The Role of Remote Sensing for the Assessment and Monitoring of Forest Health: A Systematic Evidence Synthesis
暂无分享,去创建一个
Hector Nieto | Alba Viana-Soto | Pablo Torres | Mariano García | Marina Rodes-Blanco | Mariano García | H. Nieto | P. Torres | Alba Viana-Soto | Marina Rodes-Blanco | Marina Rodes-Blanco
[1] Hengameh Shiranvand,et al. An analysis of dieback areas of Zagros oak forests using remote sensing data case study: Lorestan oak forest, Iran , 2020, Modeling Earth Systems and Environment.
[2] João Catalão Fernandes,et al. Assessing the Use of Sentinel-2 Time Series Data for Monitoring Cork Oak Decline in Portugal , 2019, Remote. Sens..
[3] Nadhir Al-Ansari,et al. Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction , 2020, Symmetry.
[4] M. Hasanlou,et al. Mapping oak decline through long-term analysis of time series of satellite images in the forests of Malekshahi, Iran , 2019, International Journal of Remote Sensing.
[5] Francisco Javier Mesas-Carrascosa,et al. 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 , 2019, Remote. Sens..
[6] Ali Shamsoddini,et al. Mapping red edge-based vegetation health indicators using Landsat TM data for Australian native vegetation cover , 2018, Earth Science Informatics.
[7] Nhat-Duc Hoang,et al. GIS-based spatial prediction of tropical forest fire danger using a new hybrid machine learning method , 2018, Ecol. Informatics.
[8] Yong Pang,et al. Characterizing forest canopy structure with lidar composite metrics and machine learning , 2011 .
[9] Hong Wang,et al. Mapping Robinia Pseudoacacia Forest Health Conditions by Using Combined Spectral, Spatial, and Textural Information Extracted from IKONOS Imagery and Random Forest Classifier , 2015, Remote. Sens..
[10] Dhruval K. Bhavsar,et al. Red Edge Index as an Indicator of Vegetation Growth and Vigor Using Hyperspectral Remote Sensing Data , 2017 .
[11] J. Elkinton,et al. Extensive gypsy moth defoliation in Southern New England characterized using Landsat satellite observations , 2018, Biological Invasions.
[12] F. Stephen,et al. Emergent insects, pathogens and drought shape changing patterns in oak decline in North America and Europe , 2015 .
[13] F. Fassnacht,et al. Intra-annual Ips typographus outbreak monitoring using a multi-temporal GIS analysis based on hyperspectral and ALS data in the Białowieża Forests , 2019, Forest Ecology and Management.
[14] Lluís Brotons,et al. Assessing Pine Processionary Moth Defoliation Using Unmanned Aerial Systems , 2017 .
[15] Enric Pastor,et al. UAV Flight Experiments Applied to the Remote Sensing of Vegetated Areas , 2014, Remote. Sens..
[16] N. Greggio,et al. Effects of saltwater intrusion on pinewood vegetation using satellite ASTER data: the case study of Ravenna (Italy) , 2015, Environmental Monitoring and Assessment.
[17] Michael Dorman,et al. What determines tree mortality in dry environments? A multi-perspective approach. , 2015, Ecological applications : a publication of the Ecological Society of America.
[18] K. Tansey,et al. Detecting the effects of hydrocarbon pollution in the Amazon forest using hyperspectral satellite images. , 2015, Environmental pollution.
[19] Michael R. Wagner,et al. Concepts of forest health: Utilitarian and ecosystem perspectives , 1994 .
[20] L. Daniels,et al. Novel forest decline triggered by multiple interactions among climate, an introduced pathogen and bark beetles , 2017, Global change biology.
[21] N. McDowell,et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests , 2010 .
[22] P. Wężyk,et al. Estimating defoliation of Scots pine stands using machine learning methods and vegetation indices of Sentinel-2 , 2018 .
[23] A. Pitman,et al. The impact of climate change on the risk of forest and grassland fires in Australia , 2007 .
[24] Andrea Nardini,et al. Correlation of Field-Measured and Remotely Sensed Plant Water Status as a Tool to Monitor the Risk of Drought-Induced Forest Decline , 2020, Forests.
