{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T22:36:38Z","timestamp":1774478198049,"version":"3.50.1"},"reference-count":64,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T00:00:00Z","timestamp":1668556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific Research Foundation of Minnan Normal University","award":["KJ2022001"],"award-info":[{"award-number":["KJ2022001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Island ecosystems are susceptible to the considerable impacts of increasing human activities, landscape reconstruction, and urban expansion, resulting in changes in the ecological environment and urban ecological security issues. Remote sensing techniques can achieve the near-real-time ecological environment monitoring of these rapidly changing areas. The remote sensing-based ecological index (RSEI), as a comprehensive remote sensing ecological environment index, was adopted to dynamically monitor urban ecological quality (EQ) over time in this study, combined with the Landsat-based detection of trends in disturbance and recovery (LandTrendr) algorithm. Annual composite images were generated using Landsat 5, Landsat 7, and Landsat 8 imagery to extract four metrics (Greenness, Moisture, Heat, and Dryness) to calculate RSEI from 1991 to 2021. The ecological quality in the study area was evaluated using a five-level classification (poor, inferior, medium, good, and excellent), and the changes in EQ on a pixel basis were identified by the LandTrendr algorithm. The results showed that (1) the average value of the RSEI ranged from 0.47 to 0.57 over 31 years, indicating that EQ was maintained at the medium level; (2) the distribution of different EQ levels had visible patterns, and an area of 47.87 km2 was affected by a poor EQ at least once in 31 years; (3) 38.22 km2 of this area experienced EQ poor disturbance once, and 3.05 km2 of the area had poor disturbance twice. Urban expansion, forest degradation, and policy are the main factors causing the reduction of the RSEI. The results demonstrate that combining time series of RSEI and LandTrendr can effectively monitor the changes of EQ, which is helpful to identify the spatial\u2013temporal variation patterns of EQ and provide valuable information for policymakers and protection.<\/jats:p>","DOI":"10.3390\/rs14225773","type":"journal-article","created":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T02:36:36Z","timestamp":1668566196000},"page":"5773","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Monitoring Ecological Changes on a Rapidly Urbanizing Island Using a Remote Sensing-Based Ecological Index Produced Time Series"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5098-8182","authenticated-orcid":false,"given":"Lili","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Biological Science and Biotechnology, Minnan Normal University, Zhangzhou 363000, China"},{"name":"University Key Lab for Fujian and Taiwan Garden Plants in Fujian Province, Zhangzhou 363000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4094-7157","authenticated-orcid":false,"given":"Zhenbang","family":"Hao","sequence":"additional","affiliation":[{"name":"Department of Biological Science and Biotechnology, Minnan Normal University, Zhangzhou 363000, China"},{"name":"College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China"}]},{"given":"Christopher J.","family":"Post","sequence":"additional","affiliation":[{"name":"Department of Forestry and Environmental Conservation, Clemson University, Clemson, SC 29634, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1711-7910","authenticated-orcid":false,"given":"Elena A.","family":"Mikhailova","sequence":"additional","affiliation":[{"name":"Department of Forestry and Environmental Conservation, Clemson University, Clemson, SC 29634, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1071\/PC140206","article-title":"Conservation of biodiversity in the pacific islands of Oceania: Challenges and opportunities","volume":"20","author":"Jupiter","year":"2014","journal-title":"Pac. Conserv. Biol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.ecolind.2012.07.001","article-title":"Evaluating marine ecosystem health: Case studies of indicators using direct observations and modelling methods","volume":"24","author":"Rombouts","year":"2013","journal-title":"Ecol. Indic."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1016\/j.landusepol.2018.12.036","article-title":"An insight into land-cover changes and their impacts on ecosystem services before and after the implementation of a comprehensive experimental zone plan in Pingtan island, China","volume":"82","author":"Shifaw","year":"2019","journal-title":"Land Use Pol."},{"key":"ref_4","first-page":"1599","article-title":"An assessment of China\u2019s ecological environment quality change and its spatial variation","volume":"67","author":"Sun","year":"2012","journal-title":"Acta Geogr. Sin."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1016\/j.marpolbul.2011.12.022","article-title":"Evaluation for the ecological quality status of coastal waters in East China Sea using fuzzy integrated assessment method","volume":"64","author":"Wu","year":"2012","journal-title":"Mar. Pollut. Bull."