Please wait a minute...
 
Remote Sensing for Natural Resources    2023, Vol. 35 Issue (4) : 301-311     DOI: 10.6046/zrzyyg.2022269
|
Suitability of photovoltaic development in the Western Sichuan Plateau based on remote sensing data
YUAN Hong1(), YI Guihua2(), ZHANG Tingbin1,3, BIE Xiaojuan2, LI Jingji3,4, WANG Guoyan2, XU Yonghao1
1. College of Earth Science, Chengdu University of Technology, Chengdu 610059, China
2. College of Tourism and Urban-Rural Planning, Chengdu University of Technology, Chengdu 610059, China
3. State Environmental Protection Key Laboratory of Synergetic Control and Joint Remediation for Soil and Water Pollution, Chengdu University of Technology, Chengdu 610059, China
4. College of Ecology and Environment, Chengdu University of Technology, Chengdu 610059, China
Download: PDF(3769 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

The rapid growth of China’s photovoltaic (PV) industry is accompanied by unplanned construction of PV power plants. Ascertaining the regional PV development suitability, power generation potential, and emission reduction effects holds critical significance for the sound development of the PV industry. Based on remote sensing, meteorological, and fundamental geographic data, this study constructed an evaluation index system for PV development suitability. Using this system, it assessed the zones suitable for PV development in the Western Sichuan Plateau and estimated the PV power generation potential and emission reduction effects. The results are as follows: ① The zones suitable for PV development account for 57.43% of the entire plateau, with highly suitable zones covering an area of approximately 2.07×104 km2, which are distributed primarily in the southwestern and northwestern portions of the plateau; ② The plateau exhibits significant power generation potential, reaching 17 197.97×108 KWh in highly suitable zones under a full development scenario, which is equivalent to 6.52-fold Sichuan Province’s total electricity consumption in 2019 before the COVID-19 outbreak; ③ Contrasting with conventional thermal power generation, PV power generation in highly suitable zones can achieve annual CO2 emission reduction of 12.45×108 t, which is about 12.71% of China’s total CO2 emissions in 2019 and 3.95-fold Sichuan Province’s CO2 emissions. Moreover, PV power generation can diminish the emissions of coal and conventional pollutants as well as heavy metals. The findings offer a scientific reference and guidance for selecting sites for PV power plants in the Western Sichuan Plateau and promoting the sustainable growth of the PV industry.

Keywords photovoltaic power generation      emission reduction effect      suitability      analytic hierarchy process      Western Sichuan Plateau     
ZTFLH:  TP79  
Issue Date: 21 December 2023
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Hong YUAN
Guihua YI
Tingbin ZHANG
Xiaojuan BIE
Jingji LI
Guoyan WANG
Yonghao XU
Cite this article:   
Hong YUAN,Guihua YI,Tingbin ZHANG, et al. Suitability of photovoltaic development in the Western Sichuan Plateau based on remote sensing data[J]. Remote Sensing for Natural Resources, 2023, 35(4): 301-311.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022269     OR     https://www.gtzyyg.com/EN/Y2023/V35/I4/301
Fig.1  General map of the study area
n 1 2 3 4 5 6 7 8
RI 0 0 0.58 0.90 1.12 1.24 1.32 1.41
Tab.1  Random consistency index
目标层 一级指标 权重Wk 二级指标 权重Wkl
光伏
开发A
气象A1 0.55 太阳辐射A11 0.51
气温A12 0.15
日照时数A13 0.26
降水量A14 0.08
地形A2 0.23 坡度A21 0.67
坡向A22 0.33
位置A3 0.14 道路距离A31 0.28
居民点距离A32 0.50
保护区距离A33 0.15
河流距离A34 0.07
植被A4 0.08 NDVI A41 1.00
