UAV-Based Forest Health Monitoring: A Systematic Review
Abstract
:1. Introduction
1.1. UAV Remote Sensing: An Emerging Tool in Forest Health Monitoring
1.2. UAV Types and Sensors for Forest Health Monitoring
1.3. The Importance of Forest Health
1.4. Objectives, Limitations, and Review Structure
2. Materials and Methods
2.1. Literature Search, Filtering, and Information Extraction
2.2. Dataset Structuring
- Technical and Strategic InformationTechnical details and strategic approaches common to all papers were subdivided into data acquisition, data processing, complementary data, and data analysis techniques with a focus on machine learning algorithms for image classification.
- Investigated Tree Species and Biotic and Abiotic StressorsInformation on investigated tree species and biotic and abiotic stress agents were summarized. Based on these details, the peer-reviewed papers were categorized into three main topics: (1) biotic stressors, (2) abiotic stressors, and (3) unknown/stress in general. Details on biotic stressors were further split into insects, diseases, and phytoparasites for clarity. Abiotic stressors were organized together due to the limited data. If the causal target stressors were unknown or not specified, they were grouped as unknown/stress in general. Papers in which the authors identified multiple stressors were listed in their respective categories or subcategories for completeness.
3. Results
3.1. Technical and Strategic Information Extraction
3.1.1. Data Acquisition: UAV Types, Sensors, Flight Planning, and Monitoring Strategies
3.1.2. Data Processing: Data Products, Radiometric/Spectral Calibration, and Georeferencing
- Feature detection and matching of the corresponding key points in overlapping imagery;
- Performing bundle adjustments to estimate intrinsic (e.g., focal length and lens distortion) and extrinsic (pose) parameters of the camera to compute a sparse 3D point cloud;
- Multiview stereo matching to generate a dense point cloud of the scene. Through interpolation of the point cloud, rasterized spectral (orthomosaic) and structural data (DSM, DTM) can be generated for cell-wise statistical analysis.
3.1.3. Complementary Data: Fieldwork and Traditional Remote Sensing Platforms
3.1.4. Data Analysis: Image Segmentation and Machine Learning Techniques for Image Classification
3.2. Tree Species and Stress Agents
3.2.1. Biotic Stressors
- (a)
- Pests
- (b)
- Diseases
- (c)
- Phytoparasites
3.2.2. Abiotic
3.2.3. Stress in General or Unknown Cause
4. Discussion
4.1. Data Acquisition
4.2. Data Processing
4.3. Complementary Data Use
4.4. Data Analysis
4.5. Tree Species and Stressors
5. Conclusions
- (1)
- The use of hyperspectral cameras and LiDAR sensors must increase to exploit modern sensors’ spectral and structural potential. Both sensor types are underrepresented but provide the most promising added value for FHM, in particular if used in combination. Optimized multispectral sensors for FHM need to be developed, which offer the most informative spectral bands tailored to the requirements of the analysis of forests. The miniaturization of multispectral LiDAR must be advanced for UAV applications.
- (2)
- The flexible use of drones enables recordings with short revisit intervals and thus provides a high possible temporal resolution. Despite these advantageous attributes of UAVs, multitemporal and long-term monitoring is not sufficiently performed yet. The dynamic nature of forest ecosystems requires timely and repeated data collection to capture forest health degradation and recovery.
- (3)
- A greater emphasis should be placed on early stress detection (previsual stage) to improve forest management strategies and to be able to respond to impending damage promptly.
- (4)
- Currently, a fast advance of operationalization of UAV-based FHM is mainly hampered by technical and regulatory issues. A lack of standardized radiometric and spectral calibration workflows persists and affects consistent data processing. This also involves the flexible use of drones, as flight operation is limited to stable atmospheric conditions when a detailed spectral analysis is intended. Regulatory frameworks need to be optimized in mutual consideration of stakeholders and public interests to keep drone projects operable and safe. Exemption permits should be made possible at a reasonable additional expense to achieve greater area coverage (e.g., through flights above the maximum allowed altitude or BVLOS flights) and should not pose a barrier to innovation.
- (5)
- Researchers should intensely focus on integrating traditional RS data from satellites and aircraft to enable larger-scale forest monitoring. It has been shown that there is a significant weakness in interoperability that needs to be addressed in future work to exploit complementary data with strong synergies (e.g., data fusion). Future research should focus on multisensor and multisource monitoring strategies and tackle interoperability issues by establishing standardized data infrastructures.
