapping forest changes? Synoptic view of damage on a forest landscape or regional level. Can stop
More informationAfforestation/Reforestation Afforestation/Reforestation Clean Development Mechanism Projects in Uttar Pradesh State August
Afforestation/Reforestation Clean Development Mechanism Projects in Uttar Pradesh State August 9, 2014 Suresh Chauhan TERI, New Delhi Presentation outlines 1. Guidelines for preparing Project Design Document
More informationCOMPARATIVE STUDY OF NDVI AND SAVI VEGETATION INDICES IN ANANTAPUR DISTRICT SEMI-ARID AREAS
International Journal of Civil Engineering and Technology (IJCIET) Volume 8, Issue 4, April 2017, pp. 559 566 Article ID: IJCIET_08_04_063 Available online at http://www.iaeme.com/ijciet/issues.asp?jtype=ijciet&vtype=8&itype=4
More informationGIS and Remote Sensing Analyses of Forest Plantations in Costa Rica s Atlantic Lowland Area
GIS and Remote Sensing Analyses of Forest Plantations in Costa Rica s Atlantic Lowland Area Investigator: James Donahey 1 Advisors: Marcia Snyder 2, Ann Russell 1, Lisa Schulte 1 Affiliations: 1 Iowa State
More informationRemotely-Sensed Carbon and Water Variations in Natural and Converted Ecosystems with Time Series MODIS Data
Remotely-Sensed Carbon and Water Variations in Natural and Converted Ecosystems with Time Series MODIS Data Alfredo Ramon Huete 1 Piyachat Ratana 1 Yosio Edemir Shimabukuro 2 1 University of Arizona Dept.
More informationA North-American Forest Disturbance Record from Landsat Imagery
GSFC Carbon Theme A North-American Forest Disturbance Record from Landsat Imagery Jeffrey Masek, NASA GSFC Forrest G. Hall, GSFC & UMBC Robert Wolfe, GSFC & Raytheon Warren Cohen, USFS Corvallis Eric Vermote,
More informationMapping Habitat for the Ivory Billed Woodpecker and the California Spotted Owl : A Multisensor Fusion Approach
Mapping Habitat for the Ivory Billed Woodpecker and the California Spotted Owl : A Multisensor Fusion Approach A. Swatantran 1, R. Dubayah 1, M. Hofton 1, J. B. Blair 2, A. Keister 3 B. Uihlein 3, P. Hyde
More informationModule 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation)
Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation) Module developers: Carlos Souza, Imazon Sandra Brown, Winrock International Jukka Miettinen, European Commission
More informationEVALUATING THE ACCURACY OF 2005 MULTITEMPORAL TM AND AWiFS IMAGERY FOR CROPLAND CLASSIFICATION OF NEBRASKA INTRODUCTION
EVALUATING THE ACCURACY OF 2005 MULTITEMPORAL TM AND AWiFS IMAGERY FOR CROPLAND CLASSIFICATION OF NEBRASKA Robert Seffrin, Statistician US Department of Agriculture National Agricultural Statistics Service
More informationAPPLICATION OF NATIONAL FOREST INVENTORY FOR REMOTE SENSING CLASSIFICATION OF GROUND LICHEN IN NOTHERN SWEDEN. Sweden -
ISPRS Archive Vol. XXXVIII, Part 4-8-2-W9, "Core Spatial Databases - Updating, Maintenance and Services from Theory to Practice", Haifa, Israel, 2010 APPLICATION OF NATIONAL FOREST INVENTORY FOR REMOTE
More informationANALYSIS OF ALOS/PALSAR POLARIMETRIC SIGNATURES AND SCATTERING MECHANISMS OF FOREST TYPES IN TAPAJÓS S REGION, BRAZIL
ANALYSIS OF ALOS/PALSAR POLARIMETRIC SIGNATURES AND SCATTERING MECHANISMS OF FOREST TYPES IN TAPAJÓS S REGION, BRAZIL J. R. dos Santos 1 ; I. S. Narvaes 1.; P. M. L. A. Graça 2 ; F. G. Gonçalves 3 (1)
More informationUSING REMOTELY SENSED DATA TO MAP FOREST AGE CLASS BY COVER TYPE IN EAST TEXAS