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"112752","DOI":"10.1016\/j.rse.2021.112752","article-title":"Remote sensing annual dynamics of rapid permafrost thaw disturbances with LandTrendr","volume":"268","author":"Runge","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"118126","DOI":"10.1016\/j.jclepro.2019.118126","article-title":"Ecological environment quality assessment based on remote sensing data for land consolidation","volume":"239","author":"Shan","year":"2019","journal-title":"J. Clean. Prod."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"152595","DOI":"10.1016\/j.scitotenv.2021.152595","article-title":"Instability of remote sensing based ecological index (RSEI) and its improvement for time series analysis","volume":"814","author":"Zheng","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"107518","DOI":"10.1016\/j.ecolind.2021.107518","article-title":"Assessment of spatial-temporal changes of ecological environment quality based on RSEI and GEE: A case study in Erhai Lake Basin, Yunnan province, China","volume":"125","author":"Xiong","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"133928","DOI":"10.1016\/j.scitotenv.2019.133928","article-title":"Ecological environment assessment based on land use simulation: A case study in the Heihe River Basin","volume":"697","author":"Wang","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1016\/j.jclepro.2016.09.011","article-title":"Dynamic analysis of ecological environment combined with land cover and NDVI changes and implications for sustainable urban\u2013rural development: The case of Mu Us Sandy Land, China","volume":"142","author":"Li","year":"2017","journal-title":"J. Clean. Prod."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.tourman.2016.05.006","article-title":"Examining eco-environmental changes at major recreational sites in Kenting National Park in Taiwan by integrating SPOT satellite images and NDVI","volume":"57","author":"Wu","year":"2016","journal-title":"Tour. Manag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.ecolind.2015.05.036","article-title":"Vegetation dynamics and responses to recent climate change in Xinjiang using leaf area index as an indicator","volume":"58","author":"Liang","year":"2015","journal-title":"Ecol. Indic."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1016\/j.rse.2005.04.003","article-title":"Evaluation of seasonal variation of MODIS derived leaf area index at two European deciduous broadleaf forest sites","volume":"96","author":"Wang","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1929","DOI":"10.1016\/j.jaridenv.2008.06.005","article-title":"Regional assessment of environmental vulnerability in the Tibetan Plateau: Development and application of a new method","volume":"72","author":"Wang","year":"2008","journal-title":"J. Arid Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1016\/j.ecolind.2008.05.013","article-title":"Estimating the ecological status and change of riparian zones in Andalusia assessed by multi-temporal AVHHR datasets","volume":"9","author":"Ivits","year":"2009","journal-title":"Ecol. Indic."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3485","DOI":"10.1007\/s11269-011-9867-1","article-title":"Drought monitoring by Reconnaissance Drought Index (RDI) in Iran","volume":"25","author":"Malekinezhad","year":"2011","journal-title":"Water Resour. Manag."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1002\/met.136","article-title":"On the use of Standardized Precipitation Index (SPI) for drought intensity assessment","volume":"16","author":"Murthy","year":"2009","journal-title":"Meteorol. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2875","DOI":"10.1016\/j.rse.2010.07.005","article-title":"Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data","volume":"114","author":"Rhee","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2103","DOI":"10.1016\/j.rse.2009.05.016","article-title":"Testing a MODIS Global Disturbance Index across North America","volume":"113","author":"Mildrexler","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"109080","DOI":"10.1016\/j.ecolind.2022.109080","article-title":"Time-frequency optimization of RSEI: A case study of Yangtze River Basin","volume":"141","author":"Yang","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"108214","DOI":"10.1016\/j.ecolind.2021.108214","article-title":"Spatiotemporal change and driving factors of the Eco-Environment quality in the Yangtze River Basin from 2001 to 2019","volume":"131","author":"Yang","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Cui, R., Han, J., and Hu, Z. (2022). Assessment of spatial temporal changes of ecological environment quality: A case study in Huaibei City, China. Land, 11.","DOI":"10.3390\/land11060944"},{"key":"ref_24","first-page":"889","article-title":"A remote sensing index for assessment of regional ecological changes","volume":"33","author":"Xu","year":"2013","journal-title":"China Environ. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"107438","DOI":"10.1016\/j.ecolind.2021.107438","article-title":"Spatiotemporal evolution of island ecological quality under different urban densities: A comparative analysis of Xiamen and Kinmen Islands, southeast China","volume":"124","author":"Liu","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wen, X., Ming, Y., Gao, Y., and Hu, X. (2020). Dynamic monitoring and analysis of ecological quality of Pingtan Comprehensive Experimental Zone, a new type of sea island city, based on RSEI. Sustainability, 12.","DOI":"10.3390\/su12010021"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"126995","DOI":"10.1016\/j.jclepro.2021.126995","article-title":"Spatiotemporal change detection of ecological quality and the associated affecting factors in Dongting Lake Basin, based on RSEI","volume":"302","author":"Yuan","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4477","DOI":"10.3390\/rs13214477","article-title":"Landsat TM\/OLI-Based Ecological and Environmental Quality Survey of Yellow River Basin, Inner Mongolia Section","volume":"13","author":"Gao","year":"2021","journal-title":"Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1016\/j.ecolind.2018.05.055","article-title":"Prediction of ecological effects of potential population and impervious surface increases using a remote sensing based ecological index (RSEI)","volume":"93","author":"Xu","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.rse.2015.02.012","article-title":"Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series","volume":"161","author":"DeVries","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.isprsjprs.2020.04.001","article-title":"Google Earth Engine for geo-big data applications: A meta-analysis and systematic review","volume":"164","author":"Tamiminia","year":"2020","journal-title":"ISPRS\u2014J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"112002","DOI":"10.1016\/j.rse.2020.112002","article-title":"A summary of the special issue on remote sensing of land change science with Google Earth Engine","volume":"248","author":"Wang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Dewi, R., Bijker, W., and Stein, A. (2017). Change vector analysis to monitor the changes in fuzzy shorelines. Remote Sens., 9.","DOI":"10.3390\/rs9020147"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1016\/j.rse.2009.02.004","article-title":"Updating the 2001 National Land Cover Database land cover classification to 2006 by using Landsat imagery change detection methods","volume":"113","author":"Xian","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.rse.2016.02.030","article-title":"A new approach for land cover classification and change analysis: Integrating backdating and an object-based method","volume":"177","author":"Yu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.rse.2014.01.011","article-title":"Continuous change detection and classification of land cover using all available Landsat data","volume":"144","author":"Zhu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3316","DOI":"10.1109\/TGRS.2013.2272545","article-title":"On-the-Fly massively multitemporal change detection using statistical quality control charts and Landsat data","volume":"52","author":"Brooks","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.rse.2016.02.060","article-title":"Perspectives on monitoring gradual change across the continuity of Landsat sensors using time-series data","volume":"185","author":"Vogelmann","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2897","DOI":"10.1016\/j.rse.2010.07.008","article-title":"Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr\u2014Temporal segmentation algorithms","volume":"114","author":"Kennedy","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_41","first-page":"102293","article-title":"Mapping mangrove dynamics and colonization patterns at the Suriname coast using historic satellite data and the LandTrendr algorithm","volume":"97","author":"Shen","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"108763","DOI":"10.1016\/j.ecolind.2022.108763","article-title":"Spatio-temporal coupling coordination analysis between marsh vegetation and hydrology change from 1985 to 2019 using LandTrendr algorithm and Google Earth Engine","volume":"137","author":"Fu","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.rse.2017.02.021","article-title":"Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine","volume":"202","author":"Huang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.10.028","article-title":"Object-based cloud and cloud shadow detection in Landsat imagery","volume":"118","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.rse.2014.12.014","article-title":"Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4\u20137, 8, and Sentinel 2 images","volume":"159","author":"Zhu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.rse.2017.11.015","article-title":"A LandTrendr multispectral ensemble for forest disturbance detection","volume":"205","author":"Cohen","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"6481","DOI":"10.3390\/rs5126481","article-title":"Seasonal composite Landsat TM\/ETM+ images using the Medoid (A multi-dimensional median)","volume":"5","author":"Flood","year":"2013","journal-title":"Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"863","DOI":"10.3390\/rs9080863","article-title":"A dynamic Landsat derived Normalized Difference Vegetation Index (NDVI) product for the conterminous united states","volume":"9","author":"Robinson","year":"2017","journal-title":"Remote Sens."