Assessment of the Health Status of Old Trees of Platycladus orientalis L. Using UAV Multispectral Imagery
Abstract
:1. Introduction
2. Material and Methods
2.1. Study Area
2.2. Field Measurement Data
2.3. UAV-Based Multispectral Data
2.4. Feature Extraction
2.5. Tree Health Status Modelling and Mapping of P. orientalis Health Status
3. Result
3.1. Field Health Assessment
3.2. Feature Selection
3.3. Model Comparison
3.4. Models with Crown Area and Aspect Variables
3.5. Spatial Distribution of Old Trees with Different Health Conditions
4. Discussion
4.1. Health Status and Management of Old Tree
4.2. Selecting Variables
4.3. Performance of Two Models
4.4. Adjusting Models
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Lindenmayer, D.B.; Laurance, W.F.; Franklin, J.F. Global Decline in Large Old Trees. Science 2012, 338, 1305–1306. [Google Scholar] [CrossRef]
- Lindenmayer, D.B.; Laurance, W.F. The ecology, distribution, conservation and management of large old trees. Biol. Rev. 2017, 92, 1434–1458. [Google Scholar] [CrossRef] [PubMed]
- Brovkina, O.; Cienciala, E.; Surový, P.; Janata, P. Unmanned aerial vehicles (UAV) for assessment of qualitative classification of Norway spruce in temperate forest stands. Geo-Spat. Inf. Sci. 2018, 21, 12–20. [Google Scholar] [CrossRef]
- Zhao, Z. Theory and Technology of Ancient Tree Protection; Science Publishing House: Beijing, China, 2021. [Google Scholar]
- 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. [Google Scholar] [CrossRef]
- Lausch, A.; Erasmi, S.; King, D.J.; Magdon, P.; Heurich, M. Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models. Remote Sens. 2017, 9, 129. [Google Scholar] [CrossRef]
- Torresan, C.; Berton, A.; Carotenuto, F.; Di Gennaro, S.F.; Gioli, B.; Matese, A.; Miglietta, F.; Vagnoli, C.; Zaldei, A.; Wallace, L. Forestry applications of UAVs in Europe: A review. Int. J. Remote Sens. 2017, 38, 2427–2447. [Google Scholar] [CrossRef]
- Tang, L.; Shao, G. Drone remote sensing for forestry research and practices. J. For. Res. 2015, 26, 791–797. [Google Scholar] [CrossRef]
- Minařík, R.; Langhammer, J. Use of a multisoectral UAV photogrammetry for detection and tracking of forest disturbance dynamics. ISPRS—International Archives of the Photogrammetry. Remote Sens. 2016, 41, 711–718. [Google Scholar] [CrossRef]
- Nguyen, H.T.; Lopez Caceres, M.L.; Moritake, K.; Kentsch, S.; Shu, H.; Diez, Y. Individual Sick Fir Tree (Abies mariesii) Identification in Insect Infested Forests by Means of UAV Images and Deep Learning. Remote Sens. 2021, 13, 260. [Google Scholar] [CrossRef]
- Cardil, A.; Vepakomma, U.; Brotons, L. Assessing Pine Processionary Moth Defoliation Using Unmanned Aerial Systems. Forests 2017, 8, 402. [Google Scholar] [CrossRef]
- Guimarães, N.; Pádua, L.; Marques, P.; Silva, N.; Peres, E.; Sousa, J.J. Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities. Remote Sens. 2020, 12, 1046. [Google Scholar] [CrossRef]
- Syifa, M.; Park, S.-J.; Lee, C.-W. Detection of the Pine Wilt Disease Tree Candidates for Drone Remote Sensing Using Artificial Intelligence Techniques. Engineering 2020, 6, 919–926. [Google Scholar] [CrossRef]
- Sun, Z.; Wang, Y.; Pan, L.; Xie, Y.; Zhang, B.; Liang, R.; Sun, Y. Pine wilt disease detection in high-resolution UAV images using object-oriented classification. J. For. Res. 2022, 33, 1377–1389. [Google Scholar] [CrossRef]
- Klouček, T.; Komárek, J.; Surový, P.; Hrach, K.; Janata, P.; Vašíček, B. The Use of UAV Mounted Sensors for Precise Detection of Bark Beetle Infestation. Remote Sens. 2019, 11, 1561. [Google Scholar] [CrossRef]