[25] Joanne C. White,et al. Remote Sensing Technologies for Enhancing Forest Inventories: A Review , 2016 .
[26] M. J. Holmes,et al. Early warning signals in plant disease outbreaks , 2018, Ecological Modelling.
[27] Stefan Leyk,et al. Detection of mountain pine beetle-killed ponderosa pine in a heterogeneous landscape using high-resolution aerial imagery , 2015 .
[28] Maciej Lisiewicz,et al. Species-related single dead tree detection using multi-temporal ALS data and CIR imagery , 2018, Remote Sensing of Environment.
[29] Bruno Fady-Welterlen. Is there really more biodiversity in Mediterranean forest ecosystems , 2005 .
[30] C. D. Di Bella,et al. Forest browning trends in response to drought in a highly threatened mediterranean landscape of South America , 2020, Ecological Indicators.
[31] M. Heurich,et al. Influence of selected habitat and stand factors on bark beetle Ips typographus (L.) outbreak in the Białowieża Forest , 2020 .
[32] Donghai Wu,et al. Tipping point of a conifer forest ecosystem under severe drought , 2015 .
[33] Ranga B. Myneni,et al. Amazon Forests' Response to Droughts: A Perspective from the MAIAC Product , 2016, Remote. Sens..
[34] M. Moura,et al. Space-time analysis of vegetation trends and drought occurrence in domain area of tropical forest. , 2019, Journal of environmental management.
[35] Langning Huo,et al. Tree defoliation classification based on point distribution features derived from single-scan terrestrial laser scanning data , 2019, Ecological Indicators.
[36] C. Daehler,et al. Long-term decline of native tropical dry forest remnants in an invaded Hawaiian landscape , 2019, Biodiversity and Conservation.
[37] S. Keesstra,et al. Identifying tree health using sentinel-2 images: a case study on Tortrix viridana L. infected oak trees in Western Iran , 2020, Geocarto International.
[38] Huaguo Huang,et al. Evaluating the Potential of WorldView-3 Data to Classify Different Shoot Damage Ratios of Pinus yunnanensis , 2020 .
[39] Kaelyn A. Finley,et al. Forest Health Management and Detection of Invasive Forest Insects , 2016 .
[40] Marco Heurich,et al. Understanding Forest Health with Remote Sensing-Part II - A Review of Approaches and Data Models , 2017, Remote. Sens..
[41] Johann G. Goldammer,et al. Developing a global early warning system for wildland fire , 2006 .
[42] D. Gallego,et al. Forest Decline Triggered by Phloem Parasitism-Related Biotic Factors in Aleppo Pine (Pinus halepensis) , 2019, Forests.
[43] Ning Zhang,et al. Extraction of tree crowns damaged by Dendrolimus tabulaeformis Tsai et Liu via spectral-spatial classification using UAV-based hyperspectral images , 2020, Plant methods.
[44] G. Powell,et al. Terrestrial Ecoregions of the World: A New Map of Life on Earth , 2001 .
[45] Michael G. Grant,et al. Detection of dead standing Eucalyptus camaldulensis without tree delineation for managing biodiversity in native Australian forest , 2018, Int. J. Appl. Earth Obs. Geoinformation.
[46] A. Gitelson,et al. Three‐band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves , 2006 .
[47] Niklaus E. Zimmermann,et al. Climate change may cause severe loss in the economic value of European forest land , 2013 .
[48] Heiko Balzter,et al. Modelling forest canopy height by integrating airborne LiDAR samples with satellite Radar and multispectral imagery , 2018, Int. J. Appl. Earth Obs. Geoinformation.
[49] Robert N. Coulson,et al. Development of monitoring methods for Hemlock Woolly Adelgid induced tree mortality within a Southern Appalachian landscape with inhibited access , 2016 .
[50] A. Pandey,et al. Forest health estimation in Sholayar Reserve Forest, Kerala using AVIRIS-NG hyperspectral data , 2019, Spatial Information Research.
