Deforestation, Degradation

Chapter Contents (Back)
> Deforestation. Degradation. More general changes:
See also Forest Change Evaluation, Change Detection, Temporal Analysis.
See also Forest Disturbance, Regeneration, Regrowth.

Lee, H.,
Mapping Deforestation and Age of Evergreen Trees by Applying a Binary Coding Method to Time-Series Landsat November Images,
GeoRS(46), No. 11, November 2008, pp. 3926-3936.

Arai, E., Shimabukuro, Y., Pereira, G., Vijaykumar, N.,
A Multi-Resolution Multi-Temporal Technique for Detecting and Mapping Deforestation in the Brazilian Amazon Rainforest,
RS(3), No. 9, September 2011, pp. 1943-1956.
DOI Link 1203

Mello, M.P., Vieira, C.A.O., Rudorff, B.F.T., Aplin, P., Santos, R.D.C., Aguiar, D.A.,
STARS: A New Method for Multitemporal Remote Sensing,
GeoRS(51), No. 4, April 2013, pp. 1897-1913.

Mello, M.P.[Marcio Pupin], Martins, F.S.R.V.[Flora S.R.V.], Sato, L.Y.[Luciane Y.], Cantinho, R.Z.[Roberta Z.], Aguiar, D.A.[Daniel A.], Rudorff, B.F.T.[Bernardo F.T.], Santos, R.D.C.[Rafael D.C.],
Spectral-Temporal Analysis by Response Surface applied to detect deforestation in the Brazilian Amazon,

Souza, Jr., C.M.[Carlos M.], Siqueira, J.V.[João V.], Sales, M.H.[Marcio H.], Fonseca, A.V.[Antônio V.], Ribeiro, J.G.[Júlia G.], Numata, I.[Izaya], Cochrane, M.A.[Mark A.], Barber, C.P.[Christopher P.], Roberts, D.A.[Dar A.], Barlow, J.[Jos],
Ten-Year Landsat Classification of Deforestation and Forest Degradation in the Brazilian Amazon,
RS(5), No. 11, 2013, pp. 5493-5513.
DOI Link 1312

Hirschmugl, M.[Manuela], Steinegger, M.[Martin], Gallaun, H.[Heinz], Schardt, M.[Mathias],
Mapping Forest Degradation due to Selective Logging by Means of Time Series Analysis: Case Studies in Central Africa,
RS(6), No. 1, 2014, pp. 756-775.
DOI Link 1402

Chen, F.L.[Fu-Long], Guo, H.D.[Hua-Dong], Ishwaran, N.[Natarajan], Zhou, W.[Wei], Yang, R.X.[Rui-Xia], Jing, L.H.[Lin-Hai], Chen, F.[Fang], Zeng, H.C.[Hong-Cheng],
Synthetic Aperture Radar (SAR) Interferometry for Assessing Wenchuan Earthquake (2008) Deforestation in the Sichuan Giant Panda Site,
RS(6), No. 7, 2014, pp. 6283-6299.
DOI Link 1408

Reiche, J.[Johannes], de Bruin, S.[Sytze], Hoekman, D.[Dirk], Verbesselt, J.[Jan], Herold, M.[Martin],
A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection,
RS(7), No. 5, 2015, pp. 4973-4996.
DOI Link 1506

He, T.[Tian], Shao, Q.Q.[Quan-Qin], Cao, W.[Wei], Huang, L.[Lin], Liu, L.[Lulu],
Satellite-Observed Energy Budget Change of Deforestation in Northeastern China and its Climate Implications,
RS(7), No. 9, 2015, pp. 11586.
DOI Link 1511

Huang, S.[Senwang], Kong, J.M.[Ji-Ming],
Assessing Land Degradation Dynamics and Distinguishing Human-Induced Changes from Climate Factors in the Three-North Shelter Forest Region of China,
IJGI(5), No. 9, 2016, pp. 158.
DOI Link 1610