- Abdollahnejad, A.; Panagiotidis, D. Tree Species Classification and Health Status Assessment for a Mixed Broadleaf-Conifer Forest with UAS Multispectral Imaging. Remote Sens. 2020, 12, 3722. [Google Scholar] [CrossRef]
- Torres, P.; Rodes-Blanco, M.; Viana-Soto, A.; Nieto, H.; García, M. The Role of Remote Sensing for the Assessment and Monitoring of Forest Health: A Systematic Evidence Synthesis. Forests 2021, 12, 1134. [Google Scholar] [CrossRef]
- Genuer, R.; Poggi, J.-M. Random Forests. In Random Forests with R; Genuer, R., Poggi, J.-M., Eds.; Springer International Publishing: Cham, Switzerland, 2020; pp. 33–55. [Google Scholar]
- Noble, W.S. What is a support vector machine. Nat. Biotechnol. 2006, 24, 1565–1567. [Google Scholar] [CrossRef] [PubMed]
- Zhou, Q.; Jiang, Z.; Zhang, X.; Zhang, T.; Zhu, H.; Cui, B.; Li, Y.; Zhao, F.; Zhao, Z. Leaf anatomy and ultrastructure in senescing ancient tree, Platycladus orientalis L. (Cupressaceae). PeerJ 2019, 7, 6766. [Google Scholar] [CrossRef]
- Hao, Z.; Lin, L.; Post, C.J.; Jiang, Y.; Li, M.; Wei, N.; Yu, K.; Liu, J. Assessing tree height and density of a young forest using a consumer unmanned aerial vehicle (UAV). New For. 2021, 52, 843–862. [Google Scholar] [CrossRef]
- Wang, X.; Wang, Y.; Zhou, C.; Yin, L.; Feng, X. Urban forest monitoring based on multiple features at the single tree scale by UAV. Urban For. Urban Green. 2021, 58, 126958. [Google Scholar] [CrossRef]
- Guerra-Hernández, J.; Díaz-Varela, R.A.; Ávarez-González, J.G.; Rodríguez-González, P.M. Assessing a novel modelling approach with high resolution UAV imagery for monitoring health status in priority riparian forests. For. Ecosyst. 2021, 8, 61. [Google Scholar] [CrossRef]
- Rouse, J.W.; Haas, R.H.; Schell, J.A.; Deering, D.W. Monitoring vegetation systems in the Great Plains with ERTS. NASA Spec. Pub. 1974, 351, 309. [Google Scholar]
- Gao, B.-C. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 1996, 58, 257–266. [Google Scholar] [CrossRef]
- de Castro, A.I.; Ehsani, R.; Ploetz, R.C.; Crane, J.H.; Buchanon, S. Detection of Laurel Wilt Disease in Avocado Using Low Altitude Aerial Imaging. PLoS ONE 2015, 10, e0124642. [Google Scholar] [CrossRef] [PubMed]
- Jordan, C.F. Derivation of Leaf-Area Index from Quality of Light on the Forest Floor. Ecology 1969, 50, 663–666. [Google Scholar] [CrossRef]
- Qi, J.; Chehbouni, A.; Huete, A.R.; Kerr, Y.H.; Sorooshian, S. A modified soil adjusted vegetation index. Remote Sens. Environ. 1994, 48, 119–126. [Google Scholar] [CrossRef]
- Chen, S.F.; Goodman, J. An empirical study of smoothing techniques for language modeling. Comput. Speech Lang. 1999, 13, 359–394. [Google Scholar] [CrossRef]
- Gamon, J.A.; Peñuelas, J.; Field, C.B. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens. Environ. 1992, 41, 35–44. [Google Scholar] [CrossRef]
- Pearson, R.L.; Miller, L.D. Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie. Proc. Eighth Int. Symp. Remote Sens. Environ. 1972, 2, 1357–1381. [Google Scholar]
- Huete, A.R. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 1988, 25, 295–309. [Google Scholar] [CrossRef]
- Gitelson, A.A. Wide Dynamic Range Vegetation Index for Remote Quantification of Biophysical Characteristics of Vegetation. J. Plant Physiol. 2004, 161, 165–173. [Google Scholar] [CrossRef]
- Kaufman, Y.J.; Tanre, D. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Trans. Geosci. Remote Sens. 1992, 30, 261–270. [Google Scholar] [CrossRef]
- Datt, B.; McVicar, T.R.; Van Niel, T.G.; Jupp, D.L.; Pearlman, J.S. Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes. IEEE Trans. Geosci. Remote Sens. 2003, 41, 1246–1259. [Google Scholar] [CrossRef]