Tab.2  Evaluation index and its weight of photovoltaic development suitability analysis in the study area
年总辐射量/(kWh·m-2) 分级 分级编号
≥1 750 最丰富
[1 400,1 750) 很丰富
[1 050,1 400) 较丰富
<1 050 一般
Tab.3  Classification of solar energy resources richness
太阳能资源稳定度指标 稳定程度
<2 稳定
[2,4] 较稳定
>4 不稳定
Tab.4  Classification of solar energy resources stability
Fig.2  Distribution of solar energy resources in the study area
Fig.3  Classification of the secondary indicators of photovoltaic development suitability analysis in the study area
Fig.4  Classification of photovoltaic development suitability of the study area
Fig.5  Photovoltaic development suitability classification area in different counties of the study area
县名 发电潜力 县名 发电潜力
石渠县 6 203.25 炉霍县 230.61
理塘县 2 886.88 阿坝县 191.06
稻城县 1 218.30 红原县 166.31
甘孜县 1 114.34 壤塘县 107.74
若尔盖县 1 092.23 道孚县 103.93
色达县 943.05 金川县 24.48
巴塘县 884.99 得荣县 20.09
德格县 481.37 马尔康县 17.90
雅江县 431.86 丹巴县 16.96
乡城县 383.20 康定县 9.09
新龙县 362.06 九龙县 1.14
白玉县 306.76 小金县 0.38
Tab.5  Photovoltaic power generation potential in high suitable areas in some counties
开发比例/% 发电潜力/亿kWh CO2/亿t
100 17 197.97 12.45
75 12 898.48 9.34
50 8 598.99 6.23
25 4 299.49 3.11
Tab.6  Potentials of power generation and CO2 emission reduction under different development intensity scenarios in high suitable areas
开发比例/% 标准煤 炭粉灰 SO2 NOX
100 5.76 4.68 0.52 0.26
75 4.32 3.51 0.39 0.19
50 2.88 2.34 0.26 0.13
25 1.44 1.17 0.13 0.06
Tab.7  Potentials of emission reduction of coal and conventional pollutants under different development intensity scenarios in high suitable area
开发
比例/%
As Cr Pb Hg Ni Cd
100 2 875.50 6 902.41 7 441.56 83.41 8 061.89 114.8
75 2 156.63 5 176.81 5 581.17 62.56 6 046.42 86.1
50 1 437.75 3 451.20 3 720.78 41.71 4 030.95 57.4
25 718.88 1 725.60 1 860.39 20.85 2 015.47 28.7
Tab.8  Potentials of heavy metal emision reduction under different development intensity scenarios in high suitable area
[1] IPCC. Climate change 2022:Mitigation of climate change.Contribution of working group Ⅲ to the sixth assessment report of the IPCC[R]. New York: Cambridge University Press, 2022.
[2] 国家发改委, 国家能源局. 关于完善能源绿色低碳转型体制机制和政策措施的意见(发改能源〔2022〕206号)[EB/OL].(2022-1-30)[2022-06-20]. https://www.ndrc.gov.cn/xxgk/zcfb/tz/202202/t20220210_1314511.html.
url: https://www.ndrc.gov.cn/xxgk/zcfb/tz/202202/t20220210_1314511.html
[2] National Development and Reform Commission,National Energy Administration. Policies and measures to improve the mechanism for a green-oriented transition of energy[EB/OL].(2022-1-30)[2022-06-20]. https://www.ndrc.gov.cn/xxgk/zcfb/tz/202202/t20220210_1314511.html.
url: https://www.ndrc.gov.cn/xxgk/zcfb/tz/202202/t20220210_1314511.html
[3] 杨俊峰, 李博洋, 霍婧, 等. “十四五”中国光伏行业绿色低碳发展关键问题分析[J]. 有色金属(冶炼部分), 2021(12):57-62.
[3] Yang J F, Li B Y, Huo J, et al. Analysis on key issues of green and low-carbon development in Chinese photovoltaic industry during the 14th Five-Year plan period[J]. Nonferrous Metals(Extractive Metallurgy), 2021(12):57-62.
[4] 胡鞍钢. 中国实现2030年前碳达峰目标及主要途径[J]. 北京工业大学学报(社会科学版), 2021, 21(3):1-15.
[4] Hu A G. China’s goal of achieving carbon peak by 2030 and its main approaches[J]. Journal of Beijing University of Technology(Social Sciences Edition), 2021, 21(3):1-15.
[5] 习近平. 在第七十五届联合国大会一般性辩论上的讲话[EB/OL].(2020-09-22)[2020-06-20]. http://www.gov.cn/gongbao/content/2020/content_5549875.htm.
url: http://www.gov.cn/gongbao/content/2020/content_5549875.htm
[5] Xi J P. Speech at the general debate of the 75th session of the United Nations General Assembly[EB/OL].(2020-09-22)[2020-06-20]. http://www.gov.cn/gongbao/content/2020/content_5549875.htm.
url: http://www.gov.cn/gongbao/content/2020/content_5549875.htm
[6] 李柯, 何凡能. 中国陆地太阳能资源开发潜力区域分析[J]. 地理科学进展, 2010, 29(9):1049-1054.
