24.4.13.9 Forest Change Evaluation, Change Detection, Temporal Analysis

Chapter Contents (Back)
Forest Changes. Forest. Change Detection. Temporal Analysis. Specifically:
See also Forest Disturbance, Regeneration, Regrowth.
See also Deforestation, Degradation.
See also Forest Change Evaluation, Bark Beetle, Pine Shoot Beetle, Other Insects.
See also Forest Fire Evaluation, Wildfire Analysis, Brushfire Analysis, Fire Detection.
See also Forest Storm Damage Assessment, Wind Throw.

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Maselli, F., Moriondo, M., Chiesi, M., Chirici, G., Puletti, N., Barbati, A., Corona, P.,
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Vastaranta, M., Kantola, T., Lyytikäinen-Saarenmaa, P., Holopainen, M., Kankare, V., Wulder, M., Hyyppä, J., Hyyppä, H.,
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Battista, P.[Piero], Chiesi, M.[Marta], Rapi, B.[Bernardo], Romani, M.[Maurizio], Cantini, C.[Claudio], Giovannelli, A.[Alessio], Cocozza, C.[Claudia], Tognetti, R.[Roberto], Maselli, F.[Fabio],
Integration of Ground and Multi-Resolution Satellite Data for Predicting the Water Balance of a Mediterranean Two-Layer Agro-Ecosystem,
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Eva, H.[Hugh], Carboni, S.[Silvia], Achard, F.[Frederic], Stach, N.[Nicolas], Durieux, L.[Laurent], Faure, J.F.[Jean-Francois], Mollicone, D.[Danilo],
Monitoring forest areas from continental to territorial levels using a sample of medium spatial resolution satellite imagery,
PandRS(65), No. 2, March 2010, pp. 191-197.
Elsevier DOI 1003
Forestry; Change detection; Sampling; Landsat; SPOT BibRef

Eva, H., Achard, F., Beuchle, R., de Miranda, E., Carboni, S., Seliger, R., Vollmar, M., Holler, W., Oshiro, O., Barrena Arroyo, V., Gallego, J.,
Forest Cover Changes in Tropical South and Central America from 1990 to 2005 and Related Carbon Emissions and Removals,
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Somers, B., Asner, G.,
Hyperspectral Time Series Analysis of Native and Invasive Species in Hawaiian Rainforests,
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Adhikari, S., Southworth, J.,
Simulating Forest Cover Changes of Bannerghatta National Park Based on a CA-Markov Model: A Remote Sensing Approach,
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Canavesi, V., Alvalá, R.,
Changes in Vegetation Cover in Reforested Areas in the State of São Paulo, Brazil and the Implication for Landslide Processes,
IJGI(1), No. 2, 2012, pp. 209-227.
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Frolking, S., Hagen, S., Milliman, T., Palace, M., Shimbo, J.Z., Fahnestock, M.,
Detection of Large-Scale Forest Canopy Change in Pan-Tropical Humid Forests 2000-2009 With the SeaWinds Ku-Band Scatterometer,
GeoRS(50), No. 7, July 2012, pp. 2603-2617.
IEEE DOI 1208
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Bodart, C.[Catherine], Eva, H.[Hugh], Beuchle, R.[René], Raši, R.[Rastislav], Simonetti, D.[Dario], Stibig, H.J.[Hans-Jürgen], Brink, A.[Andreas], Lindquist, E.[Erik], Achard, F.[Frédéric],
Pre-processing of a sample of multi-scene and multi-date Landsat imagery used to monitor forest cover changes over the tropics,
PandRS(66), No. 5, September 2011, pp. 555-563.
Elsevier DOI 1110
Forestry; Calibration; Matching; Landsat; Global BibRef

Frate, L., Carranza, M.,
Quantifying Landscape-Scale Patterns of Temperate Forests over Time by Means of Neutral Simulation Models,
IJGI(2), No. 1, 2013, pp. 94-109.
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Giree, N., Stehman, S., Potapov, P., Hansen, M.,
A Sample-Based Forest Monitoring Strategy Using Landsat, AVHRR and MODIS Data to Estimate Gross Forest Cover Loss in Malaysia between 1990 and 2005,
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DOI Link 1305
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Tian, J.J., Reinartz, P., d'Angelo, P., Ehlers, M.,
Region-Based Automatic Building and Forest Change Detection on Cartosat-1 Stereo Imagery,
PandRS(79), No. 1, May 2013, pp. 226-239.
Elsevier DOI 1305
Stereo imagery; Digital Surface Model (DSM); Change detection; Forest change; Industrial area change BibRef

d'Angelo, P., Kuschk, G., Reinartz, P.,
Evaluation of Skybox Video and Still Image products,
LandImaging14(95-99).
DOI Link 1411
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Tian, J.J.[Jiao-Jiao], Cui, S.Y.[Shi-Yong], Reinartz, P.,
Building Change Detection Based on Satellite Stereo Imagery and Digital Surface Models,
GeoRS(52), No. 1, January 2014, pp. 406-417.
IEEE DOI 1402
feature extraction BibRef

Mu, J., Cui, S., Reinartz, P.,
Building Detection Using Aerial Images and Digital Surface Models,
Hannover17(159-165).
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Tian, J.J., Leitloff, J., Krauß, T., Reinartz, P.,
Region Based Forest Change Detection from CARTOSAT-1 Stereo Imagery,
HighRes11(xx-yy).
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See also Comparison of Two Fusion Based Building Change Detection Methods Using Satellite Stereo Imagery and DSMS. BibRef

Solberg, S.[Svein], Astrup, R.[Rasmus], Weydahl, D.J.[Dan J.],
Detection of Forest Clear-Cuts with Shuttle Radar Topography Mission (SRTM) and Tandem-X InSAR Data,
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Oumar, Z.[Zakariyyaa], Mutanga, O.[Onisimo],
Integrating environmental variables and WorldView-2 image data to improve the prediction and mapping of Thaumastocoris peregrinus (bronze bug) damage in plantation forests,
PandRS(87), No. 1, 2014, pp. 39-46.
Elsevier DOI 1402
Thaumastocoris peregrinus BibRef