- Tucker, C.J.; Elgin, J.H.; McMurtrey, J.E.; Fan, C.J. Monitoring corn and soybean crop development with hand-held radiometer spectral data. Remote Sens. Environ. 1979, 8, 237–248. [Google Scholar] [CrossRef]
- Ahamed, T.; Tian, L.; Zhang, Y.; Ting, K.C. A review of remote sensing methods for biomass feedstock production. Biomass Bioenergy 2011, 35, 2455–2469. [Google Scholar] [CrossRef]
- Miura, T.; Yoshioka, H.; Fujiwara, K.; Yamamoto, H. Inter-Comparison of ASTER and MODIS Surface Reflectance and Vegetation Index Products for Synergistic Applications to Natural Resource Monitoring. Sensors 2008, 8, 2480–2499. [Google Scholar] [CrossRef] [PubMed]
- Jiang, Z.; Huete, A.R.; Didan, K.; Miura, T. Development of a two-band enhanced vegetation index without a blue band. Remote Sens. Environ. 2008, 112, 3833–3845. [Google Scholar] [CrossRef]
- Gitelson, A.A.; Kaufman, Y.J.; Merzlyak, M.N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 1996, 58, 289–298. [Google Scholar] [CrossRef]
- Wang, F.-m.; Huang, J.-f.; Tang, Y.-l.; Wang, X.-z. New Vegetation Index and Its Application in Estimating Leaf Area Index of Rice. Rice Sci. 2007, 14, 195–203. [Google Scholar] [CrossRef]
- Baret, F.; Guyot, G. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sens. Environ. 1991, 35, 161–173. [Google Scholar] [CrossRef]
- Jurgens, C. The modified normalized difference vegetation index (mNDVI) a new index to determine frost damages in agriculture based on Landsat TM data. Int. J. Remote Sens. 1997, 18, 3583–3594. [Google Scholar] [CrossRef]
- Gitelson, A.A.; Merzlyak, M.N. Signature Analysis of Leaf Reflectance Spectra: Algorithm Development for Remote Sensing of Chlorophyll. J. Plant Physiol. 1996, 148, 494–500. [Google Scholar] [CrossRef]
- Zebarth, B.; Younie, M.; Paul, J.; Bittman, S. Evaluation of leaf chlorophyll index for making fertilizer nitrogen recommendations for silage corn in a high fertility environment. Commun. Soil Sci. Plant Anal. 2002, 33, 665–684. [Google Scholar] [CrossRef]
- Barnes, E.; Clarke, T.; Richards, S.; Colaizzi, P.; Haberland, J.; Kostrzewski, M.; Waller, P.; Choi, C.; Riley, E.; Thompson, T. Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data. In Proceedings of the Fifth International Conference on Precision Agriculture, Bloomington, MN, USA, 16–19 July 2000; p. 6. [Google Scholar]
- Rondeaux, G.; Steven, M.; Baret, F. Optimization of soil-adjusted vegetation indices. Remote Sens. Environ. 1996, 55, 95–107. [Google Scholar] [CrossRef]
- Kursa, M.B.; Rudnicki, W.R. Feature Selection with the Boruta Package. J. Stat. Softw. 2010, 36, 1–13. [Google Scholar] [CrossRef]
- Ye, Q.; Yu, S.; Liu, J.; Zhao, Q.; Zhao, Z. Aboveground biomass estimation of black locust planted forests with aspect variable using machine learning regression algorithms. Ecol. Indic. 2021, 129, 107948. [Google Scholar] [CrossRef]
- Li, Y.; Zhang, Q.-B. History of tree growth declines recorded in old trees at two sacred sites in northern China. Front. Plant Sci. 2017, 8, 1779. [Google Scholar] [CrossRef]
- Palik, B.J.; Ostry, M.E.; Venette, R.C.; Abdela, E. Fraxinus nigra (black ash) dieback in Minnesota: Regional variation and potential contributing factors. For. Ecol. Manag. 2011, 261, 128–135. [Google Scholar] [CrossRef]
- Simard, M.; Powell, E.N.; Raffa, K.F.; Turner, M.G. What explains landscape patterns of tree mortality caused by bark beetle outbreaks in Greater Yellowstone? Glob. Ecol. Biogeogr. 2011, 21, 556–567. [Google Scholar] [CrossRef]
- Romanuk, T.N.; Silva, L.C.R.; Anand, M.; Leithead, M.D. Recent Widespread Tree Growth Decline Despite Increasing Atmospheric CO2. PLoS ONE 2010, 5, 11543. [Google Scholar] [CrossRef]
