23.4.12.10 Forest Disturbance, Regeneration, Regrowth

Chapter Contents (Back)
Forest Changes. Forest. Change Detection. Temporal Analysis.
See also Deforestation, Degradation.

Peddle, D.R., Franklin, S.E., Johnson, R.L., Lavigne, M.B., Wulder, M.A.,
Structural change detection in a disturbed conifer forest using a geometric optical reflectance model in multiple-forward mode,
GeoRS(41), No. 1, January 2003, pp. 163-166.
IEEE DOI 0304
BibRef

Cai, H.Y.[Hong-Yan], Yang, X.H.[Xiao-Huan], Wang, K.[Kejing], Xiao, L.L.[Lin-Lin],
Is Forest Restoration in the Southwest China Karst Promoted Mainly by Climate Change or Human-Induced Factors?,
RS(6), No. 10, 2014, pp. 9895-9910.
DOI Link 1411
BibRef

Forsythe, K.W.[K. Wayne], McCartney, G.[Grant],
Investigating Forest Disturbance Using Landsat Data in the Nagagamisis Central Plateau, Ontario, Canada,
IJGI(3), No. 1, 2014, pp. 254-273.
DOI Link 1404
BibRef

Chen, D.[Dong], Loboda, T.[Tatiana], Channan, S.[Saurabh], Hoffman-Hall, A.[Amanda],
Long-Term Record of Sampled Disturbances in Northern Eurasian Boreal Forest from Pre-2000 Landsat Data,
RS(6), No. 7, 2014, pp. 6020-6038.
DOI Link 1408
BibRef

Wylie, B.[Bruce], Rigge, M.[Matthew], Brisco, B.[Brian], Murnaghan, K.[Kevin], Rover, J.[Jennifer], Long, J.[Jordan],
Effects of Disturbance and Climate Change on Ecosystem Performance in the Yukon River Basin Boreal Forest,
RS(6), No. 10, 2014, pp. 9145-9169.
DOI Link 1411
BibRef

Mermoz, S.[Stéphane], Toan, T.L.[Thuy Le],
Forest Disturbances and Regrowth Assessment Using ALOS PALSAR Data from 2007 to 2010 in Vietnam, Cambodia and Lao PDR,
RS(8), No. 3, 2016, pp. 217.
DOI Link 1604
BibRef

Frantz, D.[David], Röder, A.[Achim], Udelhoven, T.[Thomas], Schmidt, M.[Michael],
Forest Disturbance Mapping Using Dense Synthetic Landsat/MODIS Time-Series and Permutation-Based Disturbance Index Detection,
RS(8), No. 4, 2016, pp. 277.
DOI Link 1604
BibRef

Brandt, M.[Martin], Tappan, G.[Gray], Diouf, A.A.[Abdoul Aziz], Beye, G.[Gora], Mbow, C.[Cheikh], Fensholt, R.[Rasmus],
Woody Vegetation Die off and Regeneration in Response to Rainfall Variability in the West African Sahel,
RS(9), No. 1, 2017, pp. xx-yy.
DOI Link 1702
BibRef

Xi, Z.Y.[Zhen-Yuan], Lu, D.S.[Deng-Sheng], Liu, L.[Lijuan], Ge, H.L.[Hong-Li],
Detection of Drought-Induced Hickory Disturbances in Western Lin An County, China, Using Multitemporal Landsat Imagery,
RS(8), No. 4, 2016, pp. 345.
DOI Link 1604
BibRef

Murillo-Sandoval, P.J.[Paulo J.], van den Hoek, J.[Jamon], Hilker, T.[Thomas],
Leveraging Multi-Sensor Time Series Datasets to Map Short- and Long-Term Tropical Forest Disturbances in the Colombian Andes,
RS(9), No. 2, 2017, pp. xx-yy.
DOI Link 1703
BibRef