Hamunyela, E.[Eliakim], Verbesselt, J.[Jan], Roerink, G.[Gerbert], Herold, M.[Martin],
Trends in Spring Phenology of Western European Deciduous Forests,
RS(5), No. 12, 2013, pp. 6159-6179.
DOI Link 1412
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Abdel-Rahman, E.M.[Elfatih M.], Mutanga, O.[Onisimo], Adam, E.[Elhadi], Ismail, R.[Riyad],
Detecting Sirex noctilio grey-attacked and lightning-struck pine trees using airborne hyperspectral data, random forest and support vector machines classifiers,
PandRS(88), No. 1, 2014, pp. 48-59.
Elsevier DOI 1402
Sirex grey stage BibRef

Wang, Q.[Qian], Zhang, L.F.[Li-Fu], Wu, T.X.[Tai-Xia], Cen, Y.[Yi], Huang, C.P.[Chang-Ping], Tong, Q.X.[Qing-Xi],
Evaluation of Multiple Spring Phenological Indicators of Yearly GPP and NEP at Three Canadian Forest Sites,
RS(6), No. 3, 2014, pp. 1991-2007.
DOI Link 1404
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Tian, J., Nielsen, A.A., Reinartz, P.,
Improving Change Detection in Forest Areas Based on Stereo Panchromatic Imagery Using Kernel MNF,
GeoRS(52), No. 11, November 2014, pp. 7130-7139.
IEEE DOI 1407
Accuracy BibRef

Song, X.P.[Xiao-Peng], Huang, C.Q.[Cheng-Quan], Sexton, J.O.[Joseph O.], Channan, S.[Saurabh], Townshend, J.R.[John R.],
Annual Detection of Forest Cover Loss Using Time Series Satellite Measurements of Percent Tree Cover,
RS(6), No. 9, 2014, pp. 8878-8903.
DOI Link 1410
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Zhu, X.L.[Xiao-Lin], Liu, D.[Desheng],
Accurate Mapping of Forest Types Using Dense Seasonal Landsat Time-Series,
PandRS(96), No. 1, 2014, pp. 1-11.
Elsevier DOI 1410
Forest types
See also Improving Forest Aboveground Biomass Estimation Using Seasonal Landsat NDVI Time-Series. BibRef

Frate, L.[Ludovico], Saura, S.[Santiago], Minotti, M.[Michele], Martino, P.D.[Paolo Di], Giancola, C.[Carmen], Carranza, M.L.[Maria Laura],
Quantifying Forest Spatial Pattern Trends at Multiple Extents: An Approach to Detect Significant Changes at Different Scales,
RS(6), No. 10, 2014, pp. 9298-9315.
DOI Link 1411
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Liu, J.[Jia], Rambal, S.[Serge], Mouillot, F.[Florent],
Soil Drought Anomalies in MODIS GPP of a Mediterranean Broadleaved Evergreen Forest,
RS(7), No. 1, 2015, pp. 1154-1180.
DOI Link 1502
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van Deventer, H., Cho, M.A., Mutanga, O., Ramoelo, A.,
Capability of models to predict leaf N and P across four seasons for six sub-tropical forest evergreen trees,
PandRS(101), No. 1, 2015, pp. 209-220.
Elsevier DOI 1503
Foliar N and P BibRef

Gao, T.[Tian], Zhu, J.J.[Jiao-Jun], Zheng, X.[Xiao], Shang, G.[Guiduo], Huang, L.Y.[Li-Yan], Wu, S.R.[Shang-Rong],
Mapping Spatial Distribution of Larch Plantations from Multi-Seasonal Landsat-8 OLI Imagery and Multi-Scale Textures Using Random Forests,
RS(7), No. 2, 2015, pp. 1702-1720.
DOI Link 1503
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Song, D.X.[Dan-Xia], Huang, C.Q.[Cheng-Quan], Sexton, J.O.[Joseph O.], Channan, S.[Saurabh], Feng, M.[Min], Townshend, J.R.[John R.],
Use of Landsat and Corona data for mapping forest cover change from the mid-1960s to 2000s: Case studies from the Eastern United States and Central Brazil,
PandRS(103), No. 1, 2015, pp. 81-92.
Elsevier DOI 1504
Corona BibRef

Lui, G.V.[Gillian V.], Coomes, D.A.[David A.],
A Comparison of Novel Optical Remote Sensing-Based Technologies for Forest-Cover/Change Monitoring,
RS(7), No. 3, 2015, pp. 2781-2807.
DOI Link 1504
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Lambert, J.[Jonas], Denux, J.P.[Jean-Philippe], Verbesselt, J.[Jan], Balent, G.[Gérard], Cheret, V.[Véronique],
Detecting Clear-Cuts and Decreases in Forest Vitality Using MODIS NDVI Time Series,
RS(7), No. 4, 2015, pp. 3588-3612.
DOI Link 1505
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Thonfeld, F.[Frank], Hecheltjen, A.[Antje], Menz, G.[Gunter],
Bi-temporal Change Detection, Change Trajectories and Time Series Analysis for Forest Monitoring,
PFG(2015), No. 2, 2015, pp. 129-141.
DOI Link 1506
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Shimada, M.[Masanobu], Itoh, T.[Takuya], Motooka, T.[Takeshi], Watanabe, M.[Manabu], Thapa, R.[Rajesh],
High-resolution satellite radar for mapping changes in global forest cover,
SPIE(Newsroom), June 5, 2015.
DOI Link 1507
Radar backscatter using L-band microwave frequencies from the Advanced Land Observing Satellite enables the generation of maps of global forest cover for 2007-2010. BibRef

Wang, H.[Hong], Zhao, Y.[Yu], Pu, R.L.[Rui-Liang], Zhang, Z.Z.[Zhen-Zhen],
Mapping Robinia Pseudoacacia Forest Health Conditions by Using Combined Spectral, Spatial, and Textural Information Extracted from IKONOS Imagery and Random Forest Classifier,
RS(7), No. 7, 2015, pp. 9020.
DOI Link 1506
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Duguay, Y.[Yannick], Bernier, M.[Monique], Lévesque, E.[Esther], Tremblay, B.[Benoit],
Potential of C and X Band SAR for Shrub Growth Monitoring in Sub-Arctic Environments,
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Dutrieux, L.P.[Loïc Paul], Verbesselt, J.[Jan], Kooistra, L.[Lammert], Herold, M.[Martin],
Monitoring forest cover loss using multiple data streams, a case study of a tropical dry forest in Bolivia,
PandRS(107), No. 1, 2015, pp. 112-125.
Elsevier DOI 1508
Landsat BibRef