[51] R. Sánchez-Cuesta,et al. Integration of WorldView-2 and airborne laser scanning data to classify defoliation levels in Quercus ilex L. Dehesas affected by root rot mortality: Management implications , 2019, Forest Ecology and Management.
[52] B. Khashir,et al. THE ECONOMIC VALUE OF FOREST ECOSYSTEM SERVICES , 2015 .
[53] Stuart H. Sweeney,et al. A framework for detecting conifer mortality across an ecoregion using high spatial resolution spaceborne imaging spectroscopy , 2018 .
[54] Xianjun Hao,et al. Sensitivity studies of the moisture effects on MODIS SWIR reflectance and vegetation water indices , 2008 .
[55] P. Brando,et al. Forest health and global change , 2015, Science.
[56] Véronique Chéret,et al. Monitoring forest decline through remote sensing time series analysis , 2013 .
[57] Heiko Paulheim,et al. Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches , 2018, Remote. Sens..
[58] Andrea Taramelli,et al. Exploring the Dunes: The Correlations between Vegetation Cover Pattern and Morphology for Sediment Retention Assessment Using Airborne Multisensor Acquisition , 2020, Remote. Sens..
[59] Manuel Sánchez de la Orden,et al. Hyperspectral and multispectral satellite sensors for mapping chlorophyll content in a Mediterranean Pinus sylvestris L. plantation , 2014, Int. J. Appl. Earth Obs. Geoinformation.
[60] Angel Fernandez-Carrillo,et al. Monitoring Bark Beetle Forest Damage in Central Europe. A Remote Sensing Approach Validated with Field Data , 2020, Remote. Sens..
[61] Marco Heurich,et al. Machine learning methods’ performance in radiative transfer model inversion to retrieve plant traits from Sentinel-2 data of a mixed mountain forest , 2020, Int. J. Digit. Earth.
[62] P. Lukeš,et al. Remote sensing-based forest health monitoring systems – case studies from Czechia and Slovakia , 2018 .
[63] Josep Peñuelas,et al. Satellite data as indicators of tree biomass growth and forest dieback in a Mediterranean holm oak forest , 2014, Annals of Forest Science.
[64] Chengquan Huang,et al. Forest disturbance across the conterminous United States from 1985-2012: The emerging dominance of forest decline , 2016 .
[65] Patterns of mortality in a montane mixed-conifer forest in San Diego County, California. , 2017, Ecological applications : a publication of the Ecological Society of America.
[66] Laurie A. Chisholm,et al. Classification of Australian Native Forest Species Using Hyperspectral Remote Sensing and Machine-Learning Classification Algorithms , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[67] D. Coomes,et al. Resilience of Spanish forests to recent droughts and climate change , 2020, Global change biology.
[68] D. Gómez,et al. Ability of Remote Sensing Systems to Detect Bark Beetle Spots in the Southeastern US , 2020 .
[69] Daniel M. Johnson,et al. Projected drought effects on the demography of Ashe juniper populations inferred from remote measurements of tree canopies , 2018, Plant Ecology.
[70] A. Sarris,et al. Detection of exposed and subsurface archaeological remains using multi-sensor remote sensing , 2007 .
[71] Feng Gao,et al. Comparison of satellite-derived LAI and precipitation anomalies over Brazil with a thermal infrared-based Evaporative Stress Index for 2003–2013 , 2015 .
[72] Paul Gérard Gbetkom,et al. A New Index to Better Detect and Monitor Agricultural Drought in Niger Using Multisensor Remote Sensing Data , 2020 .
[73] Xuehua Liu,et al. Review on carbon storage estimation of forest ecosystem and applications in China , 2019, Forest Ecosystems.
[74] L. Villard,et al. Synthetic aperture radar sensitivity to forest changes: A simulations-based study for the Romanian forests. , 2019, The Science of the total environment.
[75] H. Tani,et al. Combination of SAR Polarimetric Parameters for Estimating Tropical Forest Aboveground Biomass , 2020 .
[76] Cornelius Senf,et al. Canopy mortality has doubled in Europe’s temperate forests over the last three decades , 2018, Nature Communications.