Liu, S.S.[Shan-Shan], Wei, X.L.[Xin-Liang], Li, D.Q.[Deng-Qiu], Lu, D.S.[Deng-Sheng],
Examining Forest Disturbance and Recovery in the Subtropical Forest Region of Zhejiang Province Using Landsat Time-Series Data,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Senf, C.[Cornelius], Pflugmacher, D.[Dirk], Hostert, P.[Patrick], Seidl, R.[Rupert],
Using Landsat time series for characterizing forest disturbance dynamics in the coupled human and natural systems of Central Europe,
PandRS(130), No. 1, 2017, pp. 453-463.
Elsevier DOI 1708
Disturbance, mapping BibRef

Chen, S.[Shijuan], McDermid, G.J.[Gregory J.], Castilla, G.[Guillermo], Linke, J.[Julia],
Measuring Vegetation Height in Linear Disturbances in the Boreal Forest with UAV Photogrammetry,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link 1802
BibRef

Wang, J.[Jian], Wang, J.[Jindi], Zhou, H.M.[Hong-Min], Xiao, Z.Q.[Zhi-Qiang],
Detecting Forest Disturbance in Northeast China from GLASS LAI Time Series Data Using a Dynamic Model,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link 1802
BibRef

Hamunyela, E.[Eliakim], Reiche, J.[Johannes], Verbesselt, J.[Jan], Herold, M.[Martin],
Using Space-Time Features to Improve Detection of Forest Disturbances from Landsat Time Series,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Kukkonen, M.[Markus], Vancutsem, C.[Christelle], Simonetti, D.[Dario], Vieilledent, G.[Ghislain], Verhegghen, A.[Astrid], Gallego, J.[Javier], Stibig, H.J.[Hans-Jürgen],
Towards Operational Monitoring of Forest Canopy Disturbance in Evergreen Rain Forests: A Test Case in Continental Southeast Asia,
RS(10), No. 4, 2018, pp. xx-yy.
DOI Link 1805
BibRef

Huo, L.Z.[Lian-Zhi], Boschetti, L.[Luigi], Sparks, A.M.[Aaron M.],
Object-Based Classification of Forest Disturbance Types in the Conterminous United States,
RS(11), No. 5, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Stych, P.[Premysl], Lastovicka, J.[Josef], Hladky, R.[Radovan], Paluba, D.[Daniel],
Evaluation of the Influence of Disturbances on Forest Vegetation Using the Time Series of Landsat Data: A Comparison Study of the Low Tatras and Sumava National Parks,
IJGI(8), No. 2, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Tompalski, P.[Piotr], Rakofsky, J.[Joseph], Coops, N.C.[Nicholas C.], White, J.C.[Joanne C.], Graham, A.N.V.[Alexander N. V.], Rosychuk, K.[Kyle],
Challenges of Multi-Temporal and Multi-Sensor Forest Growth Analyses in a Highly Disturbed Boreal Mixedwood Forests,
RS(11), No. 18, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Reis, B.P.[Bruna Paolinelli], Martins, S.V.[Sebastiăo Venâncio], Filho, E.I.F.[Elpídio Inácio Fernandes], Sarcinelli, T.S.[Tathiane Santi], Gleriani, J.M.[José Marinaldo], Marcatti, G.E.[Gustavo Eduardo], Leite, H.G.[Helio Garcia], Halassy, M.[Melinda],
Management Recommendation Generation for Areas Under Forest Restoration Process through Images Obtained by UAV and LiDAR,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Puliti, S.[Stefano], Solberg, S.[Svein], Granhus, A.[Aksel],
Use of UAV Photogrammetric Data for Estimation of Biophysical Properties in Forest Stands Under Regeneration,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link 1902
BibRef

Lee, K.[Kyungil], Sung, H.C.[Hyun Chan], Seo, J.Y.[Joung-Young], Yoo, Y.J.[Young-Jae], Kim, Y.[Yoonji], Kook, J.H.[Jung Hyun], Jeon, S.W.[Seong Woo],
The Integration of Remote Sensing and Field Surveys to Detect Ecologically Damaged Areas for Restoration in South Korea,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Landry, S.[Stéphanie], St-Laurent, M.H.[Martin-Hugues], Pelletier, G.[Gaetan], Villard, M.A.[Marc-André],
The Best of Both Worlds? Integrating Sentinel-2 Images and airborne LiDAR to Characterize Forest Regeneration,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008
BibRef