Walker, J.[Jessica], de Beurs, K.[Kirsten], Wynne, R.H.[Randolph H.],
Phenological Response of an Arizona Dryland Forest to Short-Term Climatic Extremes,
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Dotzler, S.[Sandra], Hill, J.[Joachim], Buddenbaum, H.[Henning], Stoffels, J.[Johannes],
The Potential of EnMAP and Sentinel-2 Data for Detecting Drought Stress Phenomena in Deciduous Forest Communities,
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Cao, S.[Sen], Yu, Q.[Qiuyan], Sanchez-Azofeifa, G.A.[G. Arturo], Feng, J.[Jilu], Rivard, B.[Benoit], Gu, Z.[Zhujun],
Mapping tropical dry forest succession using multiple criteria spectral mixture analysis,
PandRS(109), No. 1, 2015, pp. 17-29.
Elsevier DOI 1512
Secondary tropical dry forest BibRef

Tortini, R.[Riccardo], Mayer, A.L.[Audrey L.], Maianti, P.[Pieralberto],
Using an OBCD Approach and Landsat TM Data to Detect Harvesting on Nonindustrial Private Property in Upper Michigan,
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Wang, X.Y.[Xiao-Yi], Huang, H.B.[Hua-Bing], Gong, P.[Peng], Biging, G.S.[Gregory S.], Xin, Q.C.[Qin-Chuan], Chen, Y.[Yanlei], Yang, J.[Jun], Liu, C.X.[Cai-Xia],
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RS(8), No. 1, 2016, pp. 62.
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Coops, N.C.[Nicholas C.], Waring, R.H.[Richard H.], Plowright, A.[Andrew], Lee, J.[Joanna], Dilts, T.E.[Thomas E.],
Using Remotely-Sensed Land Cover and Distribution Modeling to Estimate Tree Species Migration in the Pacific Northwest Region of North America,
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Mišurec, J.[Jan], Kopacková, V.[Veronika], Lhotáková, Z.[Zuzana], Campbell, P.[Petya], Albrechtová, J.[Jana],
Detection of Spatio-Temporal Changes of Norway Spruce Forest Stands in Ore Mountains Using Landsat Time Series and Airborne Hyperspectral Imagery,
RS(8), No. 2, 2016, pp. 92.
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Jjumba, A.[Anthony], Dragicevic, S.[Suzana],
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Elsevier DOI 1605
Voxel BibRef

Zeng, H.C.[Hong-Cheng], Lu, T.[Tao], Jenkins, H.[Hillary], Negrón-Juárez, R.I.[Robinson I.], Xu, J.[Jiceng],
Assessing Earthquake-Induced Tree Mortality in Temperate Forest Ecosystems: A Case Study from Wenchuan, China,
RS(8), No. 3, 2016, pp. 252.
DOI Link 1604
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Bi, J.[Jian], Myneni, R.[Ranga], Lyapustin, A.[Alexei], Wang, Y.J.[Yu-Jie], Park, T.[Taejin], Chi, C.[Chen], Yan, K.[Kai], Knyazikhin, Y.[Yuri],
Amazon Forests' Response to Droughts: A Perspective from the MAIAC Product,
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Ningthoujam, R.K.[Ramesh K.], Tansey, K.[Kevin], Balzter, H.[Heiko], Morrison, K.[Keith], Johnson, S.C.M.[Sarah C. M.], Gerard, F.[France], George, C.[Charles], Burbidge, G.[Geoff], Doody, S.[Sam], Veck, N.[Nick], Llewellyn, G.M.[Gary M.], Blythe, T.[Thomas],
Mapping Forest Cover and Forest Cover Change with Airborne S-Band Radar,
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Hernández-Clemente, R., Kolari, P., Porcar-Castell, A., Korhonen, L., Mõttus, M.,
Tracking the Seasonal Dynamics of Boreal Forest Photosynthesis Using EO-1 Hyperion Reflectance: Sensitivity to Structural and Illumination Effects,
GeoRS(54), No. 9, September 2016, pp. 5105-5116.
IEEE DOI 1609
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Lindquist, E.J.[Erik J.], d'Annunzio, R.[Rémi],
Assessing Global Forest Land-Use Change by Object-Based Image Analysis,
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Grogan, K.[Kenneth], Pflugmacher, D.[Dirk], Hostert, P.[Patrick], Verbesselt, J.[Jan], Fensholt, R.[Rasmus],
Mapping Clearances in Tropical Dry Forests Using Breakpoints, Trend, and Seasonal Components from MODIS Time Series: Does Forest Type Matter?,
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Lewinska, K.E.[Katarzyna Ewa], Ivits, E.[Eva], Schardt, M.[Mathias], Zebisch, M.[Marc],
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Luo, H.[Hui], Zhou, T.[Tao], Wu, H.[Hao], Zhao, X.[Xiang], Wang, Q.F.[Qian-Feng], Gao, S.[Shan], Li, Z.[Zheng],
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Wingate, V.R.[Vladimir R.], Phinn, S.R.[Stuart R.], Kuhn, N.[Nikolaus], Bloemertz, L.[Lena], Dhanjal-Adams, K.L.[Kiran L.],
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Fujiki, S.[Shogoro], Okada, K.I.[Kei-Ichi], Nishio, S.[Shogo], Kitayama, K.[Kanehiro],
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Tarantino, C.[Cristina], Lovergine, F.[Francesco], Niphadkar, M.[Madhura], Lucas, R.[Richard], Nativi, S.[Stefano], Blonda, P.[Palma],
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Qin, Y.W.[Yuan-Wei], Xiao, X.M.[Xiang-Ming], Wang, J.[Jie], Dong, J.[Jinwei], Ewing, K.[Kayti], Hoagland, B.[Bruce], Hough, D.J.[Daniel J.], Fagin, T.D.[Todd D.], Zou, Z.H.[Zhen-Hua], Geissler, G.L.[George L.], Xian, G.Z.[George Z.], Loveland, T.R.[Thomas R.],
Mapping Annual Forest Cover in Sub-Humid and Semi-Arid Regions through Analysis of Landsat and PALSAR Imagery,
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Lausch, A.[Angela], Erasmi, S.[Stefan], King, D.J.[Douglas J.], Magdon, P.[Paul], Heurich, M.[Marco],
Understanding Forest Health with Remote Sensing -Part I: A Review of Spectral Traits, Processes and Remote-Sensing Characteristics,
RS(8), No. 12, 2016, pp. 1029.
DOI Link 1612
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And:
Understanding Forest Health with Remote Sensing-Part II: A Review of Approaches and Data Models,
RS(9), No. 2, 2017, pp. xx-yy.
DOI Link 1703
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Liu, Z.H.[Zhi-Hua], Wimberly, M.C.[Michael C.], Dwomoh, F.K.[Francis K.],
Vegetation Dynamics in the Upper Guinean Forest Region of West Africa from 2001 to 2015,
RS(9), No. 1, 2017, pp. xx-yy.
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Dwomoh, F.K.[Francis K.], Wimberly, M.C.[Michael C.],
Fire Regimes and Their Drivers in the Upper Guinean Region of West Africa,
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Santos, F.[Fabián], Dubovyk, O.[Olena], Menz, G.[Gunter],
Monitoring Forest Dynamics in the Andean Amazon: The Applicability of Breakpoint Detection Methods Using Landsat Time-Series and Genetic Algorithms,
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Ulsig, L.[Laura], Nichol, C.J.[Caroline J.], Huemmrich, K.F.[Karl F.], Landis, D.R.[David R.], Middleton, E.M.[Elizabeth M.], Lyapustin, A.I.[Alexei I.], Mammarella, I.[Ivan], Levula, J.[Janne], Porcar-Castell, A.[Albert],
Detecting Inter-Annual Variations in the Phenology of Evergreen Conifers Using Long-Term MODIS Vegetation Index Time Series,
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Tian, J.J.[Jiao-Jiao], Schneider, T.[Thomas], Straub, C.[Christoph], Kugler, F.[Florian], Reinartz, P.[Peter],
Exploring Digital Surface Models from Nine Different Sensors for Forest Monitoring and Change Detection,
RS(9), No. 3, 2017, pp. xx-yy.
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Hird, J.N.[Jennifer N.], Montaghi, A.[Alessandro], McDermid, G.J.[Gregory J.], Kariyeva, J.[Jahan], Moorman, B.J.[Brian J.], Nielsen, S.E.[Scott E.], McIntosh, A.C.S.[Anne C. S.],
Use of Unmanned Aerial Vehicles for Monitoring Recovery of Forest Vegetation on Petroleum Well Sites,
RS(9), No. 5, 2017, pp. xx-yy.
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Springer, K.R.[Kyle R.], Wang, R.[Ran], Gamon, J.A.[John A.],
Parallel Seasonal Patterns of Photosynthesis, Fluorescence, and Reflectance Indices in Boreal Trees,
RS(9), No. 7, 2017, pp. xx-yy.
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Sothe, C.[Camile], de Almeida, C.M.[Cláudia Maria], Liesenberg, V.[Veraldo], Schimalski, M.B.[Marcos Benedito],
Evaluating Sentinel-2 and Landsat-8 Data to Map Sucessional Forest Stages in a Subtropical Forest in Southern Brazil,
RS(9), No. 8, 2017, pp. xx-yy.
DOI Link 1708
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Dash, J.P.[Jonathan P.], Watt, M.S.[Michael S.], Pearse, G.D.[Grant D.], Heaphy, M.[Marie], Dungey, H.S.[Heidi S.],
Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak,
PandRS(131), No. 1, 2017, pp. 1-14.
Elsevier DOI 1709
UAV BibRef