- (6)
- Researchers rely on commercial software across the whole data pipeline, from data acquisition to the final analysis. This dependency comes at the expense of more flexible open-source solutions and can adversely affect knowledge and technology transfer among stakeholders.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
ALS | airborne laser scanning |
ANN | artificial neural network |
BVLOS | beyond visual line of sight |
CHM | canopy height model |
CNN | convolutional neural network |
D-GNSS | differential global navigation satellite system |
DBH | diameter at breast height |
DSM | digital surface model |
DTM | digital terrain model |
ESA | European Space Agency |
EU | European Union |
k-NN | k-nearest neighbor |
FHM | forest health monitoring |
GCP | ground control point |
GNSS | global navigation satellite system |
LiDAR | light detection and ranging |
MLC | maximum likelihood classifier |
MTOM | maximum take-off mass |
NDVI | normalized difference vegetation index |
NDRE | normalized difference red-edge index |
NIR | near-infrared |
nm | nanometer |
OBIA | object-based image analysis |
OA | overall accuracy |
PPK | postprocessed kinematic |
PRI | photochemical reflectance index |
RF | random forest |
RGB | red green blue |
RS | remote sensing |
RTK | real-time kinematic |
SfM | structure from motion |
SVM | support vector machine |
UAS | unmanned/uncrewed aircraft system |
UAV-RS | unmanned/uncrewed aerial vehicle remote sensing |
UN | United Nations |
UAV | unmanned/uncrewed aerial vehicle |
VLOS | visual line of sight |
VOC | volatile organic compound |
VTOL | vertical take-off and landing |
Appendix A
Authors | Study Area (Country) | UAV Type | Sensor | Geometric Correction | Tree Species Affected | Stressor |
---|---|---|---|---|---|---|
Lehmann et al. [88] | Germany | Multirotor | Multispectral | GCPs | Common oak (Quercus robur) | Oak splendor beetle (Agrilus biguttatus) |
Näsi et al. [134] | Finland | Multirotor | Hyperspectral, RGB | Coregistration | Norway spruce (Picea abies) | European spruce bark beetle (Ips typographus L.) |
Minarik and Langhammer [205] | Czech Republic | Multirotor | Multispectral | GCPs | Norway spruce (Picea abies) | European spruce bark beetle (Ips typographus L.) |
Cardil et al. [206] | Spain | Multirotor | RGB | GCPs | Pine | Pine processionary moth (Thaumetopoea pityocampa) |
Klein Hentz and Strager [114] | United States | Multirotor | RGB | GCPs | Not specified | Magicicada |
Näsi et al. [135] | Finland | Multirotor | Hyperspectral, RGB | Coregistration | Norway spruce (Picea abies) | European spruce bark beetle (Ips typographus L.) |
Otsu et al. [143] | Spain | Multirotor | RGB | Coregistration | Pine (Pinus sylvestris, P. nigra) | Pine processionary moth (Thaumetopoea pityocampa) |
Röder et al. [152] | Germany | Multirotor | RGB | GCPs | Norway spruce (Picea abies) | European spruce bark beetle (Ips typographus L.) |
Stoyanova et al. [207] | Bulgaria | Multirotor | RGB, multispectral | - | Norway spruce (Picea abies), Scots pine (Pinus sylvestris) | Bark beetle (Ips typographus, I. acuminatus, I. sexdentatus) |
Zhang et al. [162] | China | Multirotor | Hyperspectral, RGB | Coregistration | Chinese pine (Pinus tabulaeformis) | Chinese pine caterpillar (Dendrolimus tabulaeformis) |
Cardil et al. [129] | Spain | Multirotor | RGB, multispectral | - | Scots pine (Pinus sylvestris) | Pine processionary moth (Thaumetopoea pityocampa) |
Dimitrov et al. [131] | Bulgaria | Fixed-Wing | Multispectral | - | Norway spruce (Picea abies), silver fir (Abies alba) | European spruce bark beetle (Ips typographus L.) |
Klouček et al. [92] | Czech Republic | Multirotor | RGB, multispectral | GCPs | Norway spruce (Picea abies) | European spruce bark beetle (Ips typographus L.) |
