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Remote sensing forest health assessment – a comprehensive literature review on a European level

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Feb 06, 2025

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Abdollahnejad, A., Panagiotidis, D., 2020: Tree Species Classification and Health Status Assessment for a Mixed Broadleaf-Conifer Forest with UAS Multi-spectral Imaging. Remote Sensing, 12:3722. Search in Google Scholar

Abdollahnejad, A., Panagiotidis, D., Surovy, P., Mod-linger, R., 2021: Investigating the Correlation between Multisource Remote Sensing Data for Predicting Potential Spread of Ips typographus L. Spots in Healthy Trees. Remote Sensing, 13:4953. Search in Google Scholar

Abdullah, H., Darvishzadeh, R., Skidmore, A., Groen, T., Heurich, M., 2018: European spruce bark beetle (Ips typographus L.) green attack affects foliar reflectance and biochemical properties. International Journal of Applied Earth Observation and Geoinformation, 64:199–209. Search in Google Scholar

Adrien, M., Piégay, H., Lisein, J., Claessens, H., Lejeune, P., 2016: Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from Unmanned Aerial System. Environmental Monitoring and Assessment, 188:146. Search in Google Scholar

Ahmed, S., Nicholson, C. E., Muto, P., Perry, J., Dean, J., 2021: Applied aerial spectroscopy: A case study on remote sensing of an ancient and semi-natural woodland. PLoS ONE, 16:e0260056. Search in Google Scholar

Algeet Abarquero, N., Guillen-Climent, M., Mas, H., Tomé, J., Fernández-Landa, A., 2020: Using hipersepctral images for decay detection in Pinus halepensis (Mill.) in the Mediterranean forest. Revista de Teledetección, 55:59–69. Search in Google Scholar

Ali, A. M., Abdullah, H., Darvishzadeh, R., Skidmore, A. K., Heurich, M., Roeoesli, C. et al., 2021: Canopy chlorophyll content retrieved from time series remote sensing data as a proxy for detecting bark beetle infestation. Remote Sensing Applications: Society and Environment, 22:100524. Search in Google Scholar

Allen, B., Dalponte, M., Ørka, H., Næsset, E., Puliti, S., Astrup, R. et al., 2022: UAV-Based Hyperspectral Imagery for Detection of Root, Butt, and Stem Rot in Norway Spruce. Remote Sensing, 14:3830. Search in Google Scholar

Andrija, K., Linardić, D., Pernar, R., 2021: Framework for Spatial and Temporal Monitoring of Urban Forest and Vegetation Conditions: Case Study Zagreb, Croatia. Sustainability, 13:6055. Search in Google Scholar

Ariza Salamanca, A., Navarro-Cerrillo, Bonet-García, Palazón, Polo, M. J., 2019: Integration of a Landsat Time-Series of NBR and Hydrological Modeling to Assess Pinus pinaster Aiton. Forest Defoliation in South-Eastern Spain. Remote Sensing, 11:2291. Search in Google Scholar

Avetisyan, D., Borisova, D., Velizarova, E., 2021. Integrated Evaluation of Vegetation Drought Stress through Satellite Remote Sensing. Forests, 12:974. Search in Google Scholar

Baders, E., Romāns, E., Desaine, I., Krisans, O., Seipulis, A., Donis, J. et al., 2022, An Integration of Linear Model and ‘Random Forest’ Techniques for Prediction of Norway Spruce Vitality: A Case Study of the Hemiboreal Forest, Latvia. Remote Sensing, 14:2122. Search in Google Scholar

Balazy, R., Ciesielski, M., Waraksa, P., Zasada, M., Zawiła-Niedźwiecki, T., 2019a: Deforestation Processes in the Polish Mountains in the Context of Terrain Topography. Forests, 10:1027. Search in Google Scholar

Balazy, R., Hycza, T., Kamińska, A., Osińska-Skotak, K., 2019b: Factors Affecting the Health Condition of Spruce Forests in Central European Mountains-Study Based on Multitemporal RapidEye Satellite Images. Forests, 10:943. Search in Google Scholar

Barka, I., Lukeš, P., Bucha, T., Hlásny, T., Strejček, R., Mlčoušek, M. et al., 2018: Remote sensing-based forest health monitoring systems-case studies from Czechia and Slovakia. Central European Forestry Journal, 64:259–275. Search in Google Scholar

Barmpoutis, P., Stathaki, T., Kamperidou, V., 2019: Monitoring of Trees’ Health Condition Using a UAV Equipped with Low-cost Digital Camera. ICASSP 2019-2019 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP). Brighton, UK, p. 8291–8295. Search in Google Scholar

Barnes, C., Balzter, H., Barrett, K., Eddy, J., Milner, S., Suarez Minguez, J., 2017: Individual Tree Crown Delineation from Airborne Laser Scanning for Diseased Larch Forest Stands. Remote Sensing, 9:231. Search in Google Scholar

Bárta, V., Lukeš, P., Homolova, L., 2021: Early detection of bark beetle infestation in Norway spruce forests of Central Europe using Sentinel-2. International Journal of Applied Earth Observation and Geoinformation, 100:102335. Search in Google Scholar