USING REMOTELY SENSED DATA TO MAP FOREST AGE CLASS BY COVER TYPE IN EAST TEXAS Daniel Unger 1, I-Kuai Hung, Jeff Williams, James Kroll, Dean Coble, Jason Grogan 1 Corresponding Author: Daniel Unger (unger@sfasu.edu)
More informationENVI Tutorial: Using SMACC to Extract Endmembers
ENVI Tutorial: Using SMACC to Extract Endmembers Table of Contents OVERVIEW OF THIS TUTORIAL...2 INTRODUCTION TO THE SMACC ENDMEMBER EXTRACTION METHOD...3 EXTRACT ENDMEMBERS WITH SMACC...5 Open and Display
More informationINFLUENCE OF FOREST STAND PARAMETERS ON VEGETATION INDICES USED FOR CONIFEROUS FOREST DAMAGE ASSESSMENT
INFLUENCE OF FOREST STAND PARAMETERS ON VEGETATION INDICES USED FOR CONIFEROUS FOREST DAMAGE ASSESSMENT Jonas Ard6 Department of Physical Geography University of Lund S61vegatan 13 S-223 62 Lund, SWEDEN
More informationLAND COVER CHANGE DUE TO OIL AND GAS EXPLORATION IN THE HAYNESVILLE SHALE REGION FROM 1984 TO 2011
LAND COVER CHANGE DUE TO OIL AND GAS EXPLORATION IN THE HAYNESVILLE SHALE REGION FROM 1984 TO 2011 D A N I E L U N G E R A P R I L 2 3, 2 0 1 3 Division of Environmental Science Arthur Temple College of
More informationSENSIN. r il. el"" ("RN R E. Cl-ti, ,(ii) 28 OUÍ NYÕ. 15 ( (o. Taylor &Frailcis Publishers sit( I,. It~TIONAL JOURNA I 4 )F.
ISSN 0143-1161 el"" ("RN It~TIONAL JOURNA I 4 )F r il 11 I Lm, R E SENSIN Volume 17 Number 4 10 March 1996 15 (41 5.28 (o 28 OUÍ NYÕ,(ii) INPE SJC An official joumal of the Remote Sensing Society BIBLIOTECA
More informationDeforestation in the Kayabi Indigenous Territory: Simulating and Predicting Land Use and Land Cover Change in the Brazilian Amazon
Deforestation in the Kayabi Indigenous Territory: Simulating and Predicting Land Use and Land Cover Change in the Brazilian Amazon Hugo de Alba 1, Joana Barros 2 GEDS, Birkbeck, University of London, Malet
More informationSPECTRAL UNMIXING ANALYSIS OF TIME SERIES LANDSAT 8 IMAGES
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3, 28 ISPRS TC III Mid-term Symposium Developments, Technologies and Applications in Remote
More informationAmazon Scenarios: Modeling modeling interactions among land use, climate, and fire
Amazon Scenarios: Modeling modeling interactions among land use, climate, and fire PI: Dan Nepstad (WHRC) (http://whrc.org) Co-PIs & Collaborators: Robert Kaufmann (BU), Paulo Moutinho, Ane Alencar (IPAM),
More informationTo link to this article: DOI: / URL:
This article was downloaded by:[matricardi, E. A. T.] On: 20 February 2007 Access Details: [subscription number 770411364] Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered
More informationMonitoring Natural Sal Forest Cover in Modhupur, Bangladesh using Temporal Landsat Imagery during
Monitoring Natural Sal Forest Cover in Modhupur, Bangladesh using Temporal Landsat Imagery during 1972 2015 Hasan Muhammad Abdullah *, M. Golam Mahboob, Md.Mezanur Rahman, Tofayel Ahmed * Assistant Professor,
More informationEuropean Forest Fire Information System (EFFIS) - Rapid Damage Assessment: Appraisal of burnt area maps with MODIS data
European Forest Fire Information System (EFFIS) - Rapid Damage Assessment: Appraisal of burnt area maps with MODIS data Paulo Barbosa European Commission, Joint Research Centre, Institute for Environment
More informationAccuracy Assessment of FIA s Nationwide Biomass Mapping Products: Results From the North Central FIA Region