Lin et al. [116] | China | Not specified | Hyperspectral, LiDAR | GCPs | Yunnan Pine (Pinus yunnanensis) | Pine shoot beetle (Tomicus spp.) |
Otsu et al. [138] | Spain | Not specified | RGB, multispectral | GCPs | Pine (Pinus sylvestris, P. nigra) | Pine processionary moth (Thaumetopoea pityocampa) |
Akıncı and Göktoǧan [125] | Turkey | Multirotor | RGB | - | Turkish pine (Pinus brutia) | Pine processionary moth (Thaumetopoea pityocampa, T. wilkinsoni) |
Safonova et al. [208] | Russia | Multirotor | RGB | - | Siberian fir (Abies sibirica) | Four-eyed fir bark beetle (Polygraphus proximus) |
Abdollahnejad and Panagiotidis [102] | Czech Republic | Multirotor | Multispectral | - | Norway spruce (Picea abies), Scots pine (Pinus sylvestris) | European spruce bark beetle (Ips typographus L.) |
Duarte et al. [133] | Portugal | Fixed-Wing | Multispectral | GCPs | Eucalyptus (Eucalyptus globulus) | Eucalyptus longhorned borers (Phoracantha semipunctata, P. recurva) |
Liu et al. [140] | China | Multirotor | Hyperspectral | Direct georeferencing (RTK/PPK), coregistration | Yunnan pine (Pinus yunnanensis) | Pine shoot beetle (Tomicus spp.) |
Minařík et al. [120] | Czech Republic | Multirotor | Multispectral | GCPs | Norway spruce (Picea abies), Scots pine (Pinus sylvestris) | European spruce bark beetle (Ips typographus L.) |
Barmpoutis et al. [209] | Greece | Multirotor | RGB | - | Pine (Pinus brutia, P. halepensis, P. pinea) | Pine shoot beetle (Tomicus piniperda) |
Zhang et al. [210] | China | Multirotor | RGB, hyperspectral | Coregistration | Chinese pine (Pinus tabulaeformis) | Chinese pine caterpillar (Dendrolimus tabulaeformis) |
Cessna et al. [96] | United States | Multirotor | RGB, multispectral | GCPs | Norway spruce (Picea abies) | Four-eyed spruce beetle (Dendroctonus rufipennis) |
Lin et al. [117] | China | Not specified | Hyperspectral, LiDAR | GCPs | Yunnan Pine (Pinus yunnanensis) | Pine shoot beetle (Tomicus spp.) |
Minařík et al. [130] | Czech Republic | Multirotor | Multispectral | Direct georeferencing (RTK/PPK) | Norway spruce (Picea abies) | European spruce bark beetle (Ips typographus L.) |
Nguyen et al. [155] | Japan | Multirotor | RGB | - | Fir (Abies mariesii) | Tortrix moth (Epinotia piceae), bark beetle (Polygraphus proximus) |
Paczkowski et al. [93] | Germany | Multirotor | Gas sensor | - | Norway spruce (Picea abies) | Bark beetle (Ips typographus, Pityogenes chalcographus) |
Safonova et al. [87] | Bulgaria | Multirotor, Fixed-Wing | RGB, multispectral | - | Norway spruce (Picea abies) | European spruce bark beetle (Ips typographus L.) |
Schaeffer et al. [110] | Mexico | Multirotor | RGB, multispectral | - | Piñon pine (Pinus cembroides) | Bark beetle (Dendroctonus mexicanus) |
Koontz et al. [22] | United States | Multirotor | RGB, multispectral | GCPs | Ponderosa pine (Pinus ponderosa) | Western pine beetle (Dendroctonus brevicomis) |
Pádua et al. [115] | Portugal | Fixed-Wing | Multispectral | GCPs | Sweet chestnut (Castanea sativa) | Oriental chestnut gall wasp (Dryocosmus kuriphilus), chestnut ink disease (Phytophthora cinnamomi), chestnut blight (Cryphonectria parasitica) |
Kampen et al. [164] | Austria | Multirotor | Multispectral | - | Norway spruce (Picea abies), European ash (Fraxinus excel-sior) | European spruce bark beetle (Ips typographus L.), ash dieback disease (Hymenoscyphus pseudoalbidus) |
Honkavaara et al. [94] | Finland | Multirotor | RGB, multispectral, thermal, hyperspectral | Direct georeferencing (RTK/PPK), GCPs | Norway spruce (Picea abies) | European spruce bark beetle (Ips typographus L.), root and butt rot (Heterobasidion annosum) |