Blaga, L., Josan, I., Herman, G., Grama, V., Nistor, S., Suba, N.-S., 2019: Assessment of the Forest Health Through Remote Sensing Techniques in Valea Roşie Natura 2000 Site, Bihor County, Romania. Journal of Applied Engineering Sciences, 9:207–215. Search in Google Scholar

Briechle, S., Krzystek, P., Vosselman, G., 2021: Silvi-Net – A dual-CNN approach for combined classification of tree species and standing dead trees from remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 98:102292. Search in Google Scholar

Brovkina, O., Cienciala, E., Zemek, F., Lukeš, P., Fabiánek, T., Russ, R., 2017: Composite indicator for monitoring of Norway spruce stand decline. European Journal of Remote Sensing, 50:550–563. Search in Google Scholar

Brovkina, O., Cienciala, E., Surovy, P., Janata, P., 2018: Unmanned aerial vehicles (UAV) for assessment of qualitative classification of Norway spruce in temperate forest stands. Geo-Spatial Information Science, 21:12–20. Search in Google Scholar

Bryk, M., Kołodziej, B., Pliszka, R., 2021: Changes of Norway Spruce Health in the Białowieża Forest (CE Europe) in 2013–2019 during a Bark Beetle Infestation, Studied with Landsat Imagery. Forests, 12:34. Search in Google Scholar

Buras, A., Rammig, A., Zang, C., 2020: Quantifying impacts of the 2018 drought on European eco-systems in comparison to 2003. Biogeosciences, 17:1655–1672. Search in Google Scholar

Camino, C., Araño, K., Berni, J. A., Dierkes, H., Trapero-Casas, J. L., León-Ropero, G. et al., 2022: Detecting Xylella fastidiosa in a machine learning framework using Vcmax and leaf biochemistry quantified with airborne hyperspectral imagery. Remote Sensing of Environment, 282:113281. Search in Google Scholar

Candotti, A., De Giglio, M., Dubbini, M., Tomelleri, E., 2022: A Sentinel-2 Based Multi-Temporal Monitoring Framework for Wind and Bark Beetle Detection and Damage Mapping. Remote Sensing, 14:6105. Search in Google Scholar

Cardil, A., Vepakomma, U., Brotons, L., 2017: Assessing Pine Processionary Moth Defoliation Using Unmanned Aerial Systems. Forests, 8:402. Search in Google Scholar

Cârlan, I., Mihai, B.-A., Nistor, C., Große-Stoltenberg, A., 2020: Identifying urban vegetation stress factors based on open access remote sensing imagery and field observations. Ecological Informatics, 55:101032. Search in Google Scholar

Chan, A., Barnes, C., Swinfield, T., Coomes, D., 2020: Monitoring ash dieback (Hymenoscyphus fraxineus) in British forests using hyperspectral remote sensing. Remote Sensing in Ecology and Conservation, 7:306–320. Search in Google Scholar

Chaparro, D., Piles, M., Martinez Vilalta, J., Vall-llossera, M., Vayreda, J., Banqué-Casanovas, M. et al., 2018: Modelling Forest Decline Using Smos Soil Moisture and Vegetation Optical Depth. IGARSS 2018 – 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, p. 1459–1462. Search in Google Scholar

Chi, D., Degerickx, J., Yu, K., Somers, B., 2020: Urban Tree Health Classification Across Tree Species by Combining Airborne Laser Scanning and Imaging Spectroscopy. Remote Sensing, 12:2435. Search in Google Scholar

Cucca, B., Recanatesi, F., Ripa, M., 2020: Evaluating the Potential of Vegetation Indices in Detecting Drought Impact Using Remote Sensing Data in a Mediterranean Pinewood. In: Gervasi, O. et al. (eds.): Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science, vol. 12253. Cham, Springer, p. 50–62. Search in Google Scholar

Curran, P., 1980: Multispectral remote sensing of vegetation amount. Progress in Physical Geography: Earth and Environment, 4:315–341. Search in Google Scholar

Dalponte, M., Kallio, A., Ørka, H., Næsset, E., Gobakken, T., 2022: Wood Decay Detection in Norway Spruce Forests Based on Airborne Hyperspectral and ALS Data. Remote Sensing, 14:1892. Search in Google Scholar

Degerickx, J., Roberts, D., Mcfadden, J., Hermy, M., Somers, B., 2018: Urban tree health assessment using airborne hyperspectral and LiDAR imagery. International Journal of Applied Earth Observation and Geoinformation, 73:26–38. Search in Google Scholar

Descals, A., Verger, A., Yin, G., Filella, I., Penuelas, J., 2022: Widespread drought-induced leaf shedding and legacy effects on productivity in European deciduous forests. Remote Sensing in Ecology and Conservation, 9:76–89. Search in Google Scholar

Dimitrov, S., Georgiev, G., Georgieva, M., Gluschkova, M., Chepisheva, V., Mirchev, P. et al., 2018: Integrated assessment of urban green infrastructure condition in Karlovo urban area by in-situ observations and remote sensing. One Ecosystem, 3:e21610. Search in Google Scholar