[6] Li K, He F N. Analysis on mainland China’s mainland’s solar energy distribution and potential to utilize solar energy as an alternative energy source[J]. Progress in Geography, 2010, 29(9):1049-1054.
[7] Stanhill G, Cohen S. Solar radiation changes in the United States during the twentieth century:Evidence from sunshine duration measurements[J]. Journal of Climate, 2005, 18(10):1503-1512.
doi: 10.1175/JCLI3354.1 url: http://journals.ametsoc.org/doi/10.1175/JCLI3354.1
[8] Tang W J, Yang K, Qin J, et al. First effort for constructing a direct solar radiation data set in China for solar energy applications[J]. Journal of Geophysical Research:Atmospheres, 2018, 123(3):1724-1734.
doi: 10.1002/jgrd.v123.3 url: https://agupubs.onlinelibrary.wiley.com/toc/21698996/123/3
[9] Feng Y, Zhang X L, Jia Y, et al. High-resolution assessment of solar radiation and energy potential in China[J]. Energy Conversion and Management, 2021, 240:114265.
doi: 10.1016/j.enconman.2021.114265 url: https://linkinghub.elsevier.com/retrieve/pii/S0196890421004416
[10] 刘淳, 任立清, 李学军, 等. 1990—2019年中国北方沙区太阳能资源评估[J]. 高原气象, 2021, 40(5):1213-1223.
doi: 10.7522/j.issn.1000-0534.2021.00058
[10] Liu C, Ren L Q, Li X J, et al. Evaluation to the solar energy resources in the sandy regions of northern China from 1990 to 2019[J]. Plateau Meteorology, 2021, 40(5):1213-1223.
doi: 10.7522/j.issn.1000-0534.2021.00058
[11] Doljak D, Stanojević G. Evaluation of natural conditions for site selection of ground-mounted photovoltaic power plants in Serbia[J]. Energy, 2017, 127:291-300.
doi: 10.1016/j.energy.2017.03.140 url: https://linkinghub.elsevier.com/retrieve/pii/S0360544217305339
[12] Merrouni A A, Elalaoui F E, Ghennioui A, et al. A GIS-AHP combination for the sites assessment of large-scale CSP plants with dry and wet cooling systems.Case study:Eastern Morocco[J]. Solar Energy, 2018, 166:2-12.
doi: 10.1016/j.solener.2018.03.038 url: https://linkinghub.elsevier.com/retrieve/pii/S0038092X18302706
[13] 张乾, 辛晓洲, 张海龙, 等. 基于遥感数据和多因子评价的中国地区建设光伏电站的适宜性分析[J]. 地球信息科学学报, 2018, 20(1):119-127.
doi: 10.12082/dqxxkx.2018.170393
[13] Zhang Q, Xin X Z, Zhang H L, et al. Suitability analysis of photovoltaic power plants in China using remote sensing data and multi criteria evaluation[J]. Journal of Geo-Information Science, 2018, 20(1):119-127.
[14] Mensour O N, El Ghazzani B, Hlimi B, et al. A geographical information system-based multi-criteria method for the evaluation of solar farms locations:A case study in Souss-Massa area,southern Morocco[J]. Energy, 2019, 182:900-919.
doi: 10.1016/j.energy.2019.06.063
[15] Sánchez-Lozano J M, García-Cascales M S, Lamata M T. Comparative TOPSIS-ELECTRE TRI methods for optimal sites for photovoltaic solar farms.Case study in Spain[J]. Journal of Cleaner Production, 2016, 127:387-398.
doi: 10.1016/j.jclepro.2016.04.005 url: https://linkinghub.elsevier.com/retrieve/pii/S0959652616302463
[16] Wang S H, Zhang L F, Fu D J, et al. Selecting photovoltaic generation sites in Tibet using remote sensing and geographic analysis[J]. Solar Energy, 2016, 133:85-93.
doi: 10.1016/j.solener.2016.03.069 url: https://linkinghub.elsevier.com/retrieve/pii/S0038092X16300226
[17] Sabo M L, Mariun N, Hizam H, et al. Spatial matching of large-scale grid-connected photovoltaic power generation with utility demand in Peninsular Malaysia[J]. Applied Energy, 2017, 191:663-688.
doi: 10.1016/j.apenergy.2017.01.087 url: https://linkinghub.elsevier.com/retrieve/pii/S0306261917300983
[18] Qiu T Z, Wang L C, Lu Y B, et al. Potential assessment of photovoltaic power generation in China[J]. Renewable and Sustainable Energy Reviews, 2022, 154:111900.
