{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:29:09Z","timestamp":1760149749949,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T00:00:00Z","timestamp":1694563200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"Project of Intergovernmental International Cooperation in Science and Technology Innovation","doi-asserted-by":"publisher","award":["2019YFE0116500","2022YFHH0065"],"award-info":[{"award-number":["2019YFE0116500","2022YFHH0065"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Provincial Scientific Research Projects of Inner Mongolia","award":["2019YFE0116500","2022YFHH0065"],"award-info":[{"award-number":["2019YFE0116500","2022YFHH0065"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The deterioration of farmland shelterbelts in the Ulan Buh desert oases could weaken their protective functions. Therefore, an accurate method is essential to assess tree decline degree in order to guide the rejuvenation and transformation of these shelterbelts. This study selected three typical farmland shelterbelts in the Ulan Buh desert oases as the objects. Terrestrial laser scanning (TLS) and airborne hyperspectral imagery (AHI) were used to acquire point cloud data and detailed spectral information of trees. Point cloud and spectral characteristics of trees with varying decline levels were analyzed. Six models were constructed to identify decline degree of shelterbelts, and model accuracy was evaluated. The coefficient of determination between the structural parameters of trees extracted by TLS and field measurements ranged from 0.76 to 0.94. Healthy trees outperformed declining trees in structural parameters, particularly in tridimensional green biomass and crown projection area. Spectral reflectance changes in the 740\u2013950 nm band were evident among the three tree types with different decline levels, decreasing significantly with increased decline level. Among the TLS-derived feature parameters, the canopy relief ratio of tree points and point cloud density strongly correlated with the degree of tree decline. The plant senescence reflectance index and normalized difference vegetation index exhibited the closest correlation with tree decline in AHI data. The average accuracy of the models constructed based on the feature parameters of LiDAR, AHI, and the combination of both of them were 0.77, 0.61, and 0.81, respectively. The light gradient-boosting machine model utilizing TLS\u2013AHI comprehensive feature parameters accurately determined tree decline. This study highlights the efficacy of employing feature parameters derived from TLS alone to accurately identify tree decline. Combining feature parameters from the TLS and AHI enhances the precision of tree decline identification. This approach offers guidance for decisions regarding the renewal and transformation of declining farmland shelterbelts.<\/jats:p>","DOI":"10.3390\/rs15184508","type":"journal-article","created":{"date-parts":[[2023,9,14]],"date-time":"2023-09-14T10:00:23Z","timestamp":1694685623000},"page":"4508","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Assessment of the Declining Degree of Farmland Shelterbelts in a Desert Oasis Based on LiDAR and Hyperspectral Imagery"],"prefix":"10.3390","volume":"15","author":[{"given":"Chengwei","family":"Luo","sequence":"first","affiliation":[{"name":"College of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China"},{"name":"State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing 100083, China"},{"name":"Inner Mongolia Dengkou Desert Ecosystem National Observation Research Station, National Forestry and Grassland Administration, Dengkou 015200, China"}]},{"given":"Yuli","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China"},{"name":"State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Zhiming","family":"Xin","sequence":"additional","affiliation":[{"name":"Inner Mongolia Dengkou Desert Ecosystem National Observation Research Station, National Forestry and Grassland Administration, Dengkou 015200, China"},{"name":"Experimental Center of Desert Forestry, Chinese Academy of Forestry, Dengkou 015200, China"}]},{"given":"Junran","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Geography, The University of Hong Kong, Hong Kong 999077, China"}]},{"given":"Xiaoxiao","family":"Jia","sequence":"additional","affiliation":[{"name":"College of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China"},{"name":"State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing 100083, China"},{"name":"Inner Mongolia Dengkou Desert Ecosystem National Observation Research Station, National Forestry and Grassland Administration, Dengkou 