D’Odorico, P., Schönbeck, L., Vitali, V., Meusburger, K., Schaub, M., Ginzler, C. et al., 2021: Drone-based physiological index reveals long-term acclimation and drought stress responses in trees. Plant, Cell & Environment, 44:3552–3570. Search in Google Scholar

Dotzler, S., Hill, J., Buddenbaum, H., Stoffels, J., 2015: The Potential of EnMAP and Sentinel-2 Data for Detecting Drought Stress Phenomena in Deciduous Forest Communities. Remote Sensing, 7:14227–14258. Search in Google Scholar

Duarte, A., Acevedo Muñoz, L., Gonçalves, C., Mota, L., Sarmento, A., Silva, M. et al., 2020: Detection of Longhorned Borer Attack and Assessment in Eucalyptus Plantations Using UAV Imagery. Remote Sensing, 12:3153. Search in Google Scholar

Einzmann, K., Atzberger, C., Pinnel, N., Glas, C., Böck, S., Seitz, R. et al., 2021: Early detection of spruce vitality loss with hyperspectral data: Results of an experimental study in Bavaria, Germany. Remote Sensing of Environment, 266:112676. Search in Google Scholar

El-Ghany, N., Abd El-Aziz, S., Marei, S., 2020: A review: application of remote sensing as a promising strategy for insect pests and diseases management. Environmental Science and Pollution Research, 27:33503–33515. Search in Google Scholar

Fassnacht, F. E., Latifi, H., Stereńczak, K., Modzelewska, A., Lefsky, M., Waser, L. T. et al., 2016: Review of studies on tree species classification from remotely sensed data. Remote Sensing of Environment. 186: 64–87. Search in Google Scholar

Fernandez-Carrillo, A., Patočka, Z., Dobrovolný, L., Franco-Nieto, A., Revilla-Romero, B., 2020: Monitoring Bark Beetle Forest Damage in Central Europe. A Remote Sensing Approach Validated with Field Data. Remote Sensing, 12:3634. Search in Google Scholar

García-Montero, L., Pascual, C., Martin-Fernández, S., Sanchez-Paus Diaz, A., Patriarca, C., Martín-Ortega, P. et al., 2021: Medium- (MR) and Very-High-Resolution (VHR) Image Integration through Collect Earth for Monitoring Forests and Land-Use Changes: Global Forest Survey (GFS) in the Temperate FAO Ecozone in Europe (2000–2015). Remote Sensing, 13:4344. Search in Google Scholar

Georgiev, G., Georgieva, M., Dimitrov, S., Iliev, M., Trenkin, V., Mirchev, P. et al., 2022: Remote Sensing Assessment of the Expansion of Ips typographus Attacks in the Chuprene Reserve,Western Balkan Range. Forests, 13:39. Search in Google Scholar

Georgieva, M., Belilov, S., Dimitrov, S., Iliev, M., Trenkin, V., Mirchev, P. et al., 2022: Application of Remote Sensing Data for Assessment of Bark Beetle Attacks in Pine Plantations in Kirkovo Region, the Eastern Rhodopes. Forests, 13:620. Search in Google Scholar

Guanter, L., Kaufmann, H., Segl, K., Foerster, S., Rogaß, C., Chabrillat, S. et al., 2015: The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation. Remote Sensing, 7:8830. Search in Google Scholar

Guillen-Climent, M. L., Mas, H., Fernández-Landa, A., Algeet-Abarquero, N., Tomé J. L., 2020: Using hipersepctral images for decay detection in Pinus halepensis (Mill.) in the Mediterranean forest. Revista de Teledetección, 55:59–69. Search in Google Scholar

Guerra, J., Díaz Varela, R., Ávarez-González, J., Rodríguez-González, P., 2021: Assessing a novel modelling approach with high resolution UAV imagery for monitoring health status in priority riparian forests. Forest Ecosystems, 8:61. Search in Google Scholar

Hansen, M. C., Potapov, P., Moore, R., Hancher, M., Turubanova, S., Tyukavina, A. et al., 2013: High-Resolution Global Maps of 21st-Century Forest Cover Change. Science, 342:850–853. Search in Google Scholar

Hawryło, P., Bednarz, B., Wezyk, P., Szostak, M., 2018: Estimating defoliation of Scots pine stands using machine learning methods and vegetation indices of Sentinel-2. European Journal of Remote Sensing, 51:194–204. Search in Google Scholar

Hernandez Clemente, R., North, P. R. J., Hornero, A., Zarco-Tejada, P., 2018: Monitoring Forest Health with Sun-Induced Chlorophyll Fluorescence Observations and 3-D Radiative Transfer Modeling. IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, p. 5999–6002. Search in Google Scholar

Ho, B., Kocer, B. B., Kovac, M., 2022: Vision based crown loss estimation for individual trees with remote aerial robots. ISPRS J. Photogramm. Remote Sensing, 188:75–88. Search in Google Scholar

Holzwarth, S., Thonfeld, F., Abdullahi, S., Asam, S., Canova, E., Gessner, U. et al., 2020: Earth Observation Based Monitoring of Forests in Germany: A Review. Remote Sensing, 12:3570. Search in Google Scholar