Hu, Y.[Yang], Hu, Y.F.[Yun-Feng],
Detecting Forest Disturbance and Recovery in Primorsky Krai, Russia, Using Annual Landsat Time Series and Multi-Source Land Cover Products,
RS(12), No. 1, 2020, pp. xx-yy.
DOI Link 2001
BibRef

Hirschmugl, M.[Manuela], Deutscher, J.[Janik], Sobe, C.[Carina], Bouvet, A.[Alexandre], Mermoz, S.[Stéphane], Schardt, M.[Mathias],
Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping,
RS(12), No. 4, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Hirschmugl, M.[Manuela], Deutscher, J.[Janik], Gutjahr, K.H., Sobe, C.[Carina], Schardt, M.[Mathias],
Combined use of SAR and optical time series data for near real-time forest disturbance mapping,
MultiTemp17(1-4)
IEEE DOI 1712
remote sensing by radar, synthetic aperture radar, time series, vegetation mapping, Peru, SAR imagery, cloud cover, time series BibRef

Cohen, W.B.[Warren B.], Healey, S.P.[Sean P.], Yang, Z.Q.[Zhi-Qiang], Zhu, Z.[Zhe], Gorelick, N.[Noel],
Diversity of Algorithm and Spectral Band Inputs Improves Landsat Monitoring of Forest Disturbance,
RS(12), No. 10, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Lastovicka, J.[Josef], Svec, P.[Pavel], Paluba, D.[Daniel], Kobliuk, N.[Natalia], Svoboda, J.[Jan], Hladky, R.[Radovan], Stych, P.[Premysl],
Sentinel-2 Data in an Evaluation of the Impact of the Disturbances on Forest Vegetation,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Myroniuk, V.[Viktor], Bilous, A.[Andrii], Khan, Y.[Yevhenii], Terentiev, A.[Andrii], Kravets, P.[Pavlo], Kovalevskyi, S.[Sergii], See, L.[Linda],
Tracking Rates of Forest Disturbance and Associated Carbon Loss in Areas of Illegal Amber Mining in Ukraine Using Landsat Time Series,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link 2007
BibRef

Shimizu, K.[Katsuto], Ota, T.[Tetsuji], Mizoue, N.[Nobuya],
Accuracy Assessments of Local and Global Forest Change Data to Estimate Annual Disturbances in Temperate Forests,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008
BibRef

Quan, Y.H.[Ying-Hui], Zhong, X.[Xian], Feng, W.[Wei], Dauphin, G.[Gabriel], Gao, L.[Lianru], Xing, M.D.[Meng-Dao],
A Novel Feature Extension Method for the Forest Disaster Monitoring Using Multispectral Data,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link 2007
BibRef

Sanchez-Lopez, N.[Nuria], Boschetti, L.[Luigi], Hudak, A.T.[Andrew T.], Hancock, S.[Steven], Duncanson, L.I.[Laura I.],
Estimating Time Since the Last Stand-Replacing Disturbance (TSD) from Spaceborne Simulated GEDI Data: A Feasibility Study,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Löw, M.[Markus], Koukal, T.[Tatjana],
Phenology Modelling and Forest Disturbance Mapping with Sentinel-2 Time Series in Austria,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Bürgi, P.M., Lohman, R.B.,
Impact of Forest Disturbance on InSAR Surface Displacement Time Series,
GeoRS(59), No. 1, January 2021, pp. 128-138.
IEEE DOI 2012
Forestry, Time series analysis, Synthetic aperture radar, Vegetation, Strain, Spaceborne radar, Deforestation, time-series analysis BibRef