Lu, M.[Meng], Pebesma, E.[Edzer], Sanchez, A.[Alber], Verbesselt, J.[Jan],
Spatio-temporal change detection from multidimensional arrays: Detecting deforestation from MODIS time series,
PandRS(117), No. 1, 2016, pp. 227-236.
Elsevier DOI 1605

Qamer, F.M.[Faisal Mueen], Shehzad, K.[Khuram], Abbas, S.[Sawaid], Murthy, M.[MSR], Xi, C.[Chen], Gilani, H.[Hammad], Bajracharya, B.[Birendra],
Mapping Deforestation and Forest Degradation Patterns in Western Himalaya, Pakistan,
RS(8), No. 5, 2016, pp. 385.
DOI Link 1606

Hamunyela, E.[Eliakim], Verbesselt, J.[Jan], de Bruin, S.[Sytze], Herold, M.[Martin],
Monitoring Deforestation at Sub-Annual Scales as Extreme Events in Landsat Data Cubes,
RS(8), No. 8, 2016, pp. 651.
DOI Link 1609

Wang, C.Y.[Chu-Yuan], Myint, S.W.[Soe W.],
Environmental Concerns of Deforestation in Myanmar 2001-2010,
RS(8), No. 9, 2016, pp. 728.
DOI Link 1610

Jin, Y.H.[Yi-Hua], Sung, S.Y.[Sun-Yong], Lee, D.K.[Dong Kun], Biging, G.S.[Gregory S.], Jeong, S.G.[Seung-Gyu],
Mapping Deforestation in North Korea Using Phenology-Based Multi-Index and Random Forest,
RS(8), No. 12, 2016, pp. 997.
DOI Link 1612

Schneibel, A.[Anne], Frantz, D.[David], Röder, A.[Achim], Stellmes, M.[Marion], Fischer, K.[Kim], Hill, J.[Joachim],
Using Annual Landsat Time Series for the Detection of Dry Forest Degradation Processes in South-Central Angola,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711

Lu, M.[Meng], Hamunyela, E.[Eliakim], Verbesselt, J.[Jan], Pebesma, E.[Edzer],
Dimension Reduction of Multi-Spectral Satellite Image Time Series to Improve Deforestation Monitoring,
RS(9), No. 10, 2017, pp. xx-yy.
DOI Link 1711

Bouvet, A.[Alexandre], Mermoz, S.[Stéphane], Ballère, M.[Marie], Koleck, T.[Thierry], Toan, T.L.[Thuy Le],
Use of the SAR Shadowing Effect for Deforestation Detection with Sentinel-1 Time Series,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link 1809

Pedraza, C.[Carlos], Clerici, N.[Nicola], Forero, C.F.[Cristian Fabián], Melo, A.[América], Navarrete, D.[Diego], Lizcano, D.[Diego], Zuluaga, A.F.[Andrés Felipe], Delgado, J.[Juliana], Galindo, G.[Gustavo],
Zero Deforestation Agreement Assessment at Farm Level in Colombia Using ALOS PALSAR,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810

Picoli, M.C.A.[Michelle Cristina Araujo], Camara, G.[Gilberto], Sanches, I.[Ieda], Simões, R.[Rolf], Carvalho, A.[Alexandre], Maciel, A.[Adeline], Coutinho, A.[Alexandre], Esquerdo, J.[Julio], Antunes, J.[João], Begotti, R.A.[Rodrigo Anzolin], Arvor, D.[Damien], Almeida, C.[Claudio],
Big earth observation time series analysis for monitoring Brazilian agriculture,
PandRS(145), 2018, pp. 328-339.
Elsevier DOI 1811
Big earth observation data, Land use science, Satellite image time series, Crop expansion, Tropical deforestation BibRef