[77] Rose-Anne Bell,et al. Investigating Banksia Coastal Woodland Decline Using Multi-Temporal Remote Sensing and Field-Based Monitoring Techniques , 2020, Remote. Sens..
[78] Jason S. Sibold,et al. Spatial and temporal trends of drought effects in a heterogeneous semi-arid forest ecosystem. , 2016 .
[79] J. Peñuelas,et al. Early Diagnosis of Vegetation Health From High-Resolution Hyperspectral and Thermal Imagery: Lessons Learned From Empirical Relationships and Radiative Transfer Modelling , 2019, Current Forestry Reports.
[80] D. R. Cutler,et al. Utah State University From the SelectedWorks of , 2017 .
[81] Anthony G. Vorster,et al. Severity of a Mountain Pine Beetle Outbreak Across a Range of Stand Conditions in Fraser Experimental Forest, Colorado, United States , 2017 .
[82] Mary Beth Parent,et al. The Browning of Alaska's Boreal Forest , 2010, Remote. Sens..
[83] Biljana Macura,et al. Eight problems with literature reviews and how to fix them , 2020, Nature Ecology & Evolution.
[84] T. Painter,et al. Quantifying insect-related forest mortality with the remote sensing of snow , 2017 .
[85] Abduwasit Ghulam,et al. Mapping drought-impacted vegetation stress in California using remote sensing , 2017 .
[86] Joachim Hill,et al. Monitoring of Canopy Stress Symptoms in New Zealand Kauri Trees Analysed with AISA Hyperspectral Data , 2020, Remote. Sens..
[87] Wu Ma,et al. Mapping Forest Health Using Spectral and Textural Information Extracted from SPOT-5 Satellite Images , 2016, Remote. Sens..
[88] A. Barbati,et al. Climate change impacts, adaptive capacity, and vulnerability of European forest ecosystems , 2010 .
[89] Marco Heurich,et al. Mapping leaf chlorophyll content from Sentinel-2 and RapidEye data in spruce stands using the invertible forest reflectance model , 2019, Int. J. Appl. Earth Obs. Geoinformation.
[90] R. Sánchez‐Salguero,et al. Long-term nutrient imbalances linked to drought-triggered forest dieback. , 2019, The Science of the total environment.
[91] G. Asner,et al. Episodic Canopy Structural Transformations and Biological Invasion in a Hawaiian Forest , 2017, Front. Plant Sci..
[92] D. Menzies. Systematic reviews and meta-analyses. , 2011, The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease.
[93] M. White,et al. Assessment of ecosystems: A system for rigorous and rapid mapping of floodplain forest condition for Australia's most important river , 2018 .
[94] Magí Franquesa,et al. Timing of Drought Triggers Distinct Growth Responses in Holm Oak: Implications to Predict Warming-Induced Forest Defoliation and Growth Decline , 2015 .
[95] Sergey V. Alexandrov,et al. Mapping individual tree health using full-waveform airborne laser scans and imaging spectroscopy: A case study for a floodplain eucalypt forest , 2016 .
[96] Tomasz Hycza,et al. Factors Affecting the Health Condition of Spruce Forests in Central European Mountains-Study Based on MultitemporalRapidEye Satellite Images , 2019, Forests.
[97] C. Jeganathan,et al. Time-series cloud noise mapping and reduction algorithm for improved vegetation and drought monitoring , 2017 .
[98] Nuno Silva,et al. Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities , 2020, Remote. Sens..
[99] D. Coomes,et al. Monitoring ash dieback (Hymenoscyphus fraxineus) in British forests using hyperspectral remote sensing , 2020, Remote Sensing in Ecology and Conservation.
[100] Jan G. P. W. Clevers,et al. Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3 , 2013, Int. J. Appl. Earth Obs. Geoinformation.
[101] Decline of dark coniferous stands in Baikal Region , 2016, Contemporary Problems of Ecology.
[102] Michael A. Wulder,et al. Surveying mountain pine beetle damage of forests: A review of remote sensing opportunities , 2006 .
[103] R. E. Harrison,et al. Review of Satellite Remote Sensing Use in Forest Health Studies~!2010-01-27~!2010-04-05~!2010-06-29~! , 2010 .