Khan, S.H., He, X., Porikli, F.M.[Fatih Murat], Bennamoun, M.,
Forest Change Detection in Incomplete Satellite Images With Deep Neural Networks,
GeoRS(55), No. 9, September 2017, pp. 5407-5423.
IEEE DOI 1709
geomorphology, geophysical image processing, image classification, land cover, vegetation mapping, deep neural networks, disaster management, environmental planning, forest change detection, incomplete satellite images, land cover change monitoring, surface reflectance information, Clouds, Spatial resolution, Change detection, deep learning, multitemporal spectral data, BibRef

Smigaj, M.[Magdalena], Gaulton, R.[Rachel], Suárez, J.C.[Juan C.], Barr, S.L.[Stuart L.],
Use of Miniature Thermal Cameras for Detection of Physiological Stress in Conifers,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711
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Earlier: A1, A2, A4, A3:
UAV-Borne Thermal Imaging for Forest Health Monitoring: Detection of Disease-Induced Canopy Temperature Increase,
GeoUAV15(349-354).
DOI Link 1602
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Zhou, W.Q.[Wei-Qi], Zhang, S.[Sai], Yu, W.J.[Wen-Juan], Wang, J.[Jing], Wang, W.M.[Wei-Min],
Effects of Urban Expansion on Forest Loss and Fragmentation in Six Megaregions, China,
RS(9), No. 10, 2017, pp. xx-yy.
DOI Link 1711
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Mulatu, K.A.[Kalkidan Ayele], Mora, B.[Brice], Kooistra, L.[Lammert], Herold, M.[Martin],
Biodiversity Monitoring in Changing Tropical Forests: A Review of Approaches and New Opportunities,
RS(9), No. 10, 2017, pp. xx-yy.
DOI Link 1711
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Qiu, B.W.[Bing-Wen], Chen, G.[Gong], Tang, Z.H.[Zheng-Hong], Lu, D.F.[Di-Fei], Wang, Z.Z.[Zhuang-Zhuang], Chen, C.C.[Chong-Chen],
Assessing the Three-North Shelter Forest Program in China by a novel framework for characterizing vegetation changes,
PandRS(133), No. Supplement C, 2017, pp. 75-88.
Elsevier DOI 1711
Temporal similarity trajectory, Jeffries-Matusita distance, Three-North Shelter Forest Program (TNSFP), Vegetation trend, BibRef