- Busby, P.E.; Newcombe, G.; Neat, A.S.; Averill, C. Facilitating Reforestation Through the Plant Microbiome: Perspectives from the Phyllosphere. Annu. Rev. Phytopathol. 2022, 60, 337–356. [Google Scholar] [CrossRef] [PubMed]
- Stobbe, H.; Dujesiefken, D.; Schröder, K. Tree Crown Stabilization with the Doublebelt System Osnabrück. Arboric. Urban For. 2000, 26, 270–274. [Google Scholar] [CrossRef]
- Mantanis, G.; Terzi, E.; Kartal, S.N.; Papadopoulos, A.N. Evaluation of mold, decay and termite resistance of pine wood treated with zinc- and copper-based nanocompounds. Int. Biodeterior. Biodegrad. 2014, 90, 140–144. [Google Scholar] [CrossRef]
- Gallardo-Salazar, J.L.; Pompa-García, M. Detecting Individual Tree Attributes and Multispectral Indices Using Unmanned Aerial Vehicles: Applications in a Pine Clonal Orchard. Remote Sens. 2020, 12, 4144. [Google Scholar] [CrossRef]
- Vivar-Vivar, E.D.; Pompa-García, M.; Martínez-Rivas, J.A.; Mora-Tembre, L.A. UAV-Based Characterization of Tree-Attributes and Multispectral Indices in an Uneven-Aged Mixed Conifer-Broadleaf Forest. Remote Sens. 2022, 14, 2775. [Google Scholar] [CrossRef]
- Deng, X.; Zhu, Z.; Yang, J.; Zheng, Z.; Huang, Z.; Yin, X.; Wei, S.; Lan, Y. Detection of Citrus Huanglongbing Based on Multi-Input Neural Network Model of UAV Hyperspectral Remote Sensing. Remote Sens. 2020, 12, 2678. [Google Scholar] [CrossRef]
- Zeng, T.; Zhang, H.; Li, Y.; Yin, C.; Liang, Q.; Fang, J.; Fu, W.; Wang, J.; Zhang, X. Monitoring the Severity of Rubber Tree Infected with Powdery Mildew Based on UAV Multispectral Remote Sensing. Forests 2023, 14, 717. [Google Scholar] [CrossRef]
- Gini, R.; Sona, G.; Ronchetti, G.; Passoni, D.; Pinto, L. Improving Tree Species Classification Using UAS Multispectral Images and Texture Measures. ISPRS Int. J. Geo-Inf. 2018, 7, 315. [Google Scholar] [CrossRef]
- Colditz, R.R. An Evaluation of Different Training Sample Allocation Schemes for Discrete and Continuous Land Cover Classification Using Decision Tree-Based Algorithms. Remote Sens. 2015, 7, 9655–9681. [Google Scholar] [CrossRef]
- Yu, J.-W.; Yoon, Y.-W.; Baek, W.-K.; Jung, H.-S. Forest Vertical Structure Mapping Using Two-Seasonal Optic Images and LiDAR DSM Acquired from UAV Platform through Random Forest, XGBoost, and Support Vector Machine Approaches. Remote Sens. 2021, 13, 4282. [Google Scholar] [CrossRef]
- Dalponte, M.; Ørka, H.O.; Ene, L.T.; Gobakken, T.; Næsset, E. Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data. Remote Sens. Environ. 2014, 140, 306–317. [Google Scholar] [CrossRef]
- Du, P.; Samat, A.; Waske, B.; Liu, S.; Li, Z. Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features. ISPRS J. Photogramm. Remote Sens. 2015, 105, 38–53. [Google Scholar] [CrossRef]
- Leckie, D.G.; Gougeon, F.A.; Tinis, S.; Nelson, T.; Burnett, C.N.; Paradine, D. Automated tree recognition in old growth conifer stands with high resolution digital imagery. Remote Sens. Environ. 2005, 94, 311–326. [Google Scholar] [CrossRef]
- Leckie, D.G.; Tinis, S.; Nelson, T.; Burnett, C.; Gougeon, F.A.; Cloney, E.; Paradine, D. Issues in species classification of trees in old growth conifer stands. Can. J. Remote Sens. 2005, 31, 175–190. [Google Scholar] [CrossRef]
- Ganthaler, A.; Losso, A.; Mayr, S. Using image analysis for quantitative assessment of needle bladder rust disease of Norway spruce. Plant Pathol. 2018, 67, 1122–1130. [Google Scholar] [CrossRef]
- Potter, K.M.; Conkling, B.L. Forest Health Monitoring: National Status, Trends, and Analysis 2021; U.S. Department of Agriculture, Forest Service, Southern Research Station: Asheville, NC, USA, 2022.
