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.J.[Li-Juan],
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],
Silva-Costa, J.V.[Joăo Vitor],
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
Krkonoe/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
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.W.[Wen-Wu],
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
Shimizu, K.[Katsuto],
Murakami, W.[Wataru],
Furuichi, T.[Takahisa],
Estoque, R.C.[Ronald C.],
Mapping Land Use/Land Cover Changes and Forest Disturbances in
Vietnam Using a Landsat Temporal Segmentation Algorithm,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link
2302
BibRef
Mulverhill, C.[Christopher],
Coops, N.C.[Nicholas C.],
Achim, A.[Alexis],
Continuous monitoring and sub-annual change detection in
high-latitude forests using Harmonized Landsat Sentinel-2 data,
PandRS(197), 2023, pp. 309-319.
Elsevier DOI
2303
HLS, Forest disturbance, Change detection, Forest monitoring
BibRef
Vernon, J.[Jordan],
Peter, J.S.[Joseph St.],
Crandall, C.[Christy],
Awowale, O.E.[Olufunke E.],
Medley, P.[Paul],
Drake, J.[Jason],
Ibeanusi, V.[Victor],
Spatial Application of Southern U.S. Pine Water Yield for
Prioritizing Forest Management Activities,
IJGI(12), No. 2, 2023, pp. xx-yy.
DOI Link
2303
BibRef
Cavalli, A.[Alice],
Francini, S.[Saverio],
McRoberts, R.E.[Ronald E.],
Falanga, V.[Valentina],
Congedo, L.[Luca],
de Fioravante, P.[Paolo],
Maesano, M.[Mauro],
Munafň, M.[Michele],
Chirici, G.[Gherardo],
Mugnozza, G.S.[Giuseppe Scarascia],
Estimating Afforestation Area Using Landsat Time Series and
Photointerpreted Datasets,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link
2303
BibRef
Míguez, C.[Clara],
Fernández, C.[Cristina],
Evaluating the Combined Use of the NDVI and High-Density Lidar Data
to Assess the Natural Regeneration of P. pinaster after a
High-Severity Fire in NW Spain,
RS(15), No. 6, 2023, pp. 1634.
DOI Link
2304
BibRef
Zhao, G.S.[Guang-Shuai],
Xu, E.[Erqi],
Yi, X.[Xutong],
Guo, Y.[Ye],
Zhang, K.[Kun],
Comparison of Forest Restorations with Different Burning Severities
Using Various Restoration Methods at Tuqiang Forestry Bureau of
Greater Hinggan Mountains,
RS(15), No. 10, 2023, pp. xx-yy.
DOI Link
2306
BibRef
Li, M.[Mei],
Zuo, S.[Shudi],
Su, Y.[Ying],
Zheng, X.M.[Xiao-Man],
Wang, W.B.[Wei-Bing],
Chen, K.C.[Kai-Chao],
Ren, Y.[Yin],
An Approach Integrating Multi-Source Data with LandTrendr Algorithm
for Refining Forest Recovery Detection,
RS(15), No. 10, 2023, pp. xx-yy.
DOI Link
2306
BibRef
Du, B.[Bing],
Yuan, Z.[Zhanliang],
Bo, Y.C.[Yan-Chen],
Zhang, Y.[Yusha],
A Combined Deep Learning and Prior Knowledge Constraint Approach for
Large-Scale Forest Disturbance Detection Using Time Series Remote
Sensing Data,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link
2307
BibRef
Ping, D.[Dazhou],
Dalagnol, R.[Ricardo],
Galvăo, L.S.[Lęnio Soares],
Nelson, B.[Bruce],
Wagner, F.[Fabien],
Schultz, D.M.[David M.],
da C.-Bispo, P.[Polyanna],
Assessing the Magnitude of the Amazonian Forest Blowdowns and
Post-Disturbance Recovery Using Landsat-8 and Time Series of
PlanetScope Satellite Constellation Data,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link
2307
BibRef
Zhao, H.P.[Hai-Ping],
Sun, Y.[Yuman],
Jia, W.W.[Wei-Wei],
Wang, F.[Fan],
Zhao, Z.[Zipeng],
Wu, S.[Simin],
Study on the Regeneration Probability of Understory Coniferous
Saplings in the Liangshui Nature Reserve Based on Four Modeling
Techniques,
RS(15), No. 19, 2023, pp. 4869.