Accuracy Assessment of FIA s Nationwide Biomass Mapping Products: Results From the North Central FIA Region Geoffrey R. Holden, Mark D. Nelson, and Ronald E. McRoberts 1 Abstract. The Remote Sensing Band
More informationHuman Pressure in the Brazilian Amazon 1 Paulo Barreto,* Carlos Souza Jr., Anthony Anderson, Rodney Salomão, Janice Wiles & Ruth Noguerón
Human Pressure in the Brazilian Amazon 1 Paulo Barreto,* Carlos Souza Jr., Anthony Anderson, Rodney Salomão, Janice Wiles & Ruth Noguerón I n 2002, approximately 47% of the Brazilian Amazon was under human
More informationREMOTE SENSING APPLICATION IN FOREST ASSESSMENT
Bulletin of the Transilvania University of Braşov Series II: Forestry Wood Industry Agricultural Food Engineering Vol. 4 (53) No. 2-2011 REMOTE SENSING APPLICATION IN FOREST ASSESSMENT A. PINEDO 1 C. WEHENKEL
More informationAccuracy assessment of the vegetation continuous field tree cover product using 3954 ground plots in the south-western USA
International Journal of Remote Sensing Vol. 26, No. 12, 20 June 2005, 2699 2704 Accuracy assessment of the vegetation continuous field tree cover product using 3954 ground plots in the south-western USA
More informationNON-PERMANENCE RISK REPORT 2016 VALPARAISO PROJECT
NON-PERMANENCE RISK REPORT 216 VALPARAISO PROJECT Document Prepared By CarbonCo 1, LLC, East Aurora, NY USA and TerraCarbon 2, LLC, Peoria, IL USA Project Title The Valparaiso Project Version Version 2.
More informationMonitoring carbon emissions from forest degradation for REDD
Global Observation of Forest and Land Cover Dynamics Monitoring carbon emissions from forest degradation for REDD Martin Herold GOFC-GOLD Land Cover Office, FSU Jena, Germany www.gofc-gold.uni-jena.de
More informationGeometrical Endmember Extraction and Linear Spectral Unmixing of Multispectral Image
I J C T A, 9(3), 2016, pp. 7-15 International Science Press Geometrical Endmember Extraction and Linear Spectral Unmixing of Multispectral Image K. Niranjani*, and K. Vani** Abstract: Accurate mapping
More informationIntegrating Liana Abundance and Forest Stature into an Estimate of Total Aboveground Biomass for an Eastern Amazonian Forest
Portland State University PDXScholar Environmental Science and Management Faculty Publications and Presentations Environmental Science and Management 5-2000 Integrating Liana Abundance and Forest Stature
More informationIntegrating Liana Abundance and Forest Stature into an Estimate of Total Aboveground Biomass for an Eastern Amazonian Forest
Portland State University PDXScholar Environmental Science and Management Faculty Publications and Presentations Environmental Science and Management 5-2000 Integrating Liana Abundance and Forest Stature
More informationIntegration of Alos PalSAR and LIDAR IceSAT data in a multistep approach for wide area biomass mapping
Integration of Alos PalSAR and LIDAR IceSAT data in a multistep approach for wide area biomass mapping. Above Ground Biomass (carbon) mapping and monitoring: Importance Supporting UNFCC KP, REDD+, Monitoring
More informationHyperspectral information for vegetation monitoring
Hyperspectral information for vegetation monitoring Gaia Vaglio Laurin DIBAF Università della Tuscia, CMCC Activities supported by the EU H2020 BACI grant #640176 Italian Space Agency, Rome, 1-3 March
More informationAmazon Scenarios: Interactions among land use, fire, and climate