[104] Ruiliang Pu,et al. Mapping health levels of Robinia pseudoacacia forests in the Yellow River delta, China, using IKONOS and Landsat 8 OLI imagery , 2015 .
[105] Christopher Brooke,et al. Mapping Heterogeneous Buried Archaeological Features Using Multisensor Data from Unmanned Aerial Vehicles , 2019, Remote. Sens..
[106] Anthony G. Vorster,et al. Mapping Progression and Severity of a Southern Colorado Spruce Beetle Outbreak Using Calibrated Image Composites , 2018, Forests.
[107] S. Bruin,et al. 50 years of water extraction in the Pampa del Tamarugal basin: Can Prosopis tamarugo trees survive in the hyper-arid Atacama Desert (Northern Chile)? , 2016 .
[108] Modeling Chlorophyll Content of Korean Pine Needles with NIR and SVM , 2011 .
[109] C. Potter,et al. Integrating multi-sensor remote sensing and species distribution modeling to map the spread of emerging forest disease and tree mortality , 2019, Remote Sensing of Environment.
[110] C. Fischer,et al. Climate Change Effects on Mediterranean Forests and Preventive Measures , 2006, New Forests.
[111] P. Dennison,et al. A multi-sensor, multi-scale approach to mapping tree mortality in woodland ecosystems , 2020 .
[112] Rafael M. Navarro-Cerrillo,et al. Analysis of Site-dependent Pinus halepensis Mill. Defoliation Caused by 'Candidatus Phytoplasma pini' through Shape Selection in Landsat Time Series , 2019, Remote. Sens..
[113] S. Barr,et al. Canopy temperature from an Unmanned Aerial Vehicle as an indicator of tree stress associated with red band needle blight severity , 2019, Forest Ecology and Management.
[114] Shan Gao,et al. Contrasting Responses of Planted and Natural Forests to Drought Intensity in Yunnan, China , 2016, Remote. Sens..
[115] Jennifer L. Dungan,et al. Seasonal LAI in slash pine estimated with landsat TM , 1992 .
[116] Valentín Pando,et al. Remote monitoring of defoliation by the beech leaf-mining weevil Rhynchaenus fagi in northern Spain , 2015 .
[117] Thomas Hilker,et al. Leveraging Multi-Sensor Time Series Datasets to Map Short- and Long-Term Tropical Forest Disturbances in the Colombian Andes , 2017, Remote. Sens..
[118] Peter Surový,et al. Unmanned aerial vehicles (UAV) for assessment of qualitative classification of Norway spruce in temperate forest stands , 2018, Geo spatial Inf. Sci..
[119] Lloyd Windrim,et al. Tree Detection and Health Monitoring in Multispectral Aerial Imagery and Photogrammetric Pointclouds Using Machine Learning , 2020, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[120] Roberta E. Martin,et al. Remote measurement of canopy water content in giant sequoias (Sequoiadendron giganteum) during drought , 2017, Forest Ecology and Management.
[121] Benjamin C. Bright,et al. Mapping Multiple Insect Outbreaks across Large Regions Annually Using Landsat Time Series Data , 2020, Remote. Sens..
[122] P. Bernier,et al. Adapting forests and their management to climate change: an overview. , 2009 .
[123] C. Marchese. Biodiversity hotspots: A shortcut for a more complicated concept , 2015 .
[124] Marco Heurich,et al. In Situ/Remote Sensing Integration to Assess Forest Health - A Review , 2016, Remote. Sens..
[125] Jian Yang,et al. Ash Decline Assessment in Emerald Ash Borer Infested Natural Forests Using High Spatial Resolution Images , 2016, Remote. Sens..
[126] Gregory Asner,et al. A Spectral Mapping Signature for the Rapid Ohia Death (ROD) Pathogen in Hawaiian Forests , 2018, Remote. Sens..
[127] Alan Grainger,et al. Dynamics of global forest area: Results from the FAO Global Forest Resources Assessment 2015 , 2015 .