Byer, S.[Sarah], Jin, Y.F.[Yu-Fang],
Detecting Drought-Induced Tree Mortality in Sierra Nevada Forests with Time Series of Satellite Data,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711
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Pádua, L.[Luís], Hruška, J.[Jonáš], Bessa, J.[José], Adão, T.[Telmo], Martins, L.M.[Luís M.], Gonçalves, J.A.[José A.], Peres, E.[Emanuel], Sousa, A.M.R.[António M. R.], Castro, J.P.[João P.], Sousa, J.J.[Joaquim J.],
Multi-Temporal Analysis of Forestry and Coastal Environments Using UASs,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802
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Rüetschi, M.[Marius], Schaepman, M.E.[Michael E.], Small, D.[David],
Using Multitemporal Sentinel-1 C-band Backscatter to Monitor Phenology and Classify Deciduous and Coniferous Forests in Northern Switzerland,
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House, M.N.[Matthew N.], Wynne, R.H.[Randolph H.],
Identifying Forest Impacted by Development in the Commonwealth of Virginia through the Use of Landsat and Known Change Indicators,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802
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Cazcarra-Bes, V.[Victor], Tello-Alonso, M.[Maria], Fischer, R.[Rico], Heym, M.[Michael], Papathanassiou, K.[Konstantinos],
Monitoring of Forest Structure Dynamics by Means of L-Band SAR Tomography,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link 1802
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Zhang, L.J.[Li-Juan], Pan, T.[Tao], Zhang, H.W.[Hong-Wen], Li, X.X.[Xia-Xiang], Jiang, L.Q.[Lan-Qi],
The Effects of Forest Area Changes on Extreme Temperature Indexes between the 1900s and 2010s in Heilongjiang Province, China,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link 1802
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Wilson, B.T.[Barry T.], Knight, J.F.[Joseph F.], McRoberts, R.E.[Ronald E.],
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Elsevier DOI 1802
Landsat time series, Image compositing, Harmonic regression, National forest inventory, Regression models, Classification models BibRef

Zarco-Tejada, P.J., Hornero, A., Hernández-Clemente, R., Beck, P.S.A.,
Understanding the temporal dimension of the red-edge spectral region for forest decline detection using high-resolution hyperspectral and Sentinel-2a imagery,
PandRS(137), 2018, pp. 134-148.
Elsevier DOI 1802
Hyperspectral, Red edge, Forest decline, Chlorophyll, Sentinel-2a, Radiative transfer BibRef

Solberg, S.[Svein], May, J.[Johannes], Bogren, W.[Wiley], Breidenbach, J.[Johannes], Torp, T.[Torfinn], Gizachew, B.[Belachew],
Interferometric SAR DEMs for Forest Change in Uganda 2000-2012,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804
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Tompalski, P.[Piotr], Coops, N.C.[Nicholas C.], Marshall, P.L.[Peter L.], White, J.C.[Joanne C.], Wulder, M.A.[Michael A.], Bailey, T.[Todd],
Combining Multi-Date Airborne Laser Scanning and Digital Aerial Photogrammetric Data for Forest Growth and Yield Modelling,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804

See also Comment on Tompalski et al. Combining Multi-Date Airborne Laser Scanning and Digital Aerial Photogrammetric Data for Forest Growth and Yield Modelling. Remote Sens. 2018, 10, 347. BibRef

Tompalski, P.[Piotr], Coops, N.C.[Nicholas C.], Marshall, P.L.[Peter L.], White, J.C.[Joanne C.], Wulder, M.A.[Michael A.], Bailey, T.[Todd],
Reply to Vauhkonen: Comment on Tompalski et al. Combining Multi-Date Airborne Laser Scanning and Digital Aerial Photogrammetric Data for Forest Growth and Yield Modelling. Remote Sens. 2018, 10, 347,
RS(10), No. 9, 2018, pp. xx-yy.
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See also Comment on Tompalski et al. Combining Multi-Date Airborne Laser Scanning and Digital Aerial Photogrammetric Data for Forest Growth and Yield Modelling. Remote Sens. 2018, 10, 347.
See also Combining Multi-Date Airborne Laser Scanning and Digital Aerial Photogrammetric Data for Forest Growth and Yield Modelling. BibRef

Vauhkonen, J.[Jari],
Comment on Tompalski et al. Combining Multi-Date Airborne Laser Scanning and Digital Aerial Photogrammetric Data for Forest Growth and Yield Modelling. Remote Sens. 2018, 10, 347,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810

See also Combining Multi-Date Airborne Laser Scanning and Digital Aerial Photogrammetric Data for Forest Growth and Yield Modelling.
See also Reply to Vauhkonen: Comment on Tompalski et al. Combining Multi-Date Airborne Laser Scanning and Digital Aerial Photogrammetric Data for Forest Growth and Yield Modelling. Remote Sens. 2018, 10, 347. BibRef

Zhu, C.H.[Cheng-Hao], Zhang, X.L.[Xiao-Li], Zhang, N.[Ning], Hassan, M.A.[Mohammed Abdelmanan], Zhao, L.[Lin],
Assessing the Defoliation of Pine Forests in a Long Time-Series and Spatiotemporal Prediction of the Defoliation Using Landsat Data,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804
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Asner, G.P.[Gregory P.], Martin, R.E.[Roberta E.], Keith, L.M.[Lisa M.], Heller, W.P.[Wade P.], Hughes, M.A.[Marc A.], Vaughn, N.R.[Nicholas R.], Hughes, R.F.[R. Flint], Balzotti, C.[Christopher],
A Spectral Mapping Signature for the Rapid Ohia Death (ROD) Pathogen in Hawaiian Forests,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804
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Melendy, L., Hagen, S.C., Sullivan, F.B., Pearson, T.R.H., Walker, S.M., Ellis, P., Kustiyo, Sambodo, A.K.[Ari Katmoko], Roswintiarti, O., Hanson, M.A., Klassen, A.W., Palace, M.W., Braswell, B.H., Delgado, G.M.,
Automated method for measuring the extent of selective logging damage with airborne LiDAR data,
PandRS(139), 2018, pp. 228-240.
Elsevier DOI 1804
Lidar, Selective logging, Tropical forest monitoring, REDD+, Automated logging algorithm, Kalimantan, Indonesia, Reduced impact logging (RIL) BibRef