DOI Link
2310
BibRef
Tao, Z.[Zefu],
Yi, L.[Lubei],
Wang, Z.Y.[Zheng-Yu],
Zheng, X.T.[Xue-Ting],
Xiong, S.[Shimei],
Bao, A.[Anming],
Xu, W.Q.[Wen-Qiang],
Remote Sensing Parameter Extraction of Artificial Young Forests under
the Interference of Undergrowth,
RS(15), No. 17, 2023, pp. 4290.
DOI Link
2310
BibRef
Ren, H.X.[Hui-Xin],
Ren, C.Y.[Chun-Ying],
Wang, Z.M.[Zong-Ming],
Jia, M.M.[Ming-Ming],
Yu, W.[Wensen],
Liu, P.[Pan],
Xia, C.Z.[Chen-Zhen],
Continuous Tracking of Forest Disturbance and Recovery in the Greater
Khingan Mountains from Annual Landsat Imagery,
RS(15), No. 22, 2023, pp. 5426.
DOI Link
2311
BibRef
Ding, N.[Ning],
Li, M.[Mingshi],
Mapping Forest Abrupt Disturbance Events in Southeastern China:
Comparisons and Tradeoffs of Landsat Time Series Analysis Algorithms,
RS(15), No. 22, 2023, pp. 5408.
DOI Link
2311
BibRef
Jamali, S.[Sadegh],
Olsson, P.O.[Per-Ola],
Ghorbanian, A.[Arsalan],
Müller, M.[Mitro],
Examining the potential for early detection of spruce bark beetle
attacks using multi-temporal Sentinel-2 and harvester data,
PandRS(205), 2023, pp. 352-366.
Elsevier DOI
2311
Early change detection, European spruce bark beetle,
Forest disturbance, Sentinel-2, Time series analysis
BibRef
Parra, A.[Adriana],
Simard, M.[Marc],
Evaluation of Tree-Growth Rate in the Laurentides Wildlife Reserve
Using GEDI and Airborne-LiDAR Data,
RS(15), No. 22, 2023, pp. 5352.
DOI Link
2311
BibRef
Hai, Y.[Yue],
Liang, M.[Mei],
Yang, Y.Z.[Yu-Ze],
Sun, H.[Hailian],
Li, R.N.[Ruo-Nan],
Yang, Y.Z.[Yan-Zheng],
Zheng, H.[Hua],
Detection of Typical Forest Degradation Patterns: Characteristics and
Drivers of Forest Degradation in Northeast China,
RS(16), No. 8, 2024, pp. 1389.
DOI Link
2405
BibRef
van der Woude, S.[Sietse],
Reiche, J.[Johannes],
Sterck, F.[Frank],
Nabuurs, G.J.[Gert-Jan],
Vos, M.[Marleen],
Herold, M.[Martin],
Sensitivity of Sentinel-1 Backscatter to Management-Related
Disturbances in Temperate Forests,
RS(16), No. 9, 2024, pp. 1553.
DOI Link
2405
BibRef
Tu, Y.W.[Yu-Wei],
Liao, K.[Kaiping],
Chen, Y.X.[Yu-Xuan],
Jiao, H.B.[Hong-Bo],
Chen, G.S.[Guang-Sheng],
Optimized Parameters for Detecting Multiple Forest Disturbance and
Recovery Events and Spatiotemporal Patterns in Fast-Regrowing
Southern China,
RS(16), No. 12, 2024, pp. 2240.
DOI Link
2406
BibRef
Wegler, M.[Marco],
Kuenzer, C.[Claudia],
Potential of Earth Observation to Assess the Impact of Climate Change
and Extreme Weather Events in Temperate Forests: A Review,
RS(16), No. 12, 2024, pp. 2224.
DOI Link
2406
BibRef
Wang, Y.T.[Yue-Ting],
Jia, X.[Xiang],
Zhang, X.L.[Xiao-Li],
Lei, L.T.[Ling-Ting],
Chai, G.Q.[Guo-Qi],
Yao, Z.Q.[Zong-Qi],
Qiu, S.[Shike],
Du, J.[Jun],
Wang, J.X.[Jing-Xu],
Wang, Z.[Zheng],
Wang, R.[Ran],
Tracking Forest Disturbance in Northeast China's Cold-Temperate
Forests Using a Temporal Sequence of Landsat Data,
RS(16), No. 17, 2024, pp. 3238.