Simoes, R.[Rolf], Camara, G.[Gilberto], Queiroz, G.[Gilberto], Souza, F.[Felipe], Andrade, P.R.[Pedro R.], Santos, L.[Lorena], Carvalho, A.[Alexandre], Ferreira, K.[Karine],
Satellite Image Time Series Analysis for Big Earth Observation Data,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107

Helmer, E.H.[Eileen H.], Ruzycki, T.S.[Thomas S.], Wilson, B.T.[Barry T.], Sherrill, K.R.[Kirk R.], Lefsky, M.A.[Michael A.], Marcano-Vega, H.[Humfredo], Brandeis, T.J.[Thomas J.], Erickson, H.E.[Heather E.], Ruefenacht, B.[Bonnie],
Tropical Deforestation and Recolonization by Exotic and Native Trees: Spatial Patterns of Tropical Forest Biomass, Functional Groups, and Species Counts and Links to Stand Age, Geoclimate, and Sustainability Goals,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812

Espejo, J.C.[Jorge Caballero], Messinger, M.[Max], Román-Dañobeytia, F.[Francisco], Ascorra, C.[Cesar], Fernandez, L.E.[Luis E.], Silman, M.[Miles],
Deforestation and Forest Degradation Due to Gold Mining in the Peruvian Amazon: A 34-Year Perspective,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901

Schultz, M.[Michael], Shapiro, A.[Aurélie], Clevers, J.G.P.W.[Jan G.P.W.], Beech, C.[Craig], Herold, M.[Martin],
Forest Cover and Vegetation Degradation Detection in the Kavango Zambezi Transfrontier Conservation Area Using BFAST Monitor,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812

Mizuochi, H.[Hiroki], Hayashi, M.[Masato], Tadono, T.[Takeo],
Development of an Operational Algorithm for Automated Deforestation Mapping via the Bayesian Integration of Long-Term Optical and Microwave Satellite Data,
RS(11), No. 17, 2019, pp. xx-yy.
DOI Link 1909

Reygadas, Y.[Yunuen], Jensen, J.L.R.[Jennifer L. R.], Moisen, G.G.[Gretchen G.],
Forest Degradation Assessment Based on Trend Analysis of MODIS-Leaf Area Index: A Case Study in Mexico,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link 1911

Horch, A.[Abdelkader], Djemal, K.[Khalifa], Gafour, A.[Abdelkader], Taleb, N.[Nasreddine],
Supervised fusion approach of local features extracted from SAR images for detecting deforestation changes,
IET-IPR(13), No. 14, 12 December 2019, pp. 2866-2876.
DOI Link 1912

Grings, F., Roitberg, E., Barraza, V.,
EVI Time-Series Breakpoint Detection Using Convolutional Networks for Online Deforestation Monitoring in Chaco Forest,
GeoRS(58), No. 2, February 2020, pp. 1303-1312.
Forestry, Time series analysis, MODIS, Remote sensing, Earth, Artificial satellites, Monitoring, Deforestation monitoring, time-series analysis BibRef

de Bem, P.P.[Pablo Pozzobon], de Carvalho Junior, O.A.[Osmar Abílio], Guimarães, R.F.[Renato Fontes], Gomes, R.A.T.[Roberto Arnaldo Trancoso],
Change Detection of Deforestation in the Brazilian Amazon Using Landsat Data and Convolutional Neural Networks,
RS(12), No. 6, 2020, pp. xx-yy.
DOI Link 2003

Adarme, M.O.[Mabel Ortega], Feitosa, R.Q.[Raul Queiroz], Happ, P.N.[Patrick Nigri], Aparecido de Almeida, C.[Claudio], Gomes, A.R.[Alessandra Rodrigues],
Evaluation of Deep Learning Techniques for Deforestation Detection in the Brazilian Amazon and Cerrado Biomes From Remote Sensing Imagery,
RS(12), No. 6, 2020, pp. xx-yy.
DOI Link 2003