Hornero, A., Hernández-Clemente, R., North, P., Beck, P., Boscia, D., Navas-Cortés, J. A. et al., 2020: Monitoring the incidence of Xylella fastidiosa infection in olive orchards using ground-based evaluations, airborne imaging spectroscopy and Sentinel-2 time series through 3-D radiative transfer modelling. Remote Sensing of Environment, 236:111480. Search in Google Scholar

Hornero, A., Zarco-Tejada, P., Quero, J., North, P. R. J., Francisco José, R.-G., Sánchez-Cuesta, R. et al., 2021: Modelling hyperspectral- and thermal-based plant traits for the early detection of Phytophthora-induced symptoms in oak decline. Remote Sensing of Environment, 263:112570. Search in Google Scholar

Hu, Y., Yang, C., Yang, J., Li, Y., Jing, W., Shu, S., 2021: Review on unmanned aerial vehicle remote sensing and its application in coastal ecological environment monitoring. IOP Conference Series: Earth and Environmental Science, 821:012018. Search in Google Scholar

Huo, L., Lindberg, E., Persson, H., 2020: Normalized Projected Red & SWIR (NPRS): A New Vegetation Index for Forest Health Estimation and Its Application on Spruce Bark Beetle Attack Detection. IGARSS 2020–2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, p. 4618–4621. Search in Google Scholar

Huo, L., Persson, H., Lindberg, E., 2021. Early detection of forest stress from European spruce bark beetle attack, and a new vegetation index: Normalized distance red & SWIR (NDRS). Remote Sensing of Environment, 255:112240. Search in Google Scholar

Jafarbiglu, H., Pourreza, A., 2022: A comprehensive review of remote sensing platforms, sensors, and applications in nut crops. Computers and Electronics in Agriculture, 197:106844. Search in Google Scholar

Junttila, S., Näsi, R., Koivumäki, N., Imangholiloo, M., Saarinen, N., Raisio, J. et al., 2022: Multispectral Imagery Provides Benefits for Mapping Spruce Tree Decline Due to Bark Beetle Infestation When Acquired Late in the Season. Remote Sensing, 14:909. Search in Google Scholar

Kälin, U., Lang, N., Hug, C., Gessler, A., Wegner, J., 2019: Defoliation estimation of forest trees from ground-level images. Remote Sensing of Environment, 223:143–153. Search in Google Scholar

Kamińska, A., Lisiewicz, M., Stereńczak, K., Kraszewski, B., Sadkowski, R., 2018: Species-related single dead tree detection using multi-temporal ALS data and CIR imagery. Remote Sensing of Environment, 219:31–43. Search in Google Scholar

Kamińska, A., 2023: Spatial autocorrelation based on remote sensing data in monitoring of Norway spruce dieback caused by the European spruce bark beetle Ips typographus L. in the Białowieża Forest. Sylwan, 166:719–732. Search in Google Scholar

Kampen, M., Lederbauer, S., Mund, J.-P., Immitzer, M., 2019: UAV-Based Multispectral Data for Tree Species Classification and Tree Vitality Analysis. Conference: Dreiländertagung der DGPF, der OVG und der SGPF in Wien, Österreich – Publikation der DGPF, Band 28, 2019, p. 623–639. Search in Google Scholar

Kanerva, H., Honkavaara, E., Näsi, R., Hakala, T., Junttila, S., Karila, K. et al., 2022: Estimating Tree Health Decline Caused by Ips typographus L. from UAS RGB Images Using a Deep One-Stage Object Detection Neural Network. Remote Sensing, 14:6257. Search in Google Scholar

Katkovsky, L., Beliaev, B., Siliuk, V., Beliaev, M., Sarmin, E., Davidovich, Y., 2020: Remote spectral methods for detecting stress coniferous. E3S Web of Conferences, 223:02004. Search in Google Scholar

Khoury, S., Coomes, D. A., 2020: Resilience of Spanish forests to recent droughts and climate change. Global Change Biology, 26:7079–7098. Search in Google Scholar

Klouček, T., Komarek, J., Surovy, P., Hrach, K., Janata, P., Vašíček, B., 2019: The Use of UAV Mounted Sensors for Precise Detection of Bark Beetle Infestation. Remote Sensing, 11:1561. Search in Google Scholar

Kotlarz, J., Kubiak, K., Spiralski, M., 2022: Monitoring Effects of Drought on Nitrogen and Phosphorus in Temperate Oak Forests Using Machine Learning Techniques. Polish Journal of Environmental Studies, 31:1137–1151. Search in Google Scholar

Lambert, J., Denux, J.-P., Verbesselt, J., Balent, G., Cheret, V., 2015: Detecting clear-cuts and decreases in forest vitality using MODIS NDVI time series. Remote Sensing, 7:3588–3612. Search in Google Scholar

Laštovička, J., Švec, P., Paluba, D., Kobliuk, N., Svoboda, J. et al., 2020: Sentinel-2 Data in an Evaluation of the Impact of the Disturbances on Forest Vegetation. Remote Sensing, 12:1914. Search in Google Scholar

Lausch, A., Erasmi, S., King, D. J., Magdon, P., Heurich, M., 2016: Understanding Forest Health with Remote Sensing -Part I – A Review of Spectral Traits, Processes and Remote-Sensing Characteristics. Remote Sensing, 8:1029. Search in Google Scholar