Seifert, F.M.[Frank Martin], Häme, T.[Tuomas],
Mapping Forest Disturbance Due to Selective Logging in the Congo Basin with RADARSAT-2 Time Series,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Khatancharoen, C.[Chulabush], Tsuyuki, S.[Satoshi], Bryanin, S.V.[Semyon V.], Sugiura, K.[Konosuke], Seino, T.[Tatsuyuki], Lisovsky, V.V.[Viktor V.], Borisova, I.G.[Irina G.], Wada, N.[Naoya],
Long-Time Interval Satellite Image Analysis on Forest-Cover Changes and Disturbances around Protected Area, Zeya State Nature Reserve, in the Russian Far East,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Komba, A.W.[Atupelye W.], Watanabe, T.[Teiji], Kaneko, M.[Masami], Chand, M.B.[Mohan Bahadur],
Monitoring of Vegetation Disturbance around Protected Areas in Central Tanzania Using Landsat Time-Series Data,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Wegmueller, S.A.[Sarah A.], Townsend, P.A.[Philip A.],
Astrape: A System for Mapping Severe Abiotic Forest Disturbances Using High Spatial Resolution Satellite Imagery and Unsupervised Classification,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Gao, Y.[Yan], Solórzano, J.V.[Jonathan V.], Quevedo, A.[Alexander], Loya-Carrillo, J.O.[Jaime Octavio],
How BFAST Trend and Seasonal Model Components Affect Disturbance Detection in Tropical Dry Forest and Temperate Forest,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Neumann, C.[Carsten], Schindhelm, A.[Anne], Müller, J.[Jörg], Weiss, G.[Gabriele], Liu, A.[Anna], Itzerott, S.[Sibylle],
The Regenerative Potential of Managed Calluna Heathlands: Revealing Optical and Structural Traits for Predicting Recovery Dynamics,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Mohan, M.[Midhun], Richardson, G.[Gabriella], Gopan, G.[Gopika], Aghai, M.M.[Matthew Mehdi], Bajaj, S.[Shaurya], Galgamuwa, G.A.P.[G. A. Pabodha], Vastaranta, M.[Mikko], Arachchige, P.S.P.[Pavithra S. Pitumpe], Amorós, L.[Lot], Corte, A.P.D.[Ana Paula Dalla], de-Miguel, S.[Sergio], Leite, R.V.[Rodrigo Vieira], Kganyago, M.[Mahlatse], Broadbent, E.[Eben_North], Doaemo, W.[Willie], Shorab, M.A.B.[Mohammed Abdullah Bin], Cardil, A.[Adrian],
UAV-Supported Forest Regeneration: Current Trends, Challenges and Implications,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Albuquerque, R.W.[Rafael Walter], Ferreira, M.E.[Manuel Eduardo], Olsen, S.I.[Sřren Ingvor], Tymus, J.R.C.[Julio Ricardo Caetano], Balieiro, C.P.[Cintia Palheta], Mansur, H.[Hendrik], Moura, C.J.R.[Ciro José Ribeiro], Costa, J.V.S.[Joăo Vitor Silva], Castello Branco, M.R.[Maurício Ruiz], Grohmann, C.H.[Carlos Henrique],
Forest Restoration Monitoring Protocol with a Low-Cost Remotely Piloted Aircraft: Lessons Learned from a Case Study in the Brazilian Atlantic Forest,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Moura, M.M.[Marks Melo], de Oliveira, L.E.S.[Luiz Eduardo Soares], Sanquetta, C.R.[Carlos Roberto], Bastos, A.[Alexis], Mohan, M.[Midhun], Corte, A.P.D.[Ana Paula Dalla],
Towards Amazon Forest Restoration: Automatic Detection of Species from UAV Imagery,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Zhang, Y.C.[Yang-Cen], Liu, X.N.[Xiang-Nan], Liu, M.L.[Mei-Ling], Zou, X.Y.[Xin-Yu], Zhang, Q.[Qian], Peng, T.[Tao],
Multi-Scale Spatiotemporal Change Characteristics Analysis of High-Frequency Disturbance Forest Ecosystem Based on Improved Spatiotemporal Cube Model,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Zagajewski, B.[Bogdan], Kluczek, M.[Marcin], Raczko, E.[Edwin], Njegovec, A.[Ajda], Dabija, A.[Anca], Kycko, M.[Marlena],