015200, China"}]},{"given":"Guangpeng","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Junying","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China"},{"name":"State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing 100083, China"},{"name":"Inner Mongolia Dengkou Desert Ecosystem National Observation Research Station, National Forestry and Grassland Administration, Dengkou 015200, China"}]},{"given":"Jindui","family":"Song","sequence":"additional","affiliation":[{"name":"College of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China"},{"name":"State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing 100083, China"},{"name":"Inner Mongolia Dengkou Desert Ecosystem National Observation Research Station, National Forestry and Grassland Administration, Dengkou 015200, China"}]},{"given":"Zhou","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China"},{"name":"State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing 100083, China"},{"name":"Inner Mongolia Dengkou Desert Ecosystem National Observation Research Station, National Forestry and Grassland Administration, Dengkou 015200, China"}]},{"given":"Huijie","family":"Xiao","sequence":"additional","affiliation":[{"name":"College of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China"},{"name":"State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing 100083, China"},{"name":"Inner Mongolia Dengkou Desert Ecosystem National Observation Research Station, National Forestry and Grassland Administration, Dengkou 015200, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105068","DOI":"10.1016\/j.apsoil.2023.105068","article-title":"Northeastern China shelterbelt-farmland glomalin differences depend on geo-climates, soil depth, and microbial interaction: Carbon sequestration, nutrient retention and implication","volume":"191","author":"Cheng","year":"2023","journal-title":"Appl. Soil Ecol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"106832","DOI":"10.1016\/j.agee.2020.106832","article-title":"Optimizing the quantity and spatial patterns of farmland shelter forests increases cotton productivity in arid lands","volume":"292","author":"Li","year":"2020","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"125611","DOI":"10.1016\/j.jhydrol.2020.125611","article-title":"Response of shelterbelt transpiration to shallow groundwater in arid areas","volume":"592","author":"Du","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"106697","DOI":"10.1016\/j.agee.2019.106697","article-title":"Variation of water uptake in degradation agroforestry shelterbelts on the North China Plain","volume":"287","author":"Liu","year":"2020","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"100139","DOI":"10.1016\/j.tfp.2021.100139","article-title":"Machine learning models perform better than traditional empirical models for stomatal conductance when applied to multiple tree species across different forest biomes\u2014ScienceDirect","volume":"6","author":"Saunders","year":"2021","journal-title":"Trees For. People"},{"doi-asserted-by":"crossref","unstructured":"Gao, Y., Skutsch, M., Rodr\u00edguez, D.L.J., and Sol\u00f3rzano, J.V. (2020). Identifying Variables to Discriminate between Conserved and Degraded Forest and to Quantify the Differences in Biomass. Forests, 11.","key":"ref_6","DOI":"10.3390\/f11091020"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.rse.2014.09.008","article-title":"Land subsidence and ground fissures in Xi\u2019an, China 2005\u20132012 revealed by multi-band InSAR time-series analysis","volume":"155","author":"Qu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"93264","DOI":"10.1109\/ACCESS.2020.2993025","article-title":"Monitoring of Drought Condition and Risk in Bangladesh Combined Data From Satellite and Ground Meteorological Observations","volume":"8","author":"Prodhan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3610","DOI":"10.1109\/TGRS.2006.881743","article-title":"Voxel-Based 3-D Modeling of Individual Trees for Estimating Leaf Area Density Using High-Resolution Portable Scanning Lidar","volume":"44","author":"Hosoi","year":"2006","journal-title":"IEEE Trans. Geosci. Remote"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.isprsjprs.2021.08.021","article-title":"A novel cotton mapping index combining Sentinel-1 SAR and Sentinel-2 multispectral imagery","volume":"181","author":"Xun","year":"2021","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.1016\/j.rse.2010.12.011","article-title":"Airborne discrete-return LIDAR data in the estimation of vertical canopy cover, angular canopy closure and leaf area index","volume":"115","author":"Korhonen","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2025","DOI":"10.1016\/j.rse.2011.04.004","article-title":"Optimising the use of hyperspectral and LiDAR data for mapping reedbed habitats","volume":"115","author":"Onojeghuo","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/rs4010001","article-title":"Retrieving Forest Inventory Variables with Terrestrial Laser Scanning (TLS) in Urban Heterogeneous Forest","volume":"4","author":"Monika","year":"2011","journal-title":"Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"119037","DOI":"10.1016\/j.foreco.2021.119037","article-title":"Detecting the effects of logging and wildfire on forest fuel structure using terrestrial laser scanning (TLS)","volume":"488","author":"Wilson","year":"2021","journal-title":"For. Ecol. Manag."