- Cantón, Y.; Del Barrio, G.; Solé-Benet, A.; Lázaro, R. Topographic controls on the spatial distribution of ground cover in the Tabernas badlands of SE Spain. CATENA 2004, 55, 341–365. [Google Scholar] [CrossRef]
- Cutler, D.R.; Edwards Jr, T.C.; Beard, K.H.; Cutler, A.; Hess, K.T.; Gibson, J.; Lawler, J.J. Random forests for classification in ecology. Ecology 2007, 88, 2783–2792. [Google Scholar] [CrossRef]
- Näsi, R.; Honkavaara, E.; Lyytikäinen-Saarenmaa, P.; Blomqvist, M.; Litkey, P.; Hakala, T.; Viljanen, N.; Kantola, T.; Tanhuanpää, T.; Holopainen, M. Using UAV-Based Photogrammetry and Hyperspectral Imaging for Mapping Bark Beetle Damage at Tree-Level. Remote Sens. 2015, 7, 15467–15493. [Google Scholar] [CrossRef]
- Spies, T.A.; Franklin, J.F. The Structure of Natural Young, Mature, and Old-Growth Douglas-Fir Forests in Oregon and Washington. In Wildlife and Vegetation of Unmanaged Douglas-Fir Forests; U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station: Portland, OR, USA, 1991; pp. 91–109. [Google Scholar]
- Morgantini, L.E.; Kansas, J.L. Differentiating mature and old-growth forests in the Upper Foothills and Subalpine Subregions of west-central Alberta. For. Chronicle. 2003, 3, 602–612. [Google Scholar] [CrossRef]
- Windrim, L.; Carnegie, A.J.; Webster, M.; Bryson, M. Tree Detection and Health Monitoring in Multispectral Aerial Imagery and Photogrammetric Pointclouds Using Machine Learning. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2020, 13, 2554–2572. [Google Scholar] [CrossRef]
- Qin, S.; Wang, H.; Li, X.; Gao, J.; Jin, J.; Li, Y.; Lu, J.; Meng, P.; Sun, J.; Song, Z.; et al. Enhancing Landsat image based aboveground biomass estimation of black locust with scale bias-corrected LiDAR AGB map and stratified sampling. Geo-Spat. Inf. Sci. 2023, 1–14. [Google Scholar] [CrossRef]
Assessment Items | Evaluation Benchmark | ||||||
---|---|---|---|---|---|---|---|
0 | 1 | 2 | 3 | 4 | Score | Weight (%) | |
Tree vigor | Vigorous growth | Adversely affected | Apparent weakness | Extremely poor | Almost dead | 9.62 | |
Tree form | Natural tree form | Nearly natural tree form but some exceptions | Natural tree form partially damaged | Natural tree form damaged and deformed | Natural tree form damaged completely | 10.31 | |
Branch access | Normal | Having a certain but not obvious influence | Shorter and thinner branches | Branches extremely shortened, internodes inflated | Only having lower growth branches | 9.76 | |
Upper branches and tree apex mortality | None | Not obvious | Many | A great many | No tree apex and branches | 9.08 | |
Lower branches mortality | None | Not obvious | Some and some broken | Many, mostly broken | Without healthy branches | 7.96 | |
Damage of trunk and large branches | None | Rarely and having been restored | Obvious | Obvious and broken | Defect in the upper part | 7.26 | |
Foliage density | Branch and leaf density equilibrium | Normal foliage density | Relatively sparse | Few branches, sparse | Dead branches | 9.07 | |