Comparison of Random Forest, Support Vector Machines, and Neural Networks for Post-Disaster Forest Species Mapping of the Krkonoše/Karkonosze Transboundary Biosphere Reserve,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Aryal, R.R.[Raja Ram], Wespestad, C.[Crystal], Kennedy, R.[Robert], Dilger, J.[John], Dyson, K.[Karen], Bullock, E.[Eric], Khanal, N.[Nishanta], Kono, M.[Marija], Poortinga, A.[Ate], Saah, D.[David], Tenneson, K.[Karis],
Lessons Learned While Implementing a Time-Series Approach to Forest Canopy Disturbance Detection in Nepal,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Osinska-Skotak, K.[Katarzyna], Radecka, A.[Aleksandra], Ostrowski, W.[Wojciech], Michalska-Hejduk, D.[Dorota], Charyton, J.[Jakub], Bakula, K.[Krzysztof], Piórkowski, H.[Hubert],
The Methodology for Identifying Secondary Succession in Non-Forest Natura 2000 Habitats Using Multi-Source Airborne Remote Sensing Data,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Hird, J.N.[Jennifer N.], Kariyeva, J.[Jahan], McDermid, G.J.[Gregory J.],
Satellite Time Series and Google Earth Engine Democratize the Process of Forest-Recovery Monitoring over Large Areas,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Chen, X.[Xi], Zhao, W.Z.[Wen-Zhi], Chen, J.G.[Jia-Ge], Qu, Y.[Yang], Wu, D.H.[Ding-Hui], Chen, X.H.[Xue-Hong],
Mapping Large-Scale Forest Disturbance Types with Multi-Temporal CNN Framework,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Ershov, D.V.[Dmitry V.], Gavrilyuk, E.A.[Egor A.], Koroleva, N.V.[Natalia V.], Belova, E.I.[Elena I.], Tikhonova, E.V.[Elena V.], Shopina, O.V.[Olga V.], Titovets, A.V.[Anastasia V.], Tikhonov, G.N.[Gleb N.],
Natural Afforestation on Abandoned Agricultural Lands during Post-Soviet Period: A Comparative Landsat Data Analysis of Bordering Regions in Russia and Belarus,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Alonso, L.[Laura], Picos, J.[Juan], Armesto, J.[Julia],
Automatic Identification of Forest Disturbance Drivers Based on Their Geometric Pattern in Atlantic Forests,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Peng, L.[Li], Zhou, S.[Shuang], Chen, T.T.[Tian-Tian],
Mapping Forest Restoration Probability and Driving Archetypes Using a Bayesian Belief Network and SOM: Towards Karst Ecological Restoration in Guizhou, China,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Mejia-Zuluaga, P.A.[Paola Andrea], Dozal, L.[León], Valdiviezo-Navarro, J.C.[Juan Carlos],
Genetic Programming Approach for the Detection of Mistletoe Based on UAV Multispectral Imagery in the Conservation Area of Mexico City,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Solórzano, J.V.[Jonathan V.], Gao, Y.[Yan],
Forest Disturbance Detection with Seasonal and Trend Model Components and Machine Learning Algorithms,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Albuquerque, R.W.[Rafael Walter], Vieira, D.L.M.[Daniel Luis Mascia], Ferreira, M.E.[Manuel Eduardo], Soares, L.P.[Lucas Pedrosa], Olsen, S.I.[Sřren Ingvor], Araujo, L.S.[Luciana Spinelli], Vicente, L.E.[Luiz Eduardo], Tymus, J.R.C.[Julio Ricardo Caetano], Balieiro, C.P.[Cintia Palheta], Matsumoto, M.H.[Marcelo Hiromiti], Grohmann, C.H.[Carlos Henrique],
Mapping Key Indicators of Forest Restoration in the Amazon Using a Low-Cost Drone and Artificial Intelligence,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Stoddart, J.[Jaz], de Almeida, D.R.A.[Danilo Roberti Alves], Silva, C.A.[Carlos Alberto], Görgens, E.B.[Eric Bastos], Keller, M.[Michael], Valbuena, R.[Ruben],
A Conceptual Model for Detecting Small-Scale Forest Disturbances Based on Ecosystem Morphological Traits,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Han, A.[Aru], Bao, Y.B.[Yong-Bin], Liu, X.[Xingpeng], Tong, Z.J.[Zhi-Jun], Qing, S.[Song], Bao, Y.[Yuhai], Zhang, J.[Jiquan],
Plant Ontogeny Strongly Influences SO2 Stress Resistance in Landscape Tree Species Leaf Functional Traits,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link 2205
BibRef