doi: 10.1016/j.rser.2021.111900 url: https://linkinghub.elsevier.com/retrieve/pii/S1364032121011667
[19] Liu F, Lyu T. Assessment of geographical distribution of photovoltaic generation in China for a low carbon electricity transition[J]. Journal of Cleaner Production, 2019, 212:655-665.
doi: 10.1016/j.jclepro.2018.12.045 url: https://linkinghub.elsevier.com/retrieve/pii/S0959652618337478
[20] 刘立程, 孙中孝, 吴锋, 等. 京津冀地区光伏开发空间适宜性及减排效益评估[J]. 地理学报, 2022, 77(3):665-678.
doi: 10.11821/dlxb202203012
[20] Liu L C, Sun Z X, Wu F, et al. Evaluation of suitability and emission reduction benefits of photovoltaic development in Beijing-Tianjin-Hebei region[J]. Acta Geographica Sinica, 2022, 77(3):665-678.
doi: 10.11821/dlxb202203012
[21] 国家能源局. 国家能源局2021年四季度网上新闻发布会文字实录[EB/OL].(2021-11-08)[2022-06-20]. http://www.nea.gov.cn/2021-11/08/c_1310298464.htm.
url: http://www.nea.gov.cn/2021-11/08/c_1310298464.htm
[21] National Energy Administration. Transcript of the online press conference of the National Energy Administration in the fourth quarter of 2021[EB/OL].(2021-11-08)[2021-06-20]. http://www.nea.gov.cn/2021-11/08/c_1310298464.htm.
url: http://www.nea.gov.cn/2021-11/08/c_1310298464.htm
[22] Zoghi M, Ehsani A H, Sadat M, et al. Optimization solar site selection by fuzzy logic model and weighted linear combination method in arid and semi-arid region:A case study Isfahan-IRAN[J]. Renewable and Sustainable Energy Reviews, 2017, 68:986-996.
doi: 10.1016/j.rser.2015.07.014 url: https://linkinghub.elsevier.com/retrieve/pii/S1364032115006619
[23] Potić I, Golić R, Joksimović T. Analysis of insolation potential of Knjaževac Municipality (Serbia) using multi-criteria approach[J]. Renewable and Sustainable Energy Reviews, 2016, 56:235-245.
doi: 10.1016/j.rser.2015.11.056 url: https://linkinghub.elsevier.com/retrieve/pii/S1364032115013234
[24] 陈静, 陈静波, 孟瑜, 等. 尺度和密度约束下基于YOLOv3的风电塔架遥感检测方法[J]. 自然资源遥感, 2021, 33(3):54-62.doi:10.6046/zrzyyg.2020309.
[24] Chen J, Chen J B, Meng Y, et al. Detection of wind turbine towers in remote sensing based on YOLOv3 model under scale and density constraints[J]. Remote Sensing for Natural Resources, 2021, 33(3):54-62.doi:10.6046/zrzyyg.2020309.
[25] 郭鹏, 申彦波, 陈峰, 等. 光伏发电潜力分析——以山西省为例[J]. 气象科技进展, 2019, 9(2):78-83.
[25] Guo P, Shen Y B, Chen F, et al. The analysis of PV electricity generation potential:A case study in Shanxi[J]. Advances in Meteorological Science and Technology, 2019, 9(2):78-83.
[26] 丁一凡. 山地光伏电站设计和运维若干关键问题研究[D]. 杭州: 浙江大学, 2021.
[26] Ding Y F. Research on several key issues in the design and maintenance of mountainous photovoltaic farms[D]. Hangzhou: Zhejiang University, 2021.
[27] 四川省人民政府. 四川省“十四五”能源发展规划(川府发〔2022〕8号)[EB/OL].(2022-03-03)[2022-06-20]. .
url: https://www.sc.gov.cn/10462/zfwjts/2022/3/4/f09dbec42f7349589d0421454 37004a6.shtml
[27] The People’s Government of Sichuan Province. The 14th Five-Year plan for energy developent in Sichuan Province[EB/OL].(2022-03-03)[2022-06-20]. .
url: https://www.sc.gov.cn/ 10462/zfwjts/2022/3/4/f09dbec42f7349589d042145437004a6.shtml
[28] 易桂花, 张廷斌, 何奕萱, 等. 四种气温空间插值方法适用性分析[J]. 成都理工大学学报(自然科学版), 2020, 47(1):115-128.