Size of leaf buds | Leaf (bud) is sufficiently large | Some leaves (bud) smaller | Most buds smaller | All significantly smaller | Only a small number of buds present and smaller | 8.89 | |
Foliage colors | Almost thick green | Green | Some obvious yellow/brown leaves | Almost light green | All yellow/brown leaves | 9.11 | |
Bark damage (peeled/necrosis) | No damage | Few holes, no significant damage | Old scars | Wound decayed significantly | Large hole or severe peeling | 5.14 | |
Bark metabolism | Fresh bark, strong metabolism | Most of the bark fresh, few locations with weak individual metabolism | Apparent lack of vigor, weak metabolism | Almost all bark without vigor | Most of the bark necrotic | 5.60 | |
Germination and sprouting | Large amount of foliage, much germination and sprouting | Large amount of foliage, some green shoots sprouting | Less foliage, fewer green shoots sprouting | Little foliage, few green shoots sprouting | No germination and sprouting | 8.21 | |
Degree of senescence = the sum of the products of the indicator scores and their weights | Final score | ||||||
Final score | <1.40 | 1.40–1.67 | 1.67–2.20 | 2.20–2.48 | >2.48 | ||
Grade | I | II | III | IV | V | ||
Senescent degree | Healthy | Declining | Severe declining | ||||
Count | 53 | 68 | 21 |
Class | Variable | Formula | Reference |
---|---|---|---|
Vegetation indices | NDVI | (Nir − R)/(Nir + R) | [24] |
NDWI | (Rededge − Nir)/(Rededge + Nir) | [25] | |
RG | R/G | [26] | |
GB | G/B | [26] | |
DVI | Nir-R | [27] | |
MSAVI | 0.5[(2Nir + 1) − √(2Nir + 1)2 − 8(Nir − R)] | [28] | |
MSR | (Nir/R − 1)/√Nir/R + 1 | [29] | |
NDGI | (G − R)/(G + R) | [30] | |
RVI | Nir/R | [31] | |
SAVI | 1.5(Nir − R)/(Nir + R + 0.5) | [32] | |
WDEVI | (0.1Nir − R)/(0.1Nir + R) | [33] | |
ARVI | (Nir − 2R + B)/(Nir + 2R − B) | [34] | |
ARVI2 | −0.18 + 0.17(Nir − R)/(Nir + R) | [34] | |
WBRVI | (0.2Nir − R)/(0.2Nir + R) | [33] | |
CVI | Nir × R/G2 | [35] | |
GDVI | Nir − G | [36] | |
EVI | 2.5(Nir − R)/(Nir + 6R − 7.5B + 1) | [37] | |
EVI2 | 2.4(Nir − R)/(Nir + R + 1) | [38] | |
EVI2-2 | 2.5(Nir − R)/(Nir + 2.4R + 1) | [39] | |
GARI | [Nir − (G − (B − R))]/[Nir − (G + (B − R))] | [40] | |
GBNDVI | (Nir − (G + B))/(Nir + (G + B)) | [41] | |
GRNDVI | (Nir − (G + R))/(Nir + (G + R)) | [41] | |
MRVI | (RVI − 1)/(RVI + 1) | [42] | |
ANDVI | (0.5Nir − R)/(0.5Nir + R) | [43] | |
RDNDVI | (Rededge − R)/(Rededge + R) | [44] | |
PNDVI | (Nir − (R + G + B))/(Nir + (R + G + B)) | [41] | |
RBNDVI | (Nir − (R+B))/(Nir + (R + B)) | [41] | |
LCI | (Nir − Rededge)/(Nir + R) | [45] | |
NDRE | (Nir − Rededge)/(Nir + Rededge) | [46] | |
OSAVI | (Nir − R)/(Nir + R + 0.16) | [47] | |
GNDVI | (Nir − G)/(Nir + G) | [40] | |
Texture | DSM_GLCM and NDVI_GLCM(window size of 3 × 3 pixels and a 45 degree shift) | “mean”, “variance”, “homogeneity”, “contrast”, “dissimilarity”, “entropy”, “second_ moment”, “correlation” | [23] |