Amazon Scenarios: Interactions among land use, fire, and climate Daniel Nepstad, Woods Hole Research Center Co-I s: Robert Kaufmann (BU), Paulo Moutinho (IPAM), Carlos Klink (Univ. de Brasilia) Collaborators:
More informationVariable Method Source
Appendix S1 Appendix S1: Table S1 Plot variables Variable Method Source COVERS % shrub cover ocular estimate of shrub cover % forb cover ocular estimate of forbs % coarse woody debris % live overhead canopy
More informationThe Satellite Monitoring and the use of ScanSAR data as a complementary data to fast detection
The Satellite Monitoring and the use of ScanSAR data as a complementary data to fast detection Humberto N. de Mesquita Jr. Head of Environmental Monitoring Center (CEMAM) Brazilian Institute for Environment
More informationABSTRACT. Deforestation Statistics. Brazilian Amazon
April 2014 Brazilian Amazon ABSTRACT In April 2014 the majority (59%) of 2014, corresponding to the first nine months the forest area of the Brazilian Amazon was of the current deforestation calendar,
More informationSPATIAL AND TEMPORAL DETERMINANTS OF FOREST FIRES ON THE AMAZONIAN DEFORESTATION FRONTIER: IMPLICATIONS FOR CURRENT AND FUTURE CARBON EMISSIONS
SPATIAL AND TEMPORAL DETERMINANTS OF FOREST FIRES ON THE AMAZONIAN DEFORESTATION FRONTIER: IMPLICATIONS FOR CURRENT AND FUTURE CARBON EMISSIONS By ANE AUXILIADORA COSTA ALENCAR A DISSERTATION PRESENTED
More informationCLASSIFICATION OF OPEN PIT MINES AND DUMP AREAS BASED ON LAND COVER MAPPING
S E S 2 5 Scientific Conference SPACE, ECOLOGY, SAFETY with International Participation 1 13 June 5, Varna, Bulgaria CLASSIFICATION OF OPEN PIT MINES AND DUMP AREAS BASED ON LAND COVER MAPPING Hristo Nikolov,
More informationIntroduction to Imaging Spectroscopy
Introduction to Imaging Spectroscopy part 2 Remote Sensing (GRS-20306) Outline Part 1 Definition History Why spectroscopy works! Measurement methods Non-imaging Imaging Applications Part 2 Analytical Methods
More informationTen-Year Landsat Classification of Deforestation and Forest Degradation in the Brazilian Amazon
Remote Sens. 2013, 5, 5493-5513; doi:10.3390/rs5115493 Article OPEN ACCESS Remote Sensing ISSN 2072-4292 www.mdpi.com/journal/remotesensing Ten-Year Landsat Classification of Deforestation and Forest Degradation
More informationAn Evaluation of Quickbird Data for Assessing Woodland Resource in Deciduous Sal Forests in Bangladesh. Sheikh Tawhidul Islam
An Evaluation of Quickbird Data for Assessing Woodland Resource in Deciduous Sal Forests in Bangladesh. Sheikh Tawhidul Islam Supervisors: Dr. Danny Donoghue and Dr. Peter Atkins Overview of the presentation
More informationSTRATIFIED ESTIMATES OF FOREST AREA USING THE k-nearest NEIGHBORS TECHNIQUE AND SATELLITE IMAGERY
STRATIFIED ESTIMATES OF FOREST AREA USING THE k-nearest NEIGHBORS TECHNIQUE AND SATELLITE IMAGERY Ronald E. McRoberts, Mark D. Nelson, and Daniel G. Wendt 1 ABSTRACT. For two study areas in Minnesota,
More informationMapping the Boreal zone forest cover and forest cover loss 2000 to 2005
Matthew Hansen a, Peter Potapov a, Steve Stehman b, Kyle Pittman a, Thomas Loveland c, Mark Carroll d, Charlene DiMiceli d Mapping the Boreal zone forest cover and forest cover loss 2000 to 2005 a - South
More informationDigital Classification of Land Use/ Land Cover by Using Remote Sensing Techniques