Lausch, A., Erasmi, S., King, D. J., Magdon, P., Heurich, M., 2017: Understanding Forest Health with Remote Sensing-Part II – A Review of Approaches and Data Models. Remote Sensing, 9:129. Search in Google Scholar

Lausch, A., Borg, E., Bumberger, J., Dietrich, P., Heu-rich, M., Huth, A. et al., 2018: Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches. Remote Sensing, 10:1120. Search in Google Scholar

Lillesand, T., Kiefer, R. W., Chipman, J., 2015: Remote Sensing and Image Interpretation, 7th Edition. Hoboken, John Wiley & Sons, 736 p. Search in Google Scholar

Liu, X., Frey, J., Denter, M., Zielewska-Büttner, K., Still, N., Koch, B., 2021: Mapping standing dead trees in temperate montane forests using a pixel- and object-based image fusion method and stereo WorldView-3 imagery. Ecological Indicators, 133:108438. Search in Google Scholar

Liu, X., Neigh, C. S. R., Pardini, M., Forkel, M., 2024: Estimating forest height and above-ground biomass in tropical forests using P-band TomoSAR and GEDI observations. International Journal of Remote Sensing, 45:3129–3148. Search in Google Scholar

Lukeš, P., 2021: Monitoring of Bark Beetle Forest Damages. In: Södergård, C., Mildorf, T., Habyarimana, E., Berre, A. J., Fernandes, J. A., Zinke-Wehlmann, C. (eds): Big Data in Bioeconomy. Cham, Springer, pp. 351–361. Search in Google Scholar

Maltezos, E., Grammalidis, N., Katagis, T., Gitas, I. Z., Charalampopoulou, V. (Betty), 2019: Development of automated workflows (spatial models) for forest monitoring with the use of time-series of multispectral optical and SAR data. In: Papadavid, G., Themistocleous, K., Michaelides, S., Ambrosia, V., Hadjimitsis, D. G. (eds.): Seventh International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2019). Presented at the Seventh International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2019), SPIE, Paphos, Cyprus, p. 60. Search in Google Scholar

Marx, A., Tetteh, G. O., 2017: A Forest Vitality and Change Monitoring Tool Based on RapidEye Imagery. IEEE Geoscience and Remote Sensing Letters, 14:801–805. Search in Google Scholar

Meyer, B. F., Buras, A., Rammig, A., Zang, C. S., 2020: Higher susceptibility of beech to drought in comparison to oak. Dendrochronologia, 64:125780. Search in Google Scholar

Migas-Mazur, R., Kycko, M., Zwijacz-Kozica, T., Zagajewski, B., 2021: Assessment of Sentinel-2 Images, Support Vector Machines and Change Detection Algorithms for Bark Beetle Outbreaks Mapping in the Tatra Mountains. Remote Sensing, 13:3314. Search in Google Scholar

Minařík, R., Langhammer, J., 2016: Use of a Multispectral UAV Photogrammetry for Detection and Tracking of Forest Disturbance Dynamics. ISPRS – Int. Arch. Photogramm. Remote Sensing and Spatial Information Sciences, XLI-B8:711–718 Search in Google Scholar

Moher, D., Liberati, A., Tetzlaff, J., Altman, D., The PRISMA Group, 2009: Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. Journal of Clinical Epidemiology, 62:1006–1012. Search in Google Scholar

Moreno-Fernández, D., Viana-Soto, A., Camarero, J., Zavala, M., Tijerin-Triviño, J., García, M., 2021: Using spectral indices as early warning signals of forest dieback: The case of drought-prone Pinus pinaster forests. Science of The Total Environment, 793:148578. Search in Google Scholar

Moreno-Fernández, D., Camarero, J., García, M., Lines, E., Sánchez-Dávila, J., Tijerin-Triviño, J. et al., 2022: The Interplay of the Tree and Stand-Level Processes Mediate Drought-Induced Forest Dieback: Evidence from Complementary Remote Sensing and Tree-Ring Approaches. Ecosystems, 25:1738–1753. Search in Google Scholar

Näsi, R., Honkavaara, E., Blomqvist, M., Paivi, L.-S., Hakala, T., Viljanen, N. et al., 2018: Remote sensing of bark beetle damage in urban forests at individual tree level using a novel hyperspectral camera from UAV and aircraft. Urban Forestry & Urban Greening, 30:72–83. Search in Google Scholar

Navarro Cerrillo, R., Varo, M., Acosta, C., Palacios, G., Sánchez-Cuesta, R., Francisco José, R.-G., 2019: 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. Forest Ecology and Management, 451:117564. Search in Google Scholar

Navrozidis, I., Alexandridis, T., Moshou, D., Haugommard, A., Lagopodi, A., 2022: Implementing Sentinel-2 Data and Machine Learning to Detect Plant Stress in Olive Groves. Remote Sensing, 14:5947. Search in Google Scholar

Navrozidis, I., Mourelatos, S., Nieto, F., Alexandridis, T., Moshou, D., Pantazi, X. et al., 2019: Olive Trees Stress Detection Using Sentinel-2 Images. IGARSS 2019 – 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, pp. 7220–7223. Search in Google Scholar