Langner, A.[Andreas], Miettinen, J.[Jukka], Vaughn, N.R.[Nicholas R.], Asner, G.P.[Gregory P.], Brodrick, P.G.[Philip G.], Martin, R.E.[Roberta E.], Heckler, J.W.[Joseph W.], Knapp, D.E.[David E.], Hughes, R.F.[R. Flint],
An Approach for High-Resolution Mapping of Hawaiian Metrosideros Forest Mortality Using Laser-Guided Imaging Spectroscopy,
RS(10), No. 4, 2018, pp. xx-yy.
DOI Link 1805
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Hruza, P.[Petr], Mikita, T.[Tomáš], Tyagur, N.[Nataliya], Krejza, Z.[Zdenek], Cibulka, M.[Miloš], Procházková, A.[Andrea], Patocka, Z.[Zdenek],
Detecting Forest Road Wearing Course Damage Using Different Methods of Remote Sensing,
RS(10), No. 4, 2018, pp. xx-yy.
DOI Link 1805
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Silveira, E.M.O.[Eduarda M. O.], Bueno, I.T.[Inácio T.], Acerbi-Junior, F.W.[Fausto W.], Mello, J.M.[José M.], Scolforo, J.R.S.[José Roberto S.], Wulder, M.A.[Michael A.],
Using Spatial Features to Reduce the Impact of Seasonality for Detecting Tropical Forest Changes from Landsat Time Series,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806
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Goodbody, T.R.H.[Tristan R.H.], Coops, N.C.[Nicholas C.], Hermosilla, T.[Txomin], Tompalski, P.[Piotr], McCartney, G.[Grant], MacLean, D.A.[David A.],
Digital aerial photogrammetry for assessing cumulative spruce budworm defoliation and enhancing forest inventories at a landscape-level,
PandRS(142), 2018, pp. 1-11.
Elsevier DOI 1807
Digital aerial photogrammetry, Forest monitoring, Spruce budworm, Cumulative defoliation, Partial least squares BibRef

Lin, C.S.[Chin-Su], Chen, S.Y.[Shih-Yu], Chen, C.C.[Chia-Chun], Tai, C.H.[Chia-Huei],
Detecting newly grown tree leaves from unmanned-aerial-vehicle images using hyperspectral target detection techniques,
PandRS(142), 2018, pp. 174-189.
Elsevier DOI 1807
Phenological events, New foliage detection, Anomaly detection, Tree growth, Climate change BibRef

Chen, S.Y.[Shih-Yu], Lin, C.S.[Chin-Su], Chuang, S.J.[Shang-Ju], Kao, Z.Y.[Zhe-Yuan],
Weighted Background Suppression Target Detection Using Sparse Image Enhancement Technique for Newly Grown Tree Leaves,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link 1905
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Lausch, A.[Angela], Borg, E.[Erik], Bumberger, J.[Jan], Dietrich, P.[Peter], Heurich, M.[Marco], Huth, A.[Andreas], Jung, A.[András], Klenke, R.[Reinhard], Knapp, S.[Sonja], Mollenhauer, H.[Hannes], Paasche, H.[Hendrik], Paulheim, H.[Heiko], Pause, M.[Marion], Schweitzer, C.[Christian], Schmulius, C.[Christiane], Settele, J.[Josef], Skidmore, A.K.[Andrew K.], Wegmann, M.[Martin], Zacharias, S.[Steffen], Kirsten, T.[Toralf], Schaepman, M.E.[Michael E.],
Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches,
RS(10), No. 7, 2018, pp. xx-yy.
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Kobayashi, H.[Hideki], Nagai, S.[Shin], Kim, Y.[Yongwon], Yang, W.[Wei], Ikeda, K.[Kyoko], Ikawa, H.[Hiroki], Nagano, H.[Hirohiko], Suzuki, R.[Rikie],
In Situ Observations Reveal How Spectral Reflectance Responds to Growing Season Phenology of an Open Evergreen Forest in Alaska,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link 1808
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Morrison, J.[Jacqueline], Higginbottom, T.P.[Thomas P.], Symeonakis, E.[Elias], Jones, M.J.[Martin J.], Omengo, F.[Fred], Walker, S.L.[Susan L.], Cain, B.[Bradley],
Detecting Vegetation Change in Response to Confining Elephants in Forests Using MODIS Time-Series and BFAST,
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Mõttus, M.[Matti], Hernández-Clemente, R.[Rocío], Perheentupa, V.[Viljami], Markiet, V.[Vincent], Aalto, J.H.[Ju-Ho], Bäck, J.[Jaana], Nichol, C.J.[Caroline J.],
Measurement of Diurnal Variation in Needle PRI and Shoot Photosynthesis in a Boreal Forest,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link 1808
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And: Correction: RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
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Li, L.[Le], Zhao, Y.L.[Yao-Long], Fu, Y.C.[Ying-Chun], Xin, Q.C.[Qin-Chuan],
Satellite-Based Models Need Improvements on Simulating Annual Gross Primary Productivity: A Comparison of Six Models for Regional Modeling of Deciduous Broadleaf Forests,
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Kranz, O.[Olaf], Schoepfer, E.[Elisabeth], Tegtmeyer, R.[Reiner], Lang, S.[Stefan],
Earth observation based multi-scale assessment of logging activities in the Democratic Republic of the Congo,
PandRS(144), 2018, pp. 254-267.
Elsevier DOI 1809
Multi-scale assessment, Logging, Conflict resources, Pixel-based change detection, Object-based change detection BibRef

Yuan, H.H.[Huan-Huan], Wu, C.Y.[Chao-Yang], Lu, L.L.[Lin-Lin], Wang, X.Y.[Xiao-Yue],
A new algorithm predicting the end of growth at five evergreen conifer forests based on nighttime temperature and the enhanced vegetation index,
PandRS(144), 2018, pp. 390-399.
Elsevier DOI 1809
Phenology, Evergreen conifer forests, T, T, NDVI/EVI BibRef