[128] Heiko Balzter,et al. Extrapolating Forest Canopy Fuel Properties in the California Rim Fire by Combining Airborne LiDAR and Landsat OLI Data , 2017, Remote. Sens..
[129] Jonathan A. Walter,et al. Application of multidimensional structural characterization to detect and describe moderate forest disturbance , 2020 .
[130] Claudia Notarnicola,et al. Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data , 2015, Remote. Sens..
[131] Yufang Jin,et al. Detecting Drought-Induced Tree Mortality in Sierra Nevada Forests with Time Series of Satellite Data , 2017, Remote. Sens..
[132] Stefan Dech,et al. Remote Sensing Time Series: Revealing Land Surface Dynamics , 2015 .
[133] Ning Zhang,et al. Assessing the Defoliation of Pine Forests in a Long Time-Series and Spatiotemporal Prediction of the Defoliation Using Landsat Data , 2018, Remote. Sens..
[134] Gonzalo Pajares,et al. Overview and Current Status of Remote Sensing Applications Based on Unmanned Aerial Vehicles (UAVs) , 2015 .
[135] John D. J. Clare,et al. Satellite-detected forest disturbance forecasts American marten population decline: The case for supportive space-based monitoring , 2019, Biological Conservation.
[136] Dirk Tiede,et al. Evaluation of Different Machine Learning Algorithms for Scalable Classification of Tree Types and Tree Species Based on Sentinel-2 Data , 2018, Remote. Sens..
[137] Tiziana Gentilesca,et al. Drought-induced oak decline in the western Mediterranean region: an overview on current evidences, mechanisms and management options to improve forest resilience , 2017 .
[138] Hans Petersson,et al. Eye on the Taiga: Removing Global Policy Impediments to Safeguard the Boreal Forest , 2014 .
[139] N. Eisenhauer,et al. Cascading spatial and trophic impacts of oak decline on the soil food web , 2018, Journal of Ecology.
[140] V. M. Chowdary,et al. Forest health assessment for geo-environmental planning and management in hilltop mining areas using Hyperion and Landsat data , 2019, Ecological Indicators.
[141] A. Noroozi,et al. Monitoring mortality in a semiarid forest under the influence of prolonged drought in Zagros region , 2020, International Journal of Environmental Science and Technology.
[142] W. Cohen,et al. Visual interpretation and time series modeling of Landsat imagery highlight drought's role in forest canopy declines , 2018, Ecosphere.
[143] Bikram Pratap Banerjee,et al. Health condition assessment for vegetation exposed to heavy metal pollution through airborne hyperspectral data , 2017, Environmental Monitoring and Assessment.
[144] Marco Heurich,et al. Sensitivity of Landsat-8 OLI and TIRS Data to Foliar Properties of Early Stage Bark Beetle (Ips typographus, L.) Infestation , 2019, Remote. Sens..
[145] Ricardo Dalagnol,et al. Vulnerability of Amazonian forests to repeated droughts , 2018, Philosophical Transactions of the Royal Society B: Biological Sciences.
[146] M. Goulden,et al. California forest die-off linked to multi-year deep soil drying in 2012–2015 drought , 2019, Nature Geoscience.
[147] Rick L. Lawrence,et al. Time-series approach for mapping mountain pine beetle infestation extent and severity in the U.S. Central Rocky Mountains , 2018, Journal of Applied Remote Sensing.
[148] L. Anderegg,et al. Testing early warning metrics for drought‐induced tree physiological stress and mortality , 2019, Global change biology.
[149] Sergio M. Vicente-Serrano,et al. Drought impacts on vegetation activity in the Mediterranean region: An assessment using remote sensing data and multi-scale drought indicators , 2017 .
[150] Huaguo Huang,et al. Detecting Shoot Beetle Damage on Yunnan Pine Using Landsat Time-Series Data , 2018 .
[151] Fabio Recanatesi,et al. Monitoring Mediterranean Oak Decline in a Peri-Urban Protected Area Using the NDVI and Sentinel-2 Images: The Case Study of Castelporziano State Natural Reserve , 2018, Sustainability.