Nowakowska, J., Hsiang, T., Patynek, P., Stereńczak, K., Olejarski, I., Oszako, T., 2020: Health Assessment and Genetic Structure of Monumental Norway Spruce Trees during A Bark Beetle (Ips typographus L.) Outbreak in the Białowieża Forest District, Poland. Forests, 11:647. Search in Google Scholar

Ogaya, R., Barbeta, A., Başnou, C., Penuelas, J., 2015: Satellite data as indicators of tree biomass growth and forest dieback in a Mediterranean holm oak forest. Annals of Forest Science, 72:135–144. Search in Google Scholar

Ogaya, R., Daijun, L., Barbeta, A., Penuelas, J., 2020: Stem Mortality and Forest Dieback in a 20-Years Experimental Drought in a Mediterranean Holm Oak Forest. Frontiers in Forests and Global Change, 2:89. Search in Google Scholar

Pardini, M., Armston, J., Qi, W., Lee, S. K., Tello, M., Cazcarra Bes, V. et al., 2019: Early Lessons on Combining Lidar and Multi-baseline SAR Measurements for Forest Structure Characterization. Surveys in Geophysics, 40:803–837. Search in Google Scholar

Pérez-Romero, J., Navarro-Cerrillo, R. M., Palacios-Rodriguez, G., Acosta, C., Mesas-Carrascosa, F. J., 2019: 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. Remote Sensing, 11:1736. Search in Google Scholar

Peters, R., Miranda, J. C., Schönbeck, L., Nievergelt, D., Fonti, M., Saurer, M. et al., 2020: Tree physiological monitoring of the 2018 larch budmoth outbreak: preference for leaf recovery and carbon storage over stem wood formation in Larix decidua. Tree Physiology, 40:1697–1711. Search in Google Scholar

Piedallu, C., Dallery, D., Bresson, C., Legay, M., Gégout, J.-C., Pierrat, R., 2022: Spatial vulnerability assessment of silver fir and Norway spruce dieback driven by climate warming. Landscape Ecology, 38:341–361. Search in Google Scholar

Piragnolo, M., Pirotti, F., Zanrosso, C., Lingua, E., Grigolato, S., 2021: Responding to Large-Scale Forest Damage in an Alpine Environment with Remote Sensing, Machine Learning, and Web-GIS. Remote Sensing, 13:1541. Search in Google Scholar

Pirotti, F. 2011: Analysis of full-waveform LiDAR data for forestry applications: a review of investigations and methods. iForest – Biogeosciences and Forestry, 4:100–106. Search in Google Scholar

Poblete, T., Navas Cortés, J., Camino, C., Calderón Madrid, R., Hornero, A., Gonzalez-dugo, V. et al., 2021: Discriminating Xylella fastidiosa from Verticillium dahliae infections in olive trees using thermal- and hyperspectral-based plant traits. ISPRS Journal of Photogrammetry and Remote Sensing, 179:133–144. Search in Google Scholar

Prăvălie, R., Sirodoev, I., Nita, I.-A., Patriche, C., Dumitraşcu, M., Roşca, B. et al., 2022: NDVI-based ecological dynamics of forest vegetation and its relationship to climate change in Romania during 1987–2018. Ecological Indicators, 136:108629. Search in Google Scholar

Puletti, N., Mattioli, W., Bussotti, F., Pollastrini, M., 2019: Monitoring the effects of extreme drought events on forest health by Sentinel-2 imagery. Journal of Applied Remote Sensing, 13:1. Search in Google Scholar

Rast, M., Nieke, J., Adams, J., Isola, C., Gascon, F., 2021: Copernicus Hyperspectral Imaging Mission for the Environment (Chime). EEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, pp. 108–111. Search in Google Scholar

Recanatesi, F., Giuliani, C., Ripa, M., 2018: 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. Sustainability, 10:3308. Search in Google Scholar

Recanatesi, F., Giuliani, C., Rossi, C., Ripa, M., 2019: A Remote Sensing-Assisted Risk Rating Study to Monitor Pinewood Forest Decline: The Study Case of the Castelporziano State Nature Reserve (Rome). In: Calabrò, F., Della Spina, L., Bevilacqua, C. (eds): New Metropolitan Perspectives. ISHT 2018. Smart Innovation, Systems and Technologies, vol 100. Cham, Springer, pp. 68–75. Search in Google Scholar

Reiche, J., Hamunyela, E., Verbesselt, J., Hoekman, D. H., Herold, M., 2018: Improving near-real time deforestation monitoring in tropical dry forests by combining dense Sentinel-1 time series with Landsat and ALOS-2 PALSAR-2. Remote Sensing of Environment, 204:147–161. Search in Google Scholar

Rodes, M., Torres, P., García, M., 2021: Assessing tree decay in an urban park using PlanetScope images: the case of Cerro Almodovar Park. Proc. SPIE 11864, Remote Sensing Technologies and Applications in Urban Environments VI:118640L. Search in Google Scholar