},{"doi-asserted-by":"crossref","unstructured":"Meunier, F., Moorthy, S.M.K., Deurwaerder, H.P.T.D., Kreus, R., and Verbeeck, H. (2020). Within-Site Variability of Liana Wood Anatomical Traits: A Case Study in Laussat, French Guiana. Forests, 11.","key":"ref_15","DOI":"10.3390\/f11050523"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2144","DOI":"10.1016\/j.patrec.2013.08.004","article-title":"Single tree species classification from Terrestrial Laser Scanning data for forest inventory","volume":"34","author":"Othmani","year":"2013","journal-title":"Pattern Recognit. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"527","DOI":"10.5632\/jila.84.527","article-title":"Application of 3D tree modeling using point cloud data by terrestrial laser scanner","volume":"84","author":"Kumazaki","year":"2021","journal-title":"J. Jpn. Inst. Landsc. Archit."},{"doi-asserted-by":"crossref","unstructured":"Fan, G., Nan, L., Dong, Y., Su, X., and Chen, F. (2020). AdQSM: A New Method for Estimating Above-Ground Biomass from TLS Point Clouds. Remote Sens., 12.","key":"ref_18","DOI":"10.3390\/rs12183089"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1093\/forestry\/cpw010","article-title":"Classification of multilayered forest development classes from low-density national airborne lidar datasets","volume":"89","author":"Valbuena","year":"2016","journal-title":"Forestry."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2166","DOI":"10.3390\/s110202166","article-title":"3-D Modeling of Tomato Canopies Using a High-Resolution Portable Scanning Lidar for Extracting Structural Information","volume":"11","author":"Hosoi","year":"2011","journal-title":"Sensors"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"15467","DOI":"10.3390\/rs71115467","article-title":"Using UAV-Based Photogrammetry and Hyperspectral Imaging for Mapping Bark Beetle Damage at Tree-Level","volume":"7","author":"Honkavaara","year":"2015","journal-title":"Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1007\/s12524-014-0392-6","article-title":"Spectral and Texture Features Combined for Forest Tree species Classification with Airborne Hyperspectral Imagery","volume":"43","author":"Yuanyong","year":"2015","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1614\/WS-06-212.1","article-title":"Estimating Yellow Starthistle (Centaurea solstitialis) Leaf Area Index and Aboveground Biomass with the Use of Hyperspectral Data","volume":"55","year":"2007","journal-title":"Weed Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4065","DOI":"10.3390\/rs13204065","article-title":"Three-Dimensional Convolutional Neural Network Model for Early Detection of Pine Wilt Disease Using UAV-Based Hyperspectral Images","volume":"13","author":"Ren","year":"2021","journal-title":"Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.rse.2018.06.008","article-title":"Mapping canopy defoliation by herbivorous insects at the individual tree level using bi-temporal airborne imaging spectroscopy and LiDAR measurements","volume":"215","author":"Meng","year":"2018","journal-title":"Remote Sens. Environ."},{"doi-asserted-by":"crossref","unstructured":"Chi, D., Degerickx, J., Yu, K., and Somers, B. (2020). Urban Tree Health Classification Across Tree Species by Combining Airborne Laser Scanning and Imaging Spectroscopy. Remote Sens., 12.","key":"ref_26","DOI":"10.3390\/rs12152435"},{"doi-asserted-by":"crossref","unstructured":"Iordache, M.D., Mantas, V., Baltazar, E., and Lewyckyj, N. (2020). A Machine Learning Approach to Detecting Pine Wilt Disease Using Airborne Spectral Imagery. Remote Sens., 101.","key":"ref_27","DOI":"10.3390\/rs12142280"},{"key":"ref_28","first-page":"854","article-title":"Ecological of Stoichiometric Characteristics of Populus davidiana forests with Different Growth and Decline Degrees in Southern Daxing\u2019anling","volume":"52","author":"Wang","year":"2021","journal-title":"Chin. J. Soil Sci."