Authors | Study Area (Country) | UAV Type | Sensor | Geometric Correction | Tree Species affected | Stressor |
---|---|---|---|---|---|---|
Park and Kim [211] | South Korea | Fixed-Wing | RGB | - | Pine | Pine wilt disease (Bursaphelenchus xylophilus) |
Smigaj et al. [136] | United Kingdom | Fixed-Wing | Thermal, multispectral | Coregistration | Pine (Pinus sylvestris, P. contorta) | Red band needle blight (Dothistroma septosporum) |
Michez et al. [89] | Belgium | Fixed-Wing | RGB, multispectral | GCPs, coregistration | Black alder (Alnus glutinosa) | Phytophthora alni |
Dash et al. [103] | New Zealand | Multirotor | Multispectral | GCPs | Monterey pine (Pinus radiata) | Herbicide |
Brovkina et al. [90] | Czech Republic | Multirotor | RGB, multispectral | GCPs | Norway spruce (Picea abies) | Honey fungus (Armillaria ostoyae) |
Dash et al. [104] | New Zealand | Multirotor | Multispectral | GCPs | Monterey pine (Pinus radiata) | Herbicide |
Ganthaler et al. [122] | Austria | Multirotor | RGB | - | Norway spruce (Picea abies) | Needle bladder rust disease (Chrysomyxa rhododendri) |
Gerard et al. [123] | United Kingdom | Multirotor | Hyperspectral | - | Common oak (Quercus robur) | Acute oak decline (AOD) |
Sandino et al. [163] | Australia | Multirotor | Hyperspectral | GCPs | Paperbark tea tree (Melaleuca quinquenervia) | Myrtle rust (Austropuccinia psidii) |
Dell et al. [159] | Indonesia | Multirotor | RGB | GCPs | Eucalyptus (Eucalyptus pellita) | Bacterial wilt (Ralstonia sp.) |
Jung and Park [212] | South Korea | Fixed-Wing | RGB, multispectral | - | Pine | Pine wilt disease (Bursaphelenchus xylophilus) |
Lee et al. [107] | South Korea | Multirotor | RGB | - | Oak (Quercus mongolica, Q. serrata, Q. dentate) | Oak wilt (Raffaelea quercus-mongolicae) |
Navarro et al. [158] | Portugal | Fixed-Wing | Multispectral | GCPs | Cork oak (Quercus suber) | Cork oak decline |
Smigaj et al. [137] | United Kingdom | Multirotor | Thermal | Coregistration | Scots pine (Pinus sylvestris) | Red band needle blight (Dothistroma septosporum) |
Deng et al. [213] | China | Fixed-Wing | RGB | - | Chinese red pine (Pinus massoniana) | Pine wilt disease (Bursaphelenchus xylophilus) |
Iordache et al. [214] | Portugal | Multirotor | RGB, multispectral, hyperspectral | Direct georeferencing (RTK/PPK), GCPs | Maritime pine (Pinus pinaster) | Pine wilt disease (Bursaphelenchus xylophilus) |
Syifa et al. [157] | South Korea | Multirotor | RGB | - | Pine (Pinus densiflora, P. thunbergii) | Pine wilt disease (Bursaphelenchus xylophilus) |
Hoshikawa and Yamamoto [215] | Japan | Multirotor | RGB, multispectral | - | Japanese black pine (Pinus thunbergii) | Pine wilt disease (Bursaphelenchus xylophilus) |
Tao et al. [216] | China | Multirotor | RGB | - | Chinese red pine (Pinus massoniana) | Pine wilt disease (Bursaphelenchus xylophilus) |
Guerra-Hernández et al. [119] | Portugal | Fixed-Wing | RGB, multispectral | GCPs | Black alder (Alnus glutinosa) | Phytophthora alni |
Li et al. [124] | China | Multirotor | RGB | - | Chinese pine (Pinus tabulaeformis) | Pine wilt disease (Bursaphelenchus xylophilus) |
Qin et al. [141] | China | Multirotor | Multispectral | Direct georeferencing (RTK/PPK) | Chinese red pine (Pinus massoniana) | Pine wilt disease (Bursaphelenchus xylophilus) |
Sun et al. [217] | China | Multirotor | RGB | - | Chinese red pine (Pinus massoniana) | Pine wilt disease (Bursaphelenchus xylophilus) |
Wu et al. [111] | China | Mulirotor | RGB | - | Chinese pine (Pinus tabulaeformis) | Pine wilt disease (Bursaphelenchus xylophilus) |
Xia et al. [44] | China | Fixed-Wing | RGB | - | Pine (Pinus thunbergii, P. densiflora) | Pine wilt disease (Bursaphelenchus xylophilus) |