Romero-Ramirez, F., Navarro-Cerrillo, R., Varo, M., Quero, J., Doerr, S., Hernandez Clemente, R., 2018: Determination of forest fuels characteristics in mortality-affected Pinus forests using integrated hyperspectral and ALS data. International Journal of Applied Earth Observation and Geoinformation, 68:157–167. Search in Google Scholar

Rullán, C., Olthoff, A., Pando, V., Pajares, J., Delgado, J., 2015: Remote monitoring of defoliation by the beech leaf-mining weevil Rhynchaenus fagi in northern Spain. Forest Ecology and Management, 347:200–208. Search in Google Scholar

Safonova, A., Hamad, Y., Dmitriev, E., Georgiev, G., Trenkin, V., Georgieva, M. et al., 2021: Individual Tree Crown Delineation for the Species Classification and Assessment of Vital Status of Forest Stands from UAV Images. Drones, 5:77. Search in Google Scholar

Santoro, M., Cartus, O., Wegmüller, U., Besnard, S., Carvalhais, N., Araza, A. et al., 2022: Global estimation of above-ground biomass from spaceborne C-band scatterometer observations aided by LiDAR metrics of vegetation structure. Remote Sensing of Environment, 279:113114. Search in Google Scholar

Schratz, P., Muenchow, J., Iturritxa, E., Cortés, J., Bischl, B., Brenning, A., 2021: Monitoring Forest Health Using Hyperspectral Imagery: Does Feature Selection Improve the Performance of Machine-Learning Techniques? Remote Sensing, 13:4832. Search in Google Scholar

Senf, C., Seidl, R., Poulter, B., 2021: Post-disturbance canopy recovery and the resilience of Europe’s forests. Global Ecology and Biogeography, 31:822–825. Search in Google Scholar

Smigaj, M., Gaulton, R., Barr, S., Suarez Minguez, J., 2015: UAV-Borne Thermal Imaging for Forest Health Monitoring: Detection of Disease-Induced Canopy Temperature Increase. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-3/W3:349–354. Search in Google Scholar

Smigaj, M., Gaulton, R., Suárez, J. C., Barr, S. L., 2019: Canopy temperature from an Unmanned Aerial Vehicle as an indicator of tree stress associated with red band needle blight severity. Forest Ecology and Management, 433:699–708. Search in Google Scholar

Solano, F., Di Fazio, S., Modica, G., 2019: A methodology based on GEOBIA and WorldView-3 imagery to derive vegetation indices at tree crown detail in olive orchards. International Journal of Applied Earth Observation and Geoinformation, 83:101912. Search in Google Scholar

Stereńczak, K., Bartłomiej, K., Miłosz, M., Żaneta, P., 2017: Inventory of standing dead trees in the surroundings of communication routes – The contribution of remote sensing to potential risk assessment. Forest Ecology and Management, 402, 76–91. Search in Google Scholar

Stereńczak, K., Mielcarek, M., Modzelewska, A., Kraszewski, B., Fassnacht, F., Hilszczański, J., 2019: Intra-annual Ips typographus outbreak monitoring using a multi-temporal GIS analysis based on hyperspectral and ALS data in the Białowieża Forests. Forest Ecology and Management, 442:105–116. Search in Google Scholar

Stereńczak, K., Mielcarek, M., Kamińska, A., Kraszewski, B., Piasecka, Ż., Miścicki, S. et al., 2020: Influence of selected habitat and stand factors on bark beetle Ips typographus (L.) outbreak in the Białowieża Forest. Forest Ecology and Management, 459:117826. Search in Google Scholar

Sturm, J., Santos, M., Schmid, B., Damm, A., 2022: Satellite data reveal differential responses of Swiss forests to unprecedented 2018 drought. Global Change Biology, 28:2956–2978. Search in Google Scholar

Tilly, N., Reddig, F., Lussem, U., Bareth, G., 2020: First investigation of mediterranean oak tree vitality with high-resolution WorldView-3 data: Comparing ten vegetation indices and three machine learning classifiers. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B3-2020:1069–1076. Search in Google Scholar

Thonfeld, F., Gessner, U., Holzwarth, S., Kriese, J., da Ponte, E., Huth, J. et al., 2022: A First Assessment of Canopy Cover Loss in Germany’s Forests after the 2018–2020 Drought Years. Remote Sensing, 14:562. Search in Google Scholar

Torres, P., Rodes-Blanco, M., Viana-Soto, A., Nieto, H., García, M., 2021: The Role of Remote Sensing for the Assessment and Monitoring of Forest Health: A Systematic Evidence Synthesis. Forests, 12:1134. Trumbore, S., Brando, P., Hartmann, H., 2015: Forest health and global change. Science, 349:814–818. Search in Google Scholar

Trujillo-Toro, J., Navarro Cerrillo, R., 2019: Analysis of Site-dependent Pinus halepensis Mill. Defoliation Caused by ‘Candidatus Phytoplasma pini’ through Shape Selection in Landsat Time Series. Remote Sensing, 11:1868. Search in Google Scholar

Varo, M., Navarro Cerrillo, R., 2021: Stand Delineation of Pinus sylvestris L. Plantations Suffering Decline Processes Based on Biophysical Tree Crown Variables: A Necessary Tool for Adaptive Silviculture. Remote Sensing, 13:436. Search in Google Scholar