},{"doi-asserted-by":"crossref","unstructured":"Zhang, W., Qi, J., Wan, P., Wang, H., Xie, D., Wang, X., and Yan, G. (2016). An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation. Remote Sens., 8.","key":"ref_29","DOI":"10.3390\/rs8060501"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1111\/2041-210X.13342","article-title":"LeWoS: A universal leaf-wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR","volume":"11","author":"Wang","year":"2020","journal-title":"Methods Ecol. Evol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"112912","DOI":"10.1016\/j.rse.2022.112912","article-title":"Quantifying tropical forest structure through terrestrial and UAV laser scanning fusion in Australian rainforests","volume":"271","author":"Terryn","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1111\/2041-210X.12301","article-title":"Nondestructive estimates of above-ground biomass using terrestrial laser scanning","volume":"6","author":"Calders","year":"2015","journal-title":"Methods Ecol. Evol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.rse.2004.03.014","article-title":"A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky\u2013Golay filter","volume":"91","author":"Chen","year":"2004","journal-title":"Remote Sens. Environ."},{"unstructured":"Sidle, G.D. (2017). Using Multi-Class Machine Learning Methods to Predict Major League Baseball Pitches, North Carolina State University\u2009ProQuest Dissertations Publishing.","key":"ref_34"},{"key":"ref_35","first-page":"1589","article-title":"Research on Extraction of Camellia Oleifera by Integrating Spectral, Texture and Time Sequence Remote Sensing Information","volume":"43","author":"Meng","year":"2023","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3958","DOI":"10.1109\/TGRS.2012.2187907","article-title":"Computational-Geometry-Based Retrieval of Effective Leaf Area Index Using Terrestrial Laser Scanning","volume":"50","author":"Zheng","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"106595","DOI":"10.1016\/j.agee.2019.106595","article-title":"Precipitaion and soil water thresholds associated with drought-induced mortality of farmland shelter forests in a semi-arid area","volume":"284","author":"Sun","year":"2019","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/s11104-011-0733-y","article-title":"Effects of cadmium and salicylic acid on growth, spectral reflectance and photosynthesis of castor bean seedlings","volume":"344","author":"Liu","year":"2011","journal-title":"Plant Soil"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/S1011-1344(96)07394-0","article-title":"The Shibata shift; effects of in vitro conditions on the spectral blue-shift of chlorophyllide in irradiated isolated prolamellar bodies","volume":"36","author":"Zhong","year":"1996","journal-title":"J. Photochem. Photobiol. B Biol."},{"key":"ref_40","first-page":"53","article-title":"Research of Damage Monitoring Models and Judgment Rules of Pinus yunnanensis with Tomicus yunnanensis","volume":"31","author":"Wang","year":"2018","journal-title":"For. Res."},{"key":"ref_41","first-page":"80","article-title":"Classification Diagnosis on the Damage Degree of Tomicus yunnanensis to Pinus yunnanensis Based on Hyperspectral and Airborne LiDAR","volume":"42","author":"Ma","year":"2022","journal-title":"J. Southwest For. Univ."},{"key":"ref_42","first-page":"26","article-title":"Urban tree health assessment using airborne hyperspectral and LiDAR imagery","volume":"73","author":"Degerickx","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1016\/j.ecolind.2019.03.036","article-title":"Tree defoliation classification based on point distribution features derived from single-scan terrestrial laser scanning data","volume":"103","author":"Huo","year":"2019","journal-title":"Ecol. Indic."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.foreco.2003.09.001","article-title":"The canopy surface and stand development: Assessing forest canopy structure and complexity with near-surface altimetry","volume":"189","author":"Parker","year":"2004","journal-title":"For. Ecol. Manag."},{"doi-asserted-by":"crossref","unstructured":"Lin, Q., Huang, H., Wang, J., Huang, K., and Liu, Y. (2019). Detection of Pine Shoot Beetle (PSB) Stress on Pine Forests at Individual Tree Level using UAV-Based Hyperspectral Imagery and Lidar. Remote Sens., 11.","key":"ref_45","DOI":"10.3390\/rs11212540"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/18\/4508\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:50:18Z","timestamp":1760129418000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/18\/4508"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,13]]},"references-count":45,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["rs15184508"],"URL":"https:\/\/doi.org\/10.3390\/rs15184508","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,9,13]]}}}