[152] Zhihua Liu,et al. Vegetation Dynamics in the Upper Guinean Forest Region of West Africa from 2001 to 2015 , 2016, Remote. Sens..
[153] Francisco Herrera,et al. Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning , 2019, Remote. Sens..
[154] B. Wardlow,et al. Assessing responses of Betula papyrifera to climate variability in a remnant population along the Niobrara River Valley in Nebraska, U.S.A., through dendroecological and remote-sensing techniques , 2019, Canadian Journal of Forest Research.
[155] Gao,et al. Combining GF-2 and Sentinel-2 Images to Detect Tree Mortality Caused by Red Turpentine Beetle during the Early Outbreak Stage in North China , 2020 .
[156] Tomáš Bucha,et al. Applicability of a vegetation indices-based method to map bark beetle outbreaks in the High Tatra Mountains , 2015 .
[157] Jamil Amanollahi,et al. Monitoring infestations of oak forests by Tortrix viridana (Lepidoptera: Tortricidae) using remote sensing , 2016 .
[158] Lian-Zhi Huo,et al. Object-Based Classification of Forest Disturbance Types in the Conterminous United States , 2019, Remote. Sens..
[159] Joanne C. White,et al. Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective* , 2016, The Canadian Entomologist.
[160] Olga V. Brovkina,et al. Composite indicator for monitoring of Norway spruce stand decline , 2017 .
[161] Jordi Martínez-Vilalta,et al. Satellite-based vegetation optical depth as an indicator of drought-driven tree mortality , 2019, Remote Sensing of Environment.
[162] World Food and Agriculture - Statistical Yearbook 2020 , 2020 .
[163] R. Sánchez‐Salguero,et al. Drought legacies are short, prevail in dry conifer forests and depend on growth variability , 2020, Journal of Ecology.
[164] S. Goetz,et al. Detecting early warning signals of tree mortality in boreal North America using multiscale satellite data , 2018, Global change biology.
[165] A. Shvidenko,et al. Boreal forest health and global change , 2015, Science.
[166] Michael H. Glantz,et al. Early Warning Systems Defined , 2014, Reducing Disaster: Early Warning Systems For Climate Change.
[167] Saso Dzeroski,et al. Estimating vegetation height and canopy cover from remotely sensed data with machine learning , 2010, Ecol. Informatics.
[168] Omid Abdi,et al. Climate-Triggered Insect Defoliators and Forest Fires Using Multitemporal Landsat and TerraClimate Data in NE Iran: An Application of GEOBIA TreeNet and Panel Data Analysis , 2019, Sensors.
[169] Nick van de Giesen,et al. Water stress detection in the Amazon using radar , 2017 .
[170] Hans-Joachim Klemmt,et al. Are Scots pine forest edges particularly prone to drought-induced mortality? , 2018 .
[171] Marco Heurich,et al. Understanding Forest Health with Remote Sensing -Part I - A Review of Spectral Traits, Processes and Remote-Sensing Characteristics , 2016, Remote. Sens..
[172] S. Goetz,et al. Impacts of climate and insect herbivory on productivity and physiology of trembling aspen (Populus tremuloides) in Alaskan boreal forests , 2019, Environmental Research Letters.
[173] B. Rock,et al. Low-level Adelges tsugae infestation detection in New England through partition modeling of Landsat data , 2017 .
[174] J. Ioannidis,et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. , 2009, Journal of clinical epidemiology.
[175] Henning Buddenbaum,et al. Stress Detection in New Zealand Kauri Canopies with WorldView-2 Satellite and LiDAR Data , 2020, Remote. Sens..
[176] P. Beck,et al. Understanding the temporal dimension of the red-edge spectral region for forest decline detection using high-resolution hyperspectral and Sentinel-2a imagery , 2018, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.
[177] Ewa Grabska,et al. Continuous Detection of Small-Scale Changes in Scots Pine Dominated Stands Using Dense Sentinel-2 Time Series , 2020, Remote. Sens..
[178] Tarin Paz-Kagan,et al. Drivers of woody canopy water content responses to drought in a Mediterranean-type ecosystem. , 2017, Ecological applications : a publication of the Ecological Society of America.