Walshe, D., McInerney, D., Van De Kerchove, R., Goyens, C., Balaji, P., Byrne, K., 2019: Detecting nutrient deficiency in spruce forests using multispectral satellite imagery. International Journal of Applied Earth Observation and Geoinformation, 86:101975. Search in Google Scholar

Wellbrock, N., Bolte, A., 2019: Status and Dynamics of Forests in Germany: Results of the National Forest Monitoring. Cham, Springer, 384 p. Search in Google Scholar

Wu, D., Johansen, K., Phinn, S., Robson, A., Tu, Y.-H., 2020: Inter-comparison of remote sensing platforms for height estimation of mango and avocado tree crowns. International Journal of Applied Earth Observation and Geoinformation, 89:102091. Search in Google Scholar

Wulder, M., White, J., Nelson, R., Næsset, E., Ørka, H., Coops, N. et al., 2012: LiDAR sampling for large-area forest characterization: a review. Remote Sensing of Environment, 121:196–209. Search in Google Scholar

Wulder, M. A., Loveland, T. R., Roy, D. P., Crawford, C. J., Masek, J. G., Woodcock, C. E. et al., 2019: Current status of Landsat program, science, and applications. Remote Sensing of Environment, 225:127–147. Search in Google Scholar

Žabota, B., Kobal, M., 2022: The Use of UAV-Acquired Multiband Images for Detecting Rockfall-Induced Injuries at Tree Crown Level. Forests, 13:1039. Search in Google Scholar

Zagoranski, F., Pernar, R., Seletković, A., Ančić, M., Kolić, J., 2018: Monitoring the Health Status of Trees in Maksimir Forest Park Using Remote Sensing Methods. South-east European forestry, 9:81–87. Search in Google Scholar

Zarco-Tejada, P., Hornero, A., Beck, P., Kattenborn, T., Kempeneers, P., Hernandez Clemente, R., 2019: Chlorophyll content estimation in an open-canopy conifer forest with Sentinel-2A and hyperspectral imagery in the context of forest decline. Remote Sensing of Environment, 223:320–335. Search in Google Scholar

Bayerische Landesanstalt für Wald und Forstwirtschaft, 2020. Buchdrucker und Kupferstecher an Fichte. LWF Merkbl. (In German). Search in Google Scholar

BMEL – Bundesministerium für Ernährung und Land-wirtschaft, 2022. Ergebnisse der Waldzustandserhebung 2021 [WWW Document]. BMEL. Available at https://www.bmel.de/DE/themen/wald/wald-indeutschland/waldzustandserhebung.html (accessed 2.16.23). (In German). Search in Google Scholar

Drechsel, J., 2022. Waldzustandsbericht der Landeshauptstadt Hannover. (In German). Search in Google Scholar

Eichhorn, J., Roskams, P., Potočić, N., Timmermann, V., Ferretti, M., Mues, V. et al., 2020: Part IV: Visual Assessment of Crown Condition and Damaging Agents. In: UNECE ICP Forests Programme Coordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Eberswalde, Germany, Thünen Institute of Forest Ecosystems, 49 p. + Annex Search in Google Scholar

European Commission, 2001: Directorate-General for Research and Innovation, EUR 19435 Satellite Based Environmental Monitoring of European Forests, Publications Office. Search in Google Scholar

Food and Agriculture Organization (FAO) of United Nations, 2020. Country Reports | Global Forest Resources Assessments | Food and Agriculture Organization of the United Nations [WWW Document]. Available at https://www.fao.org/forest-resources-assessment/fra-2020/country-reports/en/ (accessed 2.18.23). Search in Google Scholar

Food and Agriculture Organization (FAO) of United Nations, 2021. Available at https://www.fao.org/forestry-fao/pests/99464/en/ last updated: Friday, November 19, 2021. Search in Google Scholar

Food and Agriculture Organization (FAO) of United Nations, 2024. Available at https://www.fao.org/forestry-fao/pests/99464/en/ last updated: Sunday, June 30, 2024. Search in Google Scholar

Henkel, A., Hese, S., Thiel, C., 2022: Erhöhte Buchenmortalität im Nationalpark Hainich? AFZ – derWald. (In German). Search in Google Scholar

Michel, A., Kirchner, T., Prescher, A.-K., Schwärzel, K., 2022: Forest Condition in Europe: The 2022 Assessment. ICP Forests Technical Report under the UNECE Convention on Long-range Transboundary Air Pollution (Air Convention). Braunschweig, Thünen-Institut, Bundesforschungsinstitut für Ländliche Räume, Wald und Fischerei. Search in Google Scholar

Wellbrock, N., Eickenscheidt, N., Hilbrig, L., Dühnelt, P., Holzhausen, M., Bauer, A. et al., 2020: Thünen Working Paper 84: Leitfaden und Dokumentation zur Waldzustandserhebung in Deutschland. Braun-schweig, Thünen-Institut für Waldökosysteme, 97 p. (In German). Search in Google Scholar

Language:
English
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Journal Subjects:
Life Sciences, Plant Science, Ecology, Life Sciences, other