Yu et al. [112] | China | Multirotor | RGB, Hyperspectral, LiDAR | GCPs | Korean pine (Pinus koraiensis) | Pine wilt disease (Bursaphelenchus xylophilus) |
Yu et al. [118] | China | Multirotor | Multispectral, RGB | Direct georeferencing (RTK/PPK) | Chinese red pine (Pinus massoniana) | Pine wilt disease (Bursaphelenchus xylophilus) |
Yu et al. [142] | China | Multirotor | Hyperspectral, LiDAR | GCPs | Chinese red pine (Pinus massoniana) | Pine wilt disease (Bursaphelenchus xylophilus) |
Yu et al. [146] | China | Multirotor | Hyperspectral | GCPs | Chinese pine (Pinus tabulaeformis) | Pine wilt disease (Bursaphelenchus xylophilus) |
Pádua et al. [109] | Portugal | Multirotor | RGB, multispectral | GCPs | Sweet chestnut (Castanea sativa) | Chestnut ink disease (Phytophthora cinnamomi), chestnut blight (Cryphonectria parasitica), nutritional deficiencies |
Pádua et al. [115] | Portugal | Fixed-Wing | Multispectral | GCPs | Sweet chestnut (Castanea sativa) | Chestnut ink disease (Phytophthora cinnamomi), chestnut blight (Cryphonectria parasitica), Oriental chestnut gall wasp (Dryocosmus kuriphilus) |
Kampen et al. [164] | Austria | Multirotor | Multispectral | - | Norway spruce (Picea abies), European ash (Fraxinus excelsior) | Ash dieback disease (Hymenoscyphus pseudoalbidus), European spruce bark beetle (Ips typographus L.) |
Honkavaara et al. [94] | Finland | Multirotor | RGB, multispectral, thermal, hyperspectral | Direct georeferencing (RTK/PPK), GCPs | Norway spruce (Picea abies) | Root and butt rot (Heterobasidion annosum), European spruce bark beetle (Ips typographus L.) |
Authors | Study Area (Country) | UAV Type | Sensor | Geometric Correction | Tree Species Affected | Stressor |
---|---|---|---|---|---|---|
Li et al. [144] | Costa Rica | Multirotor | Multispectral | GCPs | Tropical dry forest | Lianas |
Maes et al. [108] | Australia | Multirotor | RGB, thermal | GCPs | Eucalyptus (Eucalyptus fibrosa, E. moluccana) | Box mistletoe (Amyema miquelii) |
Waite et al. [145] | Malaysia | Multirotor | RGB | - | Tropical forest | Liana |
Yuan et al. [166] | Costa Rica | Multirotor | Multispectral, thermal | GCPs | Neotropical dry forest | Liana |
Miraki et al. [113] | Iran | Multirotor | RGB | GCPs | Persian ironwood (Parrotia persica) | Common mistletoe (Viscum album) |
Authors | Study Area (Country) | UAV Type | Sensor | Geometric Correction | Tree Species Affected | Stressor |
---|---|---|---|---|---|---|
Hernández-Clemente et al. [85] | Spain | Fixed-Wing | Multispectral | - | Scots pine (Pinus sylvestris) | Drought |
Fraser et al. [149] | Canada | Multirotor | RGB | - | Jack pine (Pinus banksiana), spruce (Picea glauca, P. mariana) | Fire |
Ludovisi et al. [147] | Italy | Multirotor | Thermal | GCPs | Black poplar (Populus nigra) | Drought |
McKenna et al. [101] | Australia | Fixed-Wing | RGB | GCPs | Eucalypt forest | Fire |
Buras et al. [150] | Germany | Multirotor | Multispectral | - | Scots pine (Pinus sylvestris) | Drought |
Nagai et al. [169] | Japan | Multirotor | RGB | - | Japanese cedar (Cryptomeria japonica) | Heavy snow |
Rossi et al. [91] | Argentina | Fixed-Wing | Multispectral | Direct georeferencing (GNSS) | Subtropical forest | Fire |
Arkin et al. [154] | Canada | Multirotor | RGB | GCPs | Douglas-fir (Pseudotsuga menziesii), white spruce (Picea engelmannii × Picea glauca), lodgepole pine (Pinus contorta) | Fire |
Carvajal-Ramírez et al. [99] | Spain | Multirotor | RGB, multispectral | Direct georeferencing (RTK/PPK), GCPs | Mediterranean forest | Fire |
Padua et al. [86] | Portugal | Multirotor, Fixed-Wing | RGB, mulispectral | - | Maritime pine (Pinus pinaster) | Fire |