Valle, D.[Denis], Hyde, J.[Jacy], Marsik, M.[Matthew], Perz, S.[Stephen],
Improved Inference and Prediction for Imbalanced Binary Big Data Using Case-Control Sampling: A Case Study on Deforestation in the Amazon Region,
RS(12), No. 8, 2020, pp. xx-yy.
DOI Link 2004

Perazzoni, F.[Franco], Bacelar-Nicolau, P.[Paula], Painho, M.[Marco],
Geointelligence against Illegal Deforestation and Timber Laundering in the Brazilian Amazon,
IJGI(9), No. 6, 2020, pp. xx-yy.
DOI Link 2006
And: Correction: IJGI(9), No. 10, 2020, pp. xx-yy.
DOI Link 2010

Velastegui-Montoya, A.[Andres], de Lima, A.[Aline], Adami, M.[Marcos],
Multitemporal Analysis of Deforestation in Response to the Construction of the Tucuruí Dam,
IJGI(9), No. 10, 2020, pp. xx-yy.
DOI Link 2010

Lee, S.H.[Seong-Hyeok], Han, K.J.[Kuk-Jin], Lee, K.[Kwon], Lee, K.J.[Kwang-Jae], Oh, K.Y.[Kwan-Young], Lee, M.J.[Moung-Jin],
Classification of Landscape Affected by Deforestation Using High-Resolution Remote Sensing Data and Deep-Learning Techniques,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link 2010

Giannetti, F.[Francesca], Pegna, R.[Raffaello], Francini, S.[Saverio], McRoberts, R.E.[Ronald E.], Travaglini, D.[Davide], Marchetti, M.[Marco], Mugnozza, G.S.[Giuseppe Scarascia], Chirici, G.[Gherardo],
A New Method for Automated Clearcut Disturbance Detection in Mediterranean Coppice Forests Using Landsat Time Series,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011

Doblas, J.[Juan], Shimabukuro, Y.[Yosio], Sant'Anna, S.[Sidnei], Carneiro, A.[Arian], Aragão, L.[Luiz], Almeida, C.[Claudio],
Optimizing Near Real-Time Detection of Deforestation on Tropical Rainforests Using Sentinel-1 Data,
RS(12), No. 23, 2020, pp. xx-yy.
DOI Link 2012

Dupuis, C.[Chloé], Lejeune, P.[Philippe], Michez, A.[Adrien], Fayolle, A.[Adeline],
How Can Remote Sensing Help Monitor Tropical Moist Forest Degradation?: A Systematic Review,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link 2004

Malhi, R.K.M.[Ramandeep Kaur M.], Anand, A.[Akash], Srivastava, P.K.[Prashant K.], Kiran, G.S.[G. Sandhya], Petropoulos, G.P.[George P.], Chalkias, C.[Christos],
An Integrated Spatiotemporal Pattern Analysis Model to Assess and Predict the Degradation of Protected Forest Areas,
IJGI(9), No. 9, 2020, pp. xx-yy.
DOI Link 2009

Wiederkehr, N.C.[Natalia C.], Gama, F.F.[Fabio F.], Castro, P.B.N.[Paulo B. N.], da Conceição Bispo, P.[Polyanna], Balzter, H.[Heiko], Sano, E.E.[Edson E.], Liesenberg, V.[Veraldo], Santos, J.R.[João R.], Mura, J.C.[José C.],
Discriminating Forest Successional Stages, Forest Degradation, and Land Use in Central Amazon Using ALOS/PALSAR-2 Full-Polarimetric Data,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011

Yu, T.[Tao], Liu, P.J.[Peng-Ju], Zhang, Q.[Qiang], Ren, Y.[Yi], Yao, J.N.[Jing-Ning],
Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images,
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link 2104

Pickering, J.[Jeffrey], Tyukavina, A.[Alexandra], Khan, A.[Ahmad], Potapov, P.[Peter], Adusei, B.[Bernard], Hansen, M.C.[Matthew C.], Lima, A.[André],
Using Multi-Resolution Satellite Data to Quantify Land Dynamics: Applications of PlanetScope Imagery for Cropland and Tree-Cover Loss Area Estimation,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106