Luo, Y.P.[Yun-Peng], El-Madany, T.S.[Tarek S.], Filippa, G.[Gianluca], Ma, X.L.[Xuan-Long], Ahrens, B.[Bernhard], Carrara, A.[Arnaud], Gonzalez-Cascon, R.[Rosario], Cremonese, E.[Edoardo], Galvagno, M.[Marta], Hammer, T.W.[Tiana W.], Pacheco-Labrador, J.[Javier], Martín, M.P.[M. Pilar], Moreno, G.[Gerardo], Perez-Priego, O.[Oscar], Reichstein, M.[Markus], Richardson, A.D.[Andrew D.], Römermann, C.[Christine], Migliavacca, M.[Mirco],
Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree-Grass Ecosystems,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link 1809
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And: Correction: RS(11), No. 6, 2019, pp. xx-yy.
DOI Link 1903
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Dash, J.P.[Jonathan P.], Pearse, G.D.[Grant D.], Watt, M.S.[Michael S.],
UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link 1809
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Guo, J.[Jing], Gong, P.[Peng],
The Potential of Spectral Indices in Detecting Various Stages of Afforestation over the Loess Plateau Region of China,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810
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Santos, M.J.[Maria J.], Disney, M.[Mathias], Chave, J.[Jérôme],
Detecting Human Presence and Influence on Neotropical Forests with Remote Sensing,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
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Rybakov, G.[Georgy], Peuhkurinen, J.[Jussi], Latva-Käyrä, P.[Petri], Villikka, M.[Maria], Sirparanta, S.[Sanna], Kolesnikov, A.[Alexander], Junttila, V.[Virpi], Kauranne, T.[Tuomo],
Combining Camera Relascope-Measured Field Plots and Multi-Seasonal Landsat 8 Imagery for Enhancing the Forest Inventory of Boreal Forests in Central Russia,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812
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Berveglieri, A.[Adilson], Imai, N.N.[Nilton N.], Tommaselli, A.M.G.[Antonio M.G.], Casagrande, B.[Baltazar], Honkavaara, E.[Eija],
Successional stages and their evolution in tropical forests using multi-temporal photogrammetric surface models and superpixels,
PandRS(146), 2018, pp. 548-558.
Elsevier DOI 1812
DSM, Forest classification, Forest succession, Temporal superpixel, Segmentation BibRef

McCarthy, M.J.[Matthew J.], Dimmitt, B.[Benjamin], Muller-Karger, F.E.[Frank E.],
Rapid Coastal Forest Decline in Florida's Big Bend,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812
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Honeck, E.[Erica], Castello, R.[Roberto], Chatenoux, B.[Bruno], Richard, J.P.[Jean-Philippe], Lehmann, A.[Anthony], Giuliani, G.[Gregory],
From a Vegetation Index to a Sustainable Development Goal Indicator: Forest Trend Monitoring Using Three Decades of Earth Observations across Switzerland,
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Garcia-Millan, V.E.[Virginia E.], Sanchez-Azofeifa, G.A.[G. Arturo],
Quantifying Changes on Forest Succession in a Dry Tropical Forest Using Angular-Hyperspectral Remote Sensing,
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Haro-Carrión, X.[Xavier], Southworth, J.[Jane],
Understanding Land Cover Change in a Fragmented Forest Landscape in a Biodiversity Hotspot of Coastal Ecuador,
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DOI Link 1901
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Moreno, A.[Adam], Neumann, M.[Mathias], Mohebalian, P.M.[Phillip M.], Thurnher, C.[Christopher], Hasenauer, H.[Hubert],
The Continental Impact of European Forest Conservation Policy and Management on Productivity Stability,
RS(11), No. 1, 2019, pp. xx-yy.
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Bai, B.X.[Bing-Xin], Tan, Y.M.[Yu-Min], Guo, D.[Dong], Xu, B.[Bo],
Dynamic Monitoring of Forest Land in Fuling District Based on Multi-Source Time Series Remote Sensing Images,
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Burke, M.W.V.[Morgen W.V.], Rundquist, B.C.[Bradley C.], Zheng, H.C.[Hao-Chi],
Detection of Shelterbelt Density Change Using Historic APFO and NAIP Aerial Imagery,
RS(11), No. 3, 2019, pp. xx-yy.
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Evaluate changes in windbreaks. BibRef

Watt, M.S.[Michael S.], Pearse, G.D.[Grant D.], Dash, J.P.[Jonathan P.], Melia, N.[Nathanael], Leonardo, E.M.C.[Ellen Mae C.],
Application of remote sensing technologies to identify impacts of nutritional deficiencies on forests,
PandRS(149), 2019, pp. 226-241.
Elsevier DOI 1903
ALS, Aerial laser scanning, Hyperspectral, Light detection and ranging BibRef

Shen, W.J.[Wen-Juan], Li, M.[Mingshi], Huang, C.Q.[Cheng-Quan], Tao, X.[Xin], Li, S.[Shu], Wei, A.[Anshi],
Mapping Annual Forest Change Due to Afforestation in Guangdong Province of China Using Active and Passive Remote Sensing Data,
RS(11), No. 5, 2019, pp. xx-yy.
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Tomppo, E.[Erkki], Antropov, O.[Oleg], Praks, J.[Jaan],
Boreal Forest Snow Damage Mapping Using Multi-Temporal Sentinel-1 Data,
RS(11), No. 4, 2019, pp. xx-yy.
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Marshak, C.[Charlie], Simard, M.[Marc], Denbina, M.[Michael],
Monitoring Forest Loss in ALOS/PALSAR Time-Series with Superpixels,
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Pinagé, E.R.[Ekena Rangel], Keller, M.[Michael], Duffy, P.[Paul], Longo, M.[Marcos], dos-Santos, M.N.[Maiza Nara], Morton, D.C.[Douglas C.],
Long-Term Impacts of Selective Logging on Amazon Forest Dynamics from Multi-Temporal Airborne LiDAR,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link 1903
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Rossi, F.[Fernando], Breidenbach, J.[Johannes], Puliti, S.[Stefano], Astrup, R.[Rasmus], Talbot, B.[Bruce],
Assessing Harvested Sites in a Forested Boreal Mountain Catchment through Global Forest Watch,
RS(11), No. 5, 2019, pp. xx-yy.
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Voight, C.[Carly], Hernandez-Aguilar, K.[Karla], Garcia, C.[Christina], Gutierrez, S.[Said],
Predictive Modeling of Future Forest Cover Change Patterns in Southern Belize,
RS(11), No. 7, 2019, pp. xx-yy.
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Dalagnol, R.[Ricardo], Phillips, O.L.[Oliver L.], Gloor, E.[Emanuel], Galvão, L.S.[Lênio S.], Wagner, F.H.[Fabien H.], Locks, C.J.[Charton J.], Aragão, L.E.O.C.[Luiz E. O. C.],
Quantifying Canopy Tree Loss and Gap Recovery in Tropical Forests under Low-Intensity Logging Using VHR Satellite Imagery and Airborne LiDAR,
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Chen, B.[Bin], Jin, Y.F.[Yu-Fang], Brown, P.[Patrick],
Automatic mapping of planting year for tree crops with Landsat satellite time series stacks,
PandRS(151), 2019, pp. 176-188.
Elsevier DOI 1904
Planting year, Time series analysis, NDVI, Google Earth Engine, Crop dynamics, Change detection, California BibRef