Rossi and Becker [161] | Argentina | Fixed-Wing | RGB | - | Subtropical forest | Fire |
Shin et al. [218] | South Korea | Multirotor | Multispectral | - | Korean red pine (Pinus densiflora) | Fire |
Zhu et al. [139] | China | Multirotor | RGB, LiDAR | Direct georeferencing (RTK/PPK) | Mangrove (Kandelia obovate, Avicennia marina, Aegiceras corniculatum) | Inundation stress |
Campbell et al. [153] | United States | Multirotor | RGB | GCPs | Piñon woodland | Drought |
Pádua et al. [151] | Portugal | Fixed-Wing | RGB, multispectral | - | Maritime pine (Pinus pinaster) | Fire |
Talucci et al. [132] | Russia | Multirotor | RGB, multispectral | GCPs | Cajander larch forest (Larix cajanderi) | Fire |
Tran et al. [219] | South Korea | Multirotor | RGB | - | Not specified | Fire |
Viedma et al. [95] | Spain | Not specified | LiDAR | Direct georeferencing (RTK/PPK) | Aleppo pine (Pinus halepensis) | Fire |
Araujo et al. [97] | Panama | Not specified | RGB | - | Tropical forest | Extreme rainfall |
Cohen et al. [98] | United States | Multirotor | RGB | GCPs | Black mangrove (Avicennia germinans) | Winter freeze |
D’Odorico et al. [105] | Switzerland | Multirotor | Multispectral (modified to sense PRI) | GCPs | Scots pine (Pinus sylvestris) | Drought |
Hillman et al. [100] | Australia | Multirotor | RGB, LiDAR | Direct georeferencing (RTK/PPK), GCPs, Coregistration | Eucalyptus (Eucalyptus obliqua, E. globulus) | Fire |
Woo et al. [220] | South Korea | Multirotor | Multispectral | - | Korean red pine (Pinus densiflora), Japanese larch (Larix kaempferi) | Fire |
Pádua et al. [109] | Portugal | Multirotor | RGB, multispectral | GCPs | Sweet chestnut (Castanea sativa) | Nutritional deficiencies, chestnut ink disease (Phytophthora cinnamomi), chestnut blight (Cryphonectria parasitica) |
Koontz et al. [22] | United States | Multirotor | RGB, multispectral | GCPs | Ponderosa pine (Pinus ponderosa) | Drought, Western pine beetle (Dendroctonus brevicomis) |
Authors | Study Area (Country) | UAV Type | Sensor | Geometric Correction | Tree Species Affected | Stressor |
---|---|---|---|---|---|---|
Yuan and Hu [126] | China | - | RGB | - | Not specified | Not specified/unknown |
Saarinen et al. [34] | Finland | Single-rotor helicopter | RGB, hyperspectral | GCPs | Norway spruce (Picea abies), Scots pine (Pinus sylvestris), downy birch (Betula pendula and B. pubescens) | Not specified/unknown |
Barmpoutis et al. [121] | Greece | Multirotor | RGB | - | Fir | Not specified/unknown |
Khokthong et al. [148] | Indonesia | Multirotor | RGB | GCPs | Oil palm (Elaeis guineensis) | Not specified/unknown |
Abdalla et al. [160] | Indonesia | Multirotor | RGB | - | Acacia | Not specified/unknown |
Campos-Vargas et al. [156] | Costa Rica | Multirotor | Multispectral | GCPs | Tropical dry forest | Not specified/unknown |
Gallardo-Salazar and Pompa-García [221] | Mexico | Multirotor | Multispectral | - | Arizona pine (Pinus arizonica) | Not specified/unknown |
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Ecke, S.; Dempewolf, J.; Frey, J.; Schwaller, A.; Endres, E.; Klemmt, H.-J.; Tiede, D.; Seifert, T. UAV-Based Forest Health Monitoring: A Systematic Review. Remote Sens. 2022, 14, 3205. https://doi.org/10.3390/rs14133205
Ecke S, Dempewolf J, Frey J, Schwaller A, Endres E, Klemmt H-J, Tiede D, Seifert T. UAV-Based Forest Health Monitoring: A Systematic Review. Remote Sensing. 2022; 14(13):3205. https://doi.org/10.3390/rs14133205
Chicago/Turabian StyleEcke, Simon, Jan Dempewolf, Julian Frey, Andreas Schwaller, Ewald Endres, Hans-Joachim Klemmt, Dirk Tiede, and Thomas Seifert. 2022. "UAV-Based Forest Health Monitoring: A Systematic Review" Remote Sensing 14, no. 13: 3205. https://doi.org/10.3390/rs14133205