Digital Classification of Land Use/ Land Cover by Using Remote Sensing Techniques Dr. S.S. Manugula Professor, Department of Civil Engineering, Guru Nanak Institutions, Hyderabad, Telangana, India. M Sagar
More informationCounty- Scale Carbon Estimation in NASA s Carbon Monitoring System
County- Scale Carbon Estimation in NASA s Carbon Monitoring System Ralph Dubayah, University of Maryland 1. Motivation There is an urgent need to develop carbon monitoring capabilities at fine scales and
More informationDetection of Deforestation in China and South East Asia using GF-1 time-series Data
Detection of Deforestation in China and South East Asia using GF-1 time-series Data Project No.10549 Dr. Tan Bingxiang, Institute of Forest Resources Information Technique, CAF, Beijing, China Mike Wooding,
More informationAGRICULTURAL CROP MAPPING USING OPTICAL AND SAR MULTI- TEMPORAL SEASONAL DATA: A CASE STUDY IN LOMBARDY REGION, ITALY
AGRICULTURAL CROP MAPPING USING OPTICAL AND SAR MULTI- TEMPORAL SEASONAL DATA: A CASE STUDY IN LOMBARDY REGION, ITALY G. Fontanelli, A. Crema, R. Azar, D. Stroppiana, P. Villa, M. Boschetti Institute for
More informationBiogeochemical Consequences of Land Use Transitions Along Brazil s Agricultural Frontier
Biogeochemical Consequences of Land Use Transitions Along Brazil s Agricultural Frontier Gillian Galford* 1,2, John Mustard 1, Jerry Melillo 2, Carlos C. Cerri 3, C.E.P. Cerri 3, David Kicklighter 2, Benjamin
More informationForest Disturbances Requirements of Biomass Datasets
CENTRE FOR LANDSCAPE AND CLIMATE RESEARCH Forest Disturbances Requirements of Biomass Datasets Heiko Balzter Pedro Rodriguez-Veiga 1 st ESA CCI Biomass Workshop, Paris, France 25-26 September 2018 Biomass
More informationRemote Sensing of Environment
RSE-07187; No of Pages 10 ARTICLE IN PRESS Remote Sensing of Environment xxx (2008) xxx-xxx Contents lists available at ScienceDirect Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse
More informationTowards Methodologies for Global Monitoring of Forest Cover with Coarse Resolution Data
Towards Methodologies for Global Monitoring of Forest Cover with Coarse Resolution Data R. DeFries M. Hansen J. Townshend R. Sohlberg M. Carroll C. DiMicelli University of Maryland, College Park Objective:
More informationAmazonia. Manoel Cardoso, Gilvan Sampaio and Carlos Nobre
Combined deforestation and fire occurrence in long-term models of forests and savannas in the Brazilian Amazonia Manoel Cardoso, Gilvan Sampaio and Carlos Nobre Instituto Nacional de Pesquisas Espaciais
More informationForest Transparency Brazilian Amazon
Heron Martins, Antônio Fonseca; Carlos Souza Jr.; Márcio Sales & Adalberto Veríssimo (Imazon) SUMMARY In October 2013, SAD detected 43 71% in relation to the previous year (August square kilometers of
More informationMapping Hemlocks to Estimate Potential Canopy Gaps Following Hemlock Woolly Adelgid Infestations in the Southern Appalachian Mountains
Mapping Hemlocks to Estimate Potential Canopy Gaps Following Hemlock Woolly Adelgid Infestations in the Southern Appalachian Mountains Tuula Kantola, Maria Tchakerian, Päivi Lyytikäinen-Saarenmaa, Robert
More informationThe application of texture measures to classifying the rain forest CHRIS OLIVER
The application of texture measures to classifying the rain forest CHRIS OLIVER DERA, St Andrew s Road, Malvern, Worcs., WR14 3PS, UK. chris@sar.dera.gov.uk Abstract. This paper describes the application
More information