Kuck, T.N.[Tahisa Neitzel], Sano, E.E.[Edson Eyji], da Conceição Bispo, P.[Polyanna], Shiguemori, E.H.[Elcio Hideiti], Filho, P.F.F.S.[Paulo Fernando Ferreira Silva], Matricardi, E.A.T.[Eraldo Aparecido Trondoli],
A Comparative Assessment of Machine-Learning Techniques for Forest Degradation Caused by Selective Logging in an Amazon Region Using Multitemporal X-Band SAR Images,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109

Cota, G.[Gizelle], Sagan, V.[Vasit], Maimaitijiang, M.[Maitiniyazi], Freeman, K.[Karen],
Forest Conservation with Deep Learning: A Deeper Understanding of Human Geography around the Betampona Nature Reserve, Madagascar,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109

Soto Vega, P.J.[Pedro Juan], da Costa, G.A.O.P.[Gilson Alexandre Ostwald Pedro], Feitosa, R.Q.[Raul Queiroz], Ortega Adarme, M.X.[Mabel Ximena], de Almeida, C.A.[Claudio Aparecido], Heipke, C.[Christian], Rottensteiner, F.[Franz],
An unsupervised domain adaptation approach for change detection and its application to deforestation mapping in tropical biomes,
PandRS(181), 2021, pp. 113-128.
Elsevier DOI 2110
Deforestation detection, Change detection, Domain adaptation, CycleGAN, Deep learning, Remote sensing BibRef

Batar, A.K.[Amit Kumar], Shibata, H.[Hideaki], Watanabe, T.[Teiji],
A Novel Approach for Forest Fragmentation Susceptibility Mapping and Assessment: A Case Study from the Indian Himalayan Region,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link 2110

Fu, H.[Hao], Zhao, W.[Wei], Zhan, Q.[Qiqi], Yang, M.J.[Meng-Jiao], Xiong, D.[Donghong], Yu, D.[Daijun],
Temporal Information Extraction for Afforestation in the Middle Section of the Yarlung Zangbo River Using Time-Series Landsat Images Based on Google Earth Engine,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112

Torres, D.L.[Daliana Lobo], Turnes, J.N.[Javier Noa], Vega, P.J.S.[Pedro Juan Soto], Feitosa, R.Q.[Raul Queiroz], Silva, D.E.[Daniel E.], Junior, J.M.[Jose Marcato], Almeida, C.[Claudio],
Deforestation Detection with Fully Convolutional Networks in the Amazon Forest from Landsat-8 and Sentinel-2 Images,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112

Jiang, H.[Hao], Song, L.S.[Li-Sheng], Li, Y.[Yan], Ma, M.G.[Ming-Guo], Fan, L.[Lei],
Monitoring the Reduced Resilience of Forests in Southwest China Using Long-Term Remote Sensing Data,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201

Matosak, B.M.[Bruno Menini], Fonseca, L.M.G.[Leila Maria Garcia], Taquary, E.C.[Evandro Carrijo], Maretto, R.V.[Raian Vargas], do Nascimento Bendini, H.[Hugo], Adami, M.[Marcos],
Mapping Deforestation in Cerrado Based on Hybrid Deep Learning Architecture and Medium Spatial Resolution Satellite Time Series,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201

Osio, A., Lefèvre, S.,
Object-based Change Detection on Acacia Xanthophloea Species Degradation Along Lake Nakuru Riparian Reserve,
ISPRS21(B3-2021: 347-352).
DOI Link 2201

Tovar, P., Adarme, M.O., Feitosa, R.Q.,
Deforestation Detection in the Amazon Rainforest with Spatial And Channel Attention Mechanisms,
ISPRS21(B3-2021: 851-858).
DOI Link 2201