[28] Yi G H, Zhang T B, He Y X, et al. Applicability analysis of four spatial interpolation methods for air temperature[J]. Journal of Chengdu University of Technology(Science and Technology Edition), 2020, 47(1):115-128.
[29] 于海敬, 陈庭甫, 张庆国, 等. 基于空间插值技术的西藏日照时数时空变化特征及其影响因素[J]. 浙江大学学报(农业与生命科学版), 2019, 45(1):75-84.
[29] Yu H J, Chen T F, Zhang Q G, et al. Temporal-spatial change characteristics of sunshine duration in Tibet based on spatial interpolation technique and its influence factors[J]. Journal of Zhejiang University (Agriculture and Life Sciences), 2019, 45(1):75-84.
[30] 谢慧君, 张廷斌, 易桂花, 等. 川西高原植被NDVI动态变化特征及对气候因子的响应[J]. 水土保持通报, 2020, 40(4):286-294,328.
[30] Xie H J, Zhang T B, Yi G H, et al. Dynamic characteristics of NDVI values and its response to climatic factors in Western Sichuan Plateau[J]. Bulletin of Soil and Water Conservation, 2020, 40(4):286-294,328.
[31] 荣欣, 易桂花, 张廷斌, 等. 2000—2015年川西高原植被EVI海拔梯度变化及其对气候变化的响应[J]. 长江流域资源与环境, 2019, 28(12):3014-3028.
[31] Rong X, Yi G H, Zhang T B, et al. Change of vegetation EVI with altitude gradient and its response to climate change in the Western Sichuan Plateau from 2000 to 2015[J]. Resources and Environment in the Yangtze Basin, 2019, 28(12):3014-3028.
[32] 四川省统计局, 国家统计局四川调查总队.四川统计年鉴(2020年)[M]. 北京: 中国统计出版社, 2020.
[32] Sichuan Provincial Bureau of Statistics,The Sichuan survey general team of the national bureau of statistics. Sichuan statistical yearbook(2020)[M]. Beijing: China Statistics Press, 2020.
[33] 裴志方. 川西高原植被覆盖度景观格局动态变化研究[D]. 成都: 成都理工大学, 2018.
[33] Pei Z F. The dynamic change of landscape pattern of vegetation coverage in Western Sichuan Plateau[D]. Chengdu: Chengdu University of Technology, 2018.
[34] 张良, 张文秀. 川西高原区农村可持续发展评价[J]. 农村经济, 2004, 22(s1):48-51.
[34] Zhang L, Zhang W X. Evaluation of rural sustainable development in Western Sichuan Plateau area[J]. Rural Economy, 2004, 22(s1):48-51.
[35] 樊宁宁. 基于层次分析法的河北省城市宜居性评价[D]. 石家庄: 河北经贸大学, 2022.
[35] Fan N N. Evaluation of urban livability in Hebei Province based on analytic hierarchy process[D]. Shijiazhuang: Hebei University of Economics and Business, 2022.
[36] Saaty T L. Decision making with the analytic hierarchy process[J]. International Journal of Services Sciences, 2008, 1(1):83-98.
doi: 10.1504/IJSSCI.2008.017590 url: http://www.inderscience.com/link.php?id=17590
[37] 中国气象局. QX/T 89—2008太阳能资源评估方法[S]. 北京: 气象出版社, 2008.
[37] China Meteorological Administration. QX/T 89—2008 Assesment method for solar energy resources[S]. Beijing: China Meteorological Press, 2008.
[38] 钟燕川, 马振峰, 徐金霞, 等. 基于地形分布式模拟的四川省太阳能资源评估[J]. 西南大学学报(自然科学版), 2018, 40(7):115-121.
[38] Zhong Y C, Ma Z F, Xu J X, et al. Assessment of solar energy resource in Sichuan based on distributed modeling on rugged terrains[J]. Journal of Southwest University(Natural Science Edition), 2018, 40(7):115-121.
[39] 肖建华, 姚正毅, 孙家欢. 并网太阳能光伏电站选址研究述评[J]. 中国沙漠, 2011, 31(6):1598-1605.
[39] Xiao J H, Yao Z Y, Sun J H. Review on optimal site selection for grid-connected solar photovoltaic plants[J]. Journal of Desert Research, 2011, 31(6):1598-1605.