de Marzo, T.[Teresa], Gasparri, N.I.[Nestor Ignacio], Lambin, E.F.[Eric F.], Kuemmerle, T.[Tobias],
Agents of Forest Disturbance in the Argentine Dry Chaco,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Sagang, L.B.T.[Le Bienfaiteur Takougoum], Ploton, P.[Pierre], Viennois, G.[Gaëlle], Féret, J.B.[Jean-Baptiste], Sonké, B.[Bonaventure], Couteron, P.[Pierre], Barbier, N.[Nicolas],
Monitoring vegetation dynamics with open earth observation tools: the case of fire-modulated savanna to forest transitions in Central Africa,
PandRS(188), 2022, pp. 142-156.
Elsevier DOI 2205
Forest-savanna transition, Google Earth Engine, Fire, UAV-LiDAR, Aboveground biomass, Species assemblage BibRef

Feng, S.Y.[Si-Yuan], Liu, X.[Xin], Zhao, W.[Wenwu], Yao, Y.[Ying], Zhou, A.[Ao], Liu, X.X.[Xiao-Xing], Pereira, P.[Paulo],
Key Areas of Ecological Restoration in Inner Mongolia Based on Ecosystem Vulnerability and Ecosystem Service,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Sedano, F.[Fernando], Mizu-Siampale, A.[Abel], Duncanson, L.[Laura], Liang, M.Y.[Meng-Yu],
Influence of Charcoal Production on Forest Degradation in Zambia: A Remote Sensing Perspective,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Doblas, J.[Juan], Reis, M.S.[Mariane S.], Belluzzo, A.P.[Amanda P.], Quadros, C.B.[Camila B.], Moraes, D.R.V.[Douglas R. V.], Almeida, C.A.[Claudio A.], Maurano, L.E.P.[Luis E. P.], Carvalho, A.F.A.[André F. A.], Sant'Anna, S.J.S.[Sidnei J. S.], Shimabukuro, Y.E.[Yosio E.],
DETER-R: An Operational Near-Real Time Tropical Forest Disturbance Warning System Based on Sentinel-1 Time Series Analysis,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Shahbandeh, M.[Mahsa], Kaim, D.[Dominik], Kozak, J.[Jacek],
The Substantial Increase of Forest Cover in Central Poland Following Extensive Land Abandonment: Szydlowiec County Case Study,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
BibRef


Cornelio, D.L.,
Reforestation Planning Based on Plant Hardiness Zones In Vitilevu Island, Fiji,
ISPRS20(B4:539-543).
DOI Link 2012
BibRef

Minarík, R., Langhammer, J.,
Use Of A Multispectral UAV Photogrammetry For Detection And Tracking Of Forest Disturbance Dynamics,
ISPRS16(B8: 711-718).
DOI Link 1610
BibRef

Haywood, A., Verbesselt, J., Baker, P.J.,
Mapping Disturbance Dynamics In Wet Sclerophyll Forests Using Time Series Landsat,
ISPRS16(B8: 633-641).
DOI Link 1610
BibRef

Vepakomma, U., Cormier, D., Thiffault, N.,
Potential of UAV Based Convergent Photogrammetry in Monitoring Regeneration Standards,
UAV-g15(281-285).
DOI Link 1512
BibRef

Chapter on Cartography, Aerial Images, Buildings, Roads, Terrain, Forests, Trees, ATR continues in
Forest Change Evaluation, Bark Beetle, Pine Shoot Beetle, Other Insects .


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