Component | Initial Eigenvalue | Extract the Sum of the Squares of the Loads | Rotational Load Sum of Squares | ||||||
---|---|---|---|---|---|---|---|---|---|
Total | Percentage of Variance | Accumulated % | Total | Percentage of Variance | Accumulated % | Total | Percentage of Variance | Accumulated % | |
1 | 6.107 | 50.894 | 50.894 | 6.107 | 50.894 | 50.894 | 3.203 | 26.694 | 26.694 |
2 | 1.234 | 10.283 | 61.177 | 1.234 | 10.283 | 61.177 | 3.038 | 25.316 | 52.011 |
3 | 1.034 | 8.62 | 69.797 | 1.034 | 8.62 | 69.797 | 2.134 | 17.787 | 69.797 |
4 | 0.748 | 6.231 | 76.029 | ||||||
5 | 0.671 | 5.595 | 81.623 | ||||||
6 | 0.592 | 4.937 | 86.56 | ||||||
7 | 0.439 | 3.659 | 90.219 | ||||||
8 | 0.344 | 2.865 | 93.083 | ||||||
9 | 0.271 | 2.26 | 95.344 | ||||||
10 | 0.223 | 1.862 | 97.206 | ||||||
11 | 0.218 | 1.816 | 99.022 | ||||||
12 | 0.117 | 0.978 | 100 |
Data and Method | Classified Levels | Reference Data | Total | UA | ||
---|---|---|---|---|---|---|
Healthy | Declining | Severe Declining | ||||
Selected with SVM | Healthy | 2 | 1 | 0 | 3 | 0.67 |
Declining | 12 | 16 | 3 | 31 | 0.52 | |
Severe declining | 0 | 0 | 2 | 2 | 1 | |
Total | 14 | 17 | 5 | 36 | ||
PA | 0.14 | 0.94 | 0.40 | |||
OA | 55.6% | Kappa | 0.197 | |||
Selected with RF | Healthy | 11 | 6 | 1 | 18 | 0.61 |
Declining | 3 | 10 | 3 | 16 | 0.62 | |
Severe declining | 0 | 1 | 1 | 2 | 0.50 | |
Total | 14 | 17 | 5 | 36 | ||
PA | 0.78 | 0.59 | 0.2 | |||
OA | 61.1% | Kappa | 0.338 |
Data and Method | Classified Levels | Reference Data | Total | UA | ||
---|---|---|---|---|---|---|
Healthy | Declining | Severe Declining | ||||
Selected & Area & Asp with SVM(A-SVM) | Healthy | 4 | 1 | 0 | 5 | 0.8 |
Declining | 10 | 16 | 2 | 28 | 0.57 | |
Severe declining | 0 | 0 | 3 | 3 | 1.0 | |
Total | 14 | 17 | 5 | 36 | ||
PA | 0.28 | 0.94 | 0.60 | |||
OA | 63.9% | Kappa | 0.363 | |||
Selected & Area & Asp with RF(A-RF) | Healthy | 12 | 3 | 1 | 16 | 0.75 |
Declining | 1 | 14 | 3 | 18 | 0.78 | |
Severe declining | 1 | 0 | 1 | 2 | 0.50 | |
Total | 14 | 17 | 5 | 36 | ||
PA | 0.86 | 0.82 | 0.2 | |||
OA | 75% | Kappa | 0.571 |
Health Status | Number of Trees | Percentage of Study Area (%) |
---|---|---|
Healthy | 1125 | 43.4 |
Declining | 1121 | 43.2 |
Severe declining | 349 | 13.4 |
Total | 2595 | 100 |
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Yin, D.; Cai, Y.; Li, Y.; Yuan, W.; Zhao, Z. Assessment of the Health Status of Old Trees of Platycladus orientalis L. Using UAV Multispectral Imagery. Drones 2024, 8, 91. https://doi.org/10.3390/drones8030091
Yin D, Cai Y, Li Y, Yuan W, Zhao Z. Assessment of the Health Status of Old Trees of Platycladus orientalis L. Using UAV Multispectral Imagery. Drones. 2024; 8(3):91. https://doi.org/10.3390/drones8030091
Chicago/Turabian StyleYin, Daihao, Yijun Cai, Yajing Li, Wenshan Yuan, and Zhong Zhao. 2024. "Assessment of the Health Status of Old Trees of Platycladus orientalis L. Using UAV Multispectral Imagery" Drones 8, no. 3: 91. https://doi.org/10.3390/drones8030091