Yordanov, V., Brovelli, M.A.,
Deforestation Mapping Using Sentinel-1 and Object-based Random Forest Classification on Google Earth Engine,
ISPRS21(B3-2021: 865-872).
DOI Link 2201

Lechler, S., Picoli, M.C.A., Soares, A.R., Sanchez, A., Chaves, M.E.D., Verstegen, J.,
Exploring NASA's Harmonized Landsat and Sentinel-2 (HLS) Dataset To Monitor Deforestation in the Amazon Rainforest,
DOI Link 2012

Andrade, R.B., Costa, G.A.O.P., Mota, G.L.A., Ortega, M.X., Feitosa, R.Q., Soto, P.J., Heipke, C.,
Evaluation of Semantic Segmentation Methods for Deforestation Detection In the Amazon,
DOI Link 2012

Reinisch, E.C., Theiler, J., Ziemann, A.,
Identifying forest thinning using anomalous change detection on synthetic aperture radar data,
geophysical signal processing, radar imaging, remote sensing by radar, synthetic aperture radar, change detection BibRef

Pir Bavaghar, M., Ghazanfari, H., Rahimi, S.,
Comparison of Analytical Hierarchy Process and Fuzzy Method In Deforestation Risk Zoning,
DOI Link 1912

Othman, M.A., Ash'aari, Z.H., Aris, A.Z., Ramli, M.F.,
Forest Degradation Analysis Using Geospatial Techniques Approach In Tropical Region of Pahang, Malaysia,
DOI Link 1912

Hasan, A.F., Laurent, F., Blanc, L., Messner, F.,
The use of Landsat time series for identification of forest degradation levels in the eastern Brazilian Amazon (Paragominas),
vegetation mapping, Brazilian Amazon, CLASlite software, Landsat time series, Photosynthetic Vegetation, Time series BibRef

Johnson, B.A., Scheyvens, H., Samejima, H., Onoda, M.,
Characteristics Of The Remote Sensing Data Used In The Proposed Unfccc Redd+ Forest Reference Emission Levels (frels),
ISPRS16(B8: 669-672).
DOI Link 1610
FREL: forest reference emission levels. reducing emissions from deforestation/forest degradation. BibRef

Gao, Y., Ghilardi, A., Mas, J.F., Paneque-Galvez, J., Skutsch, M.,
Evaluation of Annual MODIS PTC Data for Deforestation and Forest Degradation Analysis,
ISPRS16(B2: 9-13).
DOI Link 1610

Chicas, S.D., Omine, K., Arevalo, B., Ford, J.B., Sugimura, K.,
Deforestation Along The Maya Mountain Massif Belize-guatemala Border,
ISPRS16(B8: 597-602).
DOI Link 1610

Mas, J.F., Pérez Vega, A., Andablo Reyes, A., Castillo Santiago, M.A., Flamenco Sandoval, A.,
Assessing Modifiable Areal Unit Problem in the Analysis of Deforestation Drivers Using Remote Sensing and Census Data,
DOI Link 1602

Naghdizadegan, M., Behifar, M., Mirbagheri, B.,
Spatial Deforestation Modelilng Using Cellular Automata (Case Study: Central Zagros Forests),
HTML Version. 1311

Vieira, C.A.O., Santos, N.T., Carneiro, A.P.S., Balieiro, A.A.S.,
Brazilian Amazonia Deforestation Detection Using Spatio-temporal Scan Statistics,
AnnalsPRS(I-2), No. 2012, pp. 51-55.
HTML Version. 1209

Thiel, C., Weise, C., Riedel, T., Schmullius, C.,
Object based classification of L-Band SAR data for the delineation of forest cover maps and the devection of deforestation,
PDF File. 0607

Chapter on Cartography, Aerial Images, Buildings, Roads, Terrain, Forests, Trees, ATR continues in
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Last update:Jan 13, 2022 at 22:02:22