},{"key":"ref_49","first-page":"589","article-title":"A study on information extraction of water body with the modified normalized difference water index (MNDWI)","volume":"9","author":"Xu","year":"2005","journal-title":"J. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1891","DOI":"10.3390\/rs11161891","article-title":"A scheme for the long-term monitoring of impervious\u2014Relevant land disturbances using high frequency Landsat archives and the Google Earth Engine","volume":"11","author":"Xu","year":"2019","journal-title":"Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Cao, H., Liu, J., Fu, C., Zhang, W., Wang, G., Yang, G., and Luo, L. (2017). Urban expansion and its impact on the land use pattern in Xishuangbanna since the reform and opening up of China. Remote Sens., 9.","DOI":"10.3390\/rs9020137"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.ocecoaman.2012.06.014","article-title":"Urban spatial expansion and its impacts on island ecosystem services and landscape pattern: A case study of the island city of Xiamen, Southeast China","volume":"81","author":"Lin","year":"2013","journal-title":"Ocean. Coast. Manag."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Lin, L., Hao, Z., Post, C.J., Mikhailova, E.A., Yu, K., Yang, L., and Liu, J. (2020). Monitoring land cover change on a rapidly urbanizing island using Google Earth Engine. Appl. Sci., 10.","DOI":"10.3390\/app10207336"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.foreco.2015.04.022","article-title":"New estimates of CO2 forest emissions and removals: 1990\u20132015","volume":"352","author":"Federici","year":"2015","journal-title":"For. Ecol. Manag."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1126\/science.aam5962","article-title":"Tropical forests are a net carbon source based on aboveground measurements of gain and loss","volume":"358","author":"Baccini","year":"2017","journal-title":"Science"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"111853","DOI":"10.1016\/j.rse.2020.111853","article-title":"A multi-sensor, multi-scale approach to mapping tree mortality in woodland ecosystems","volume":"245","author":"Campbell","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.isprsjprs.2020.08.025","article-title":"Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin","volume":"169","author":"Vergara","year":"2020","journal-title":"ISPRS\u2013J. Photogramm. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"112847","DOI":"10.1016\/j.rse.2021.112847","article-title":"Landsat-based monitoring of southern pine beetle infestation severity and severity change in a temperate mixed forest","volume":"269","author":"Meng","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_59","first-page":"102363","article-title":"A machine learning algorithm to detect pine wilt disease using UAV-based hyperspectral imagery and LiDAR data at the tree level","volume":"101","author":"Yu","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Li, J., Gong, J., Guldmann, J., and Yang, J. (2021). Assessment of urban ecological quality and spatial heterogeneity based on remote sensing: A case study of the rapid urbanization of Wuhan city. Remote Sens., 13.","DOI":"10.3390\/rs13214440"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"112741","DOI":"10.1016\/j.rse.2021.112741","article-title":"Multi-sensor change detection for within-year capture and labelling of forest disturbance","volume":"268","author":"Cardille","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.rse.2015.08.030","article-title":"Evaluation of the Landsat-5 TM and Landsat-7 ETM+ surface reflectance products","volume":"169","author":"Claverie","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/j.rse.2015.04.004","article-title":"Characteristics of Landsat 8 OLI-derived NDVI by comparison with multiple satellite sensors and in-situ observations","volume":"164","author":"Ke","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"112517","DOI":"10.1016\/j.rse.2021.112517","article-title":"Making Landsat 5, 7 and 8 reflectance consistent using MODIS nadir-BRDF adjusted reflectance as reference","volume":"262","author":"Che","year":"2021","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/22\/5773\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:19:03Z","timestamp":1760145543000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/22\/5773"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,16]]},"references-count":64,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14225773"],"URL":"https:\/\/doi.org\/10.3390\/rs14225773","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,16]]}}}