{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T03:36:22Z","timestamp":1773977782399,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T00:00:00Z","timestamp":1686700800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2016YFB0501505"],"award-info":[{"award-number":["2016YFB0501505"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>To achieve carbon peaking and carbon neutrality in China, photovoltaic (PV) power generation has become increasingly important for promoting a low-carbon transition. The central and western desert areas of China have been identified as major areas for the construction of large PV bases. Remote sensing technology has been used to map the spatial distribution and development status of PV power stations quickly and accurately in ecologically fragile areas, as well as assess the ecological and environmental impact of their construction. However, current remote sensing monitoring of PV power stations focuses mainly on mapping and time series analysis to measure their development process and assess the environmental conditions on a large scale over a long period of time. Therefore, we constructed a random forest model based on image spectral and texture features and mapped 2022 PV power stations in the junction area of Hobq Desert, Ulan Buh Desert, Tengger Desert, and Mu Us Sands in China. Following that, we identified the construction time of the PV power stations by identifying the turning points of the normalized construction land index (NDBI) time series from 1990\u20132022 using the LandTrendr method. To assess the ecological impact of PV power stations, we used the NDVI to measure the change in vegetation condition before and after the construction of PV power stations and constructed NDVI changes for PV power stations constructed in different years. The results showed that this mapping method achieved an overall classification accuracy of 96.65% and a Kappa coefficient of 0.92. The root mean square error (RMSE) for construction year identification was less than 0.5, and the number of new PV power stations increased significantly after 2010, reaching a total area of 14.52 km2 by 2016, which is consistent with the trend driven by national and regional development plans. Furthermore, the study found that the vegetation cover level could be restored to the average level before construction within 5\u20136 years and continued to increase after that. These findings may help government policymakers and practitioners make decisions on PV power station planning and ecological environment protection, thus contributing promptly to the achievement of China\u2019s dual carbon goals.<\/jats:p>","DOI":"10.3390\/rs15123101","type":"journal-article","created":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T02:01:40Z","timestamp":1686708100000},"page":"3101","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Characterizing the Development of Photovoltaic Power Stations and Their Impacts on Vegetation Conditions from Landsat Time Series during 1990\u20132022"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3151-199X","authenticated-orcid":false,"given":"Su","family":"Ma","sequence":"first","affiliation":[{"name":"Chinese Research Academy of Environmental Sciences, Beijing 100012, China"}]},{"given":"Junhui","family":"Liu","sequence":"additional","affiliation":[{"name":"Chinese Research Academy of Environmental Sciences, Beijing 100012, China"}]},{"given":"Ping","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Geomatics Center of China, Beijing 100830, China"}]},{"given":"Xingyue","family":"Tu","sequence":"additional","affiliation":[{"name":"Chinese Research Academy of Environmental Sciences, Beijing 100012, China"}]},{"given":"Jianan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Chinese Research Academy of Environmental Sciences, Beijing 100012, China"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Chinese Research Academy of Environmental Sciences, Beijing 100012, China"}]},{"given":"Yingjuan","family":"Zheng","sequence":"additional","affiliation":[{"name":"Chinese Research Academy of Environmental Sciences, Beijing 100012, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,14]]},"reference":[{"key":"ref_1","first-page":"100937","article-title":"Mapping photovoltaic power stations and assessing their environmental impacts from multi-sensor datasets in Massachusetts, United States","volume":"30","author":"Tao","year":"2023","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.rser.2018.03.065","article-title":"Potential of solar energy in developing countries for reducing energy-related emissions","volume":"90","author":"Shahsavari","year":"2018","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rser.2012.10.003","article-title":"A review on solar energy utilisation in Australia","volume":"18","author":"Bahadori","year":"2013","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1513","DOI":"10.1016\/j.rser.2010.11.037","article-title":"Role of renewable energy sources in environmental protection: A review","volume":"15","author":"Panwar","year":"2011","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1016\/j.renene.2021.01.028","article-title":"Measurement and key influencing factors of the economic benefits for China\u2019s photovoltaic power generation: A LCOE-based hybrid model","volume":"169","author":"Wang","year":"2021","journal-title":"Renew. Energy"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Xu, M. (2021). Texture Is Important in Improving the Accuracy of Mapping Photovoltaic Power Plants: A Case Study of Ningxia Autonomous Region, China. Remote Sens., 13.","DOI":"10.3390\/rs13193909"},{"key":"ref_7","first-page":"103280","article-title":"Rapid mapping and spatial analysis on the distribution of photovoltaic power stations with Sentinel-1&2 images in Chinese coastal provinces","volume":"118","author":"Jiang","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"116338","DOI":"10.1016\/j.jenvman.2022.116338","article-title":"Solar photovoltaic program helps turn deserts green in China: Evidence from satellite monitoring","volume":"324","author":"Xia","year":"2022","journal-title":"J. Environ. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.isprsjprs.2021.01.003","article-title":"Mapping coastal salt marshes in China using time series of Sentinel-1 SAR","volume":"173","author":"Hu","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"160106","DOI":"10.1038\/sdata.2016.106","article-title":"Distributed solar photovoltaic array location and extent dataset for remote sensing object identification","volume":"3","author":"Bradbury","year":"2016","journal-title":"Sci. Data"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6506","DOI":"10.1117\/1.JRS.14.016506","article-title":"Photovoltaic power station identification using refined encoder-decoder network with channel attention and chained residual dilated convolutions","volume":"14","author":"Jie","year":"2020","journal-title":"J. Appl. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Jie, Y., Ji, X., Yue, A., Chen, J., and Zhang, Y. (2020). Combined Multi-Layer Feature Fusion and Edge Detection Method for Distributed Photovoltaic Power Station Identification. Energies, 13.","DOI":"10.3390\/en13246742"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"010802","DOI":"10.1115\/1.4051949","article-title":"A comparison between Deep Learning and Support Vector Regression Techniques applied to solar forecast in Spain","volume":"144","author":"Lima","year":"2022","journal-title":"J. Sol. Energy Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, X., Han, L., Han, L., and Zhu, L. (2020). How Well Do Deep Learning-Based Methods for Land Cover Classification and Object Detection Perform on High Resolution Remote Sensing Imagery?. Remote Sens., 12.","DOI":"10.3390\/rs12030417"},{"key":"ref_15","first-page":"103134","article-title":"Deep solar PV refiner: A detail-oriented deep learning network for refined segmentation of photovoltaic areas from satellite imagery","volume":"116","author":"Zhu","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4117","DOI":"10.1016\/j.egyr.2022.03.039","article-title":"Mapping the rapid development of photovoltaic power stations in northwestern China using remote sensing","volume":"8","author":"Xia","year":"2022","journal-title":"Energy Rep."},{"key":"ref_17","first-page":"102707","article-title":"High-resolution mapping of water photovoltaic development in China through satellite imagery","volume":"107","author":"Xia","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"112905","DOI":"10.1016\/j.rse.2022.112905","article-title":"Time series analysis for global land cover change monitoring: A comparison across sensors","volume":"271","author":"Xu","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.rse.2009.08.014","article-title":"Detecting trend and seasonal changes in satellite image time series","volume":"114","author":"Verbesselt","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.rse.2014.09.010","article-title":"Detecting changes in vegetation trends using time series segmentation","volume":"156","author":"Jamali","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"112249","DOI":"10.1016\/j.jenvman.2021.112249","article-title":"Natural and anthropogenic forcings lead to contrasting vegetation response in long-term vs. short-term timeframes","volume":"286","author":"Kazemzadeh","year":"2021","journal-title":"J. Environ. Manag."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"104225","DOI":"10.1016\/j.landurbplan.2021.104225","article-title":"Identifying and understanding alternative states of dryland landscape: A hierarchical analysis of time series of fractional vegetation-soil nexuses in China\u2019s Hexi Corridor","volume":"215","author":"Sun","year":"2021","journal-title":"Landsc. Urban Plan."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","unstructured":"Kennedy, R.E., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W.B., and Healey, S. (2018). Implementation of the LandTrendr Algorithm on Google Earth Engine. Remote Sens., 10.","DOI":"10.3390\/rs10050691"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1080\/01431160304987","article-title":"Use of normalized difference built-up index in automatically mapping urban areas from TM imagery","volume":"24","author":"Zha","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","first-page":"610","article-title":"Textural Features for Image Classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"Stud. Media Commun."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.isprsjprs.2015.03.002","article-title":"Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features","volume":"105","author":"Du","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Tassi, A., and Vizzari, M. (2020). Object-Oriented LULC Classification in Google Earth Engine Combining SNIC, GLCM, and Machine Learning Algorithms. Remote Sens., 12.","DOI":"10.3390\/rs12223776"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.rse.2011.12.003","article-title":"Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture","volume":"121","author":"Atkinson","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.patrec.2005.08.011","article-title":"Random Forests for land cover classification","volume":"27","author":"Gislason","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.isprsjprs.2020.06.022","article-title":"Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat, Random Forest, and Google Earth Engine","volume":"167","author":"Phalke","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"109428","DOI":"10.1016\/j.biocon.2021.109428","article-title":"Long-term monitoring of NDVI changes by remote sensing to assess the vulnerability of threatened plants","volume":"265","author":"Pizarro","year":"2022","journal-title":"Biol. Conserv."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"109967","DOI":"10.1016\/j.rser.2020.109967","article-title":"Potential of unsubsidized distributed solar PV to replace coal-fired power plants, and profits classification in Chinese cities","volume":"131","author":"Yang","year":"2020","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"110102","DOI":"10.1016\/j.rser.2020.110102","article-title":"Transition of China\u2019s power sector consistent with Paris Agreement into 2050: Pathways and challenges","volume":"132","author":"Zhang","year":"2020","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.enpol.2016.09.013","article-title":"Government subsidies for the Chinese photovoltaic industry","volume":"99","author":"Xiong","year":"2016","journal-title":"Energy Policy"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"111723","DOI":"10.1016\/j.enpol.2020.111723","article-title":"Does solar PV bring a sustainable future to the poor?\u2014An empirical study of anti-poverty policy effects on environmental sustainability in rural China","volume":"145","author":"Wang","year":"2020","journal-title":"Energy Policy"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"117631","DOI":"10.1016\/j.energy.2020.117631","article-title":"Has solar PV achieved the national poverty alleviation goals? Empirical evidence from the performances of 52 villages in rural China","volume":"201","author":"Li","year":"2020","journal-title":"Energy"},{"key":"ref_40","first-page":"549","article-title":"An carbon neutrality industrial chain of \u201cdesert-photovoltaic power generation-ecological agriculture\u201d: Practice from the Ulan Buh Desert, Dengkou, Inner Mongolia","volume":"5","author":"Chen","year":"2022","journal-title":"China Geol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/12\/3101\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:54:27Z","timestamp":1760126067000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/12\/3101"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,14]]},"references-count":40,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["rs15123101"],"URL":"https:\/\/doi.org\/10.3390\/rs15123101","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,14]]}}}