Lima, T.A.[Thaís Almeida], Beuchle, R.[René], Langner, A.[Andreas], Grecchi, R.C.[Rosana Cristina], Griess, V.C.[Verena C.], Achard, F.[Frédéric],
Comparing Sentinel-2 MSI and Landsat 8 OLI Imagery for Monitoring Selective Logging in the Brazilian Amazon,
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Pérez-Romero, J.[Javier], Navarro-Cerrillo, R.M.[Rafael María], Palacios-Rodriguez, G.[Guillermo], Acosta, C.[Cristina], Mesas-Carrascosa, F.J.[Francisco Javier],
Improvement of Remote Sensing-Based Assessment of Defoliation of Pinus spp. Caused by Thaumetopoea pityocampa Denis and Schiffermüller and Related Environmental Drivers in Southeastern Spain,
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Mi, J.X.[Jia-Xin], Yang, Y.J.[Yong-Jun], Zhang, S.L.[Shao-Liang], An, S.[Shi], Hou, H.P.[Hu-Ping], Hua, Y.F.[Yi-Fei], Chen, F.[Fuyao],
Tracking the Land Use/Land Cover Change in an Area with Underground Mining and Reforestation via Continuous Landsat Classification,
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Trujillo-Toro, J.[Jesus], Navarro-Cerrillo, R.M.[Rafael M.],
Analysis of Site-dependent Pinus halepensis Mill. Defoliation Caused by 'Candidatus Phytoplasma pini' through Shape Selection in Landsat Time Series,
RS(11), No. 16, 2019, pp. xx-yy.
DOI Link 1909
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Shimizu, K.[Katsuto], Ota, T.[Tetsuji], Mizoue, N.[Nobuya],
Detecting Forest Changes Using Dense Landsat 8 and Sentinel-1 Time Series Data in Tropical Seasonal Forests,
RS(11), No. 16, 2019, pp. xx-yy.
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Gebru, B.M.[Belay Manjur], Lee, W.K.[Woo-Kyun], Khamzina, A.[Asia], Lee, S.G.[Sle-Gee], Negash, E.[Emnet],
Hydrological Response of Dry Afromontane Forest to Changes in Land Use and Land Cover in Northern Ethiopia,
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Hamdi, Z.M.[Zayd Mahmoud], Brandmeier, M.[Melanie], Straub, C.[Christoph],
Forest Damage Assessment Using Deep Learning on High Resolution Remote Sensing Data,
RS(11), No. 17, 2019, pp. xx-yy.
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Anwander, J.[Julia], Brandmeier, M.[Melanie], Paczkowski, S.[Sebastian], Neubert, T.[Tarek], Paczkowska, M.[Marta],
Evaluating Different Deep Learning Approaches for Tree Health Classification Using High-Resolution Multispectral UAV Data in the Black Forest, Harz Region, and Gottinger Forest,
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Classifications of Forest Change by Using Bitemporal Airborne Laser Scanner Data,
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Salamanca, A.J.A.[Antonio Jesús Ariza], Navarro-Cerrillo, R.M.[Rafael María], Bonet-García, F.J.[Francisco J.], Pérez-Palazón, M.J.[Ma José], Polo, M.J.[María J.],
Integration of a Landsat Time-Series of NBR and Hydrological Modeling to Assess Pinus pinaster Aiton. Forest Defoliation in South-Eastern Spain,
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Bozek, P.[Piotr], Janus, J.[Jaroslaw], Mitka, B.[Bartosz],
Analysis of Changes in Forest Structure using Point Clouds from Historical Aerial Photographs,
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Dorji, Y.[Yonten], Annighöfer, P.[Peter], Ammer, C.[Christian], Seidel, D.[Dominik],
Response of Beech (Fagus sylvatica L.) Trees to Competition: New Insights from Using Fractal Analysis,
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Khare, S.[Siddhartha], Drolet, G.[Guillaume], Sylvain, J.D.[Jean-Daniel], Paré, M.C.[Maxime Charles], Rossi, S.[Sergio],
Assessment of Spatio-Temporal Patterns of Black Spruce Bud Phenology across Quebec Based on MODIS-NDVI Time Series and Field Observations,
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Haarpaintner, J.[Jörg], Hindberg, H.[Heidi],
Multi-Temporal and Multi-Frequency SAR Analysis for Forest Land Cover Mapping of the Mai-Ndombe District (Democratic Republic of Congo),
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Clark, M.L.[Matthew L.],
Comparison of multi-seasonal Landsat 8, Sentinel-2 and hyperspectral images for mapping forest alliances in Northern California,
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Land cover, Forest alliance, U.S. National Vegetation Classification (NVC), Support Vector Machine BibRef

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Hufkens, K.[Koen], de Haulleville, T.[Thalès], Kearsley, E.[Elizabeth], Jacobsen, K.[Kim], Beeckman, H.[Hans], Stoffelen, P.[Piet], Vandelook, F.[Filip], Meeus, S.[Sofie], Amara, M.[Michael], van Hirtum, L.[Leen], van den Bulcke, J.[Jan], Verbeeck, H.[Hans], Wingate, L.[Lisa],
Historical Aerial Surveys Map Long-Term Changes of Forest Cover and Structure in the Central Congo Basin,
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Adams, B.[Bryce], Iverson, L.[Louis], Matthews, S.[Stephen], Peters, M.[Matthew], Prasad, A.[Anantha], Hix, D.M.[David M.],
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Bergmüller, K.O.[Kai O.], Vanderwel, M.C.[Mark C.],
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Wang, C.L.[Chun-Ling], Wang, J.[Jianbang], He, Z.[Zhuoyu], Feng, M.[Min],
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Lu, S.Q.[Si-Qi], Zhang, C.R.[Chuan-Rong], Dong, J.[Jinwei], Adil, M.[Muhammad], Lu, H.[Heli],
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Sang, Z.H.[Zihao-Han], Hamann, A.[Andreas],
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Chapter on Cartography, Aerial Images, Buildings, Roads, Terrain, Forests, Trees, ATR continues in
Forest Disturbance, Regeneration, Regrowth .


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