DOI Link
2409
BibRef
Barbagallo, B.[Blanka],
Rocca, N.[Nicolň],
Cresi, L.[Lorenzo],
Diolaiuti, G.A.[Guglielmina Adele],
Senese, A.[Antonella],
Enhanced Impacts of Extreme Weather Events on Forest:
The Upper Valtellina (Italy) Case Study,
RS(16), No. 19, 2024, pp. 3692.
DOI Link
2410
BibRef
Yang, J.B.[Jian-Bo],
Liu, D.[Detuan],
Li, Q.[Qian],
Wanasinghe, D.N.[Dhanushka N.],
Zhai, D.L.[De-Li],
Zhao, G.J.[Gao-Juan],
Xu, J.C.[Jian-Chu],
Enhancing Accuracy in Historical Forest Vegetation Mapping in Yunnan
with Phenological Features, and Climatic and Elevation Variables,
RS(16), No. 19, 2024, pp. 3687.
DOI Link
2410
BibRef
Zhou, Q.[Quan],
Kuang, J.J.[Jin-Jia],
Yu, L.F.[Lin-Feng],
Zhang, X.D.[Xu-Dong],
Ren, L.[Lili],
Luo, Y.Q.[You-Qing],
Discriminating between Biotic and Abiotic Stress in Poplar Forests
Using Hyperspectral and LiDAR Data,
RS(16), No. 19, 2024, pp. 3751.
DOI Link
2410
BibRef
Witzmann, S.[Sarah],
Gollob, C.[Christoph],
Kraßnitzer, R.[Ralf],
Ritter, T.[Tim],
Tockner, A.[Andreas],
Moik, L.[Lukas],
Sarkleti, V.[Valentin],
Ofner-Graff, T.[Tobias],
Schume, H.[Helmut],
Nothdurft, A.[Arne],
Quantification of Forest Regeneration on Forest Inventory Sample
Plots Using Point Clouds from Personal Laser Scanning,
RS(17), No. 2, 2025, pp. 269.
DOI Link
2502
BibRef
Liang, Y.J.[Yun-Jian],
Shang, R.[Rong],
Chen, J.M.[Jing M.],
Lin, X.D.[Xu-Dong],
Li, P.[Peng],
Yang, Z.[Ziyi],
Fan, L.Y.[Ling-Yun],
Xu, S.W.[Sheng-Wei],
Lin, Y.Z.[Ying-Zheng],
Chen, Y.[Yao],
Comprehensive Comparison and Validation of Forest Disturbance
Monitoring Algorithms Based on Landsat Time Series in China,
RS(17), No. 4, 2025, pp. 680.
DOI Link
2502
BibRef
Oikonomou, P.[Paraskevi],
Karathanassi, V.[Vassilia],
Andronis, V.[Vassilis],
Papoutsis, I.[Ioannis],
Assessing and Forecasting Natural Regeneration in Mediterranean
Landscapes After Wildfires,
RS(17), No. 5, 2025, pp. 897.
DOI Link
2503
BibRef
Theocharidis, C.[Christos],
Eliades, M.[Marinos],
Kolokoussis, P.[Polychronis],
Miltiadou, M.[Milto],
Danezis, C.[Chris],
Gitas, I.[Ioannis],
Kontoes, C.[Charalampos],
Hadjimitsis, D.[Diofantos],
Exploring Sentinel-1 Radar Polarisation and Landsat Series Data to
Detect Forest Disturbance from Dust Events: A Case Study of the
Paphos Forest in Cyprus,
RS(17), No. 5, 2025, pp. 876.
DOI Link
2503
BibRef
Ignatius, A.R.[Amber R.],
Annis, A.N.[Ashley N.],
Helton, C.A.[Casey A.],
Reeb, A.W.[Alec W.],
Ricke, D.F.[Dylan F.],
Spatiotemporal Vegetation Dynamics, Forest Loss, and Recovery:
Multidecadal Analysis of the U.S. Triple Crown National Scenic Trail
Network,
RS(17), No. 7, 2025, pp. 1142.
DOI Link
2504
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 .