[40] Li X, Mauzerall D L, Bergin M H. Global reduction of solar power generation efficiency due to aerosols and panel soiling[J]. Nature Sustainability, 2020, 3(9):720-727.
doi: 10.1038/s41893-020-0553-2
[41] Wang Y, Zhou S, Huo H. Cost and CO2 reductions of solar photovoltaic power generation in China:Perspectives for 2020[J]. Renewable and Sustainable Energy Reviews, 2014, 39:370-380.
doi: 10.1016/j.rser.2014.07.027 url: https://linkinghub.elsevier.com/retrieve/pii/S1364032114004791
[42] 中华人民共和国生态环境部. 2018年度减排项目中国区域电网基准线排放因子[S]. 2018.
[42] Ministry of Ecology and Environment of the People’s Republic of China. China’s regional power grid baseline emission factor for 2018 emission reduction projects[S]. 2018.
[43] 国家发改委. CM-001-V02可再生能源并网发电方法学(第二版)[S]. 2016.
[43] National Development and Reform Commission. CM-001-V02 Methods of grid-connected power generation of renewable energy sources[S]. 2016.
[44] Guan Y R, Shan Y L, Huang Q, et al. Assessment to China’s recent emission pattern shifts[J]. Earth’s Future, 2021, 9(11): e2021EF002241.
[45] Shan Y L, Huang Q, Guan D B, et al. China CO2 emission accounts 2016—2017[J]. Scientific Data, 2020, 7:54.
doi: 10.1038/s41597-020-0393-y
[46] Shan Y L, Guan D B, Zheng H R, et al. China CO2 emission accounts 1997—2015[J]. Scientific Data, 2018, 5:170201.
doi: 10.1038/sdata.2017.201 url: https://www.nature.com/articles/sdata2017201
[47] Shan Y L, Liu J H, Liu Z, et al. New provincial CO2 emission inventories in China based on apparent energy consumption data and updated emission factors[J]. Applied Energy, 2016, 184:742-750.
doi: 10.1016/j.apenergy.2016.03.073 url: https://linkinghub.elsevier.com/retrieve/pii/S0306261916303932
[48] 卢霞. 荒漠戈壁区光伏电站建设的环境效应分析——以酒泉市东洞滩百万千瓦光伏示范基地为例[D]. 兰州: 兰州大学, 2013.
[48] Lu X. The environmental effect analysis of PV power plant construction in desert gobbi[D]. Lanzhou: Lanzhou University, 2013.
[49] 国家电投. 全球首个超高海拔光伏实证项目启动[EB/OL].(2021-12-15)[2022-06-20]. .
url: http://www.cpnn.com.cn/news/xwtopb/202112/t20211215_1466340.html
[49] State Power Investment Corporation. The world’s first ultra-high altitude photovoltaic demonstration project was launched[EB/OL].(2021-12-15)[2022-06-20]. .
url: http://www.cpnn.com.cn/news/xwtopb/202112/t20211215_1466340.html
[50] Tsoutsos T, Frantzeskaki N, Gekas V. Environmental impacts from the solar energy technologies[J]. Energy Policy, 2005, 33(3):289-296.
doi: 10.1016/S0301-4215(03)00241-6 url: https://linkinghub.elsevier.com/retrieve/pii/S0301421503002416
[51] 翟波, 高永, 党晓宏, 等. 光伏电板对羊草群落特征及多样性的影响[J]. 生态学杂志, 2018, 37(8):2237-2243.
[51] Zhai B, Gao Y, Dang X H, et al. Effects of photovoltaic panels on the characteristics and diversity of leymus chinensis community[J]. Chinese Journal of Ecology, 2018, 37(8):2237-2243.
[52] 殷代英, 马鹿, 屈建军, 等. 大型光伏电站对共和盆地荒漠区微气候的影响[J]. 水土保持通报, 2017, 37(3):15-21.
[52] Yin D Y, Ma L, Qu J J, et al. Effect of large photovoltaic power station on microclimate of desert region in Gonghe basin[J]. Bulletin of Soil and Water Conservation, 2017, 37(3):15-21.
[53] Li S, Weigand J. The potential for climate impacts from widespread deployment of utility-scale solar energy installations:An environmental remote sensing perspective[J]. Journal of Remote Sensing and GIS, 2017, 6(1):190.
[54] Zhang J, Lyu F, Zhang L. Discussion on environment impact assessment in the lifecycle of PV systems[J]. Energy Procedia, 2012, 16:234-239.
doi: 10.1016/j.egypro.2012.01.039 url: https://linkinghub.elsevier.com/retrieve/pii/S1876610212000495
[55] 王涛, 王得祥, 郭廷栋, 等. 光伏电站建设对土壤和植被的影响[J]. 水土保持研究, 2016, 23(3):90-94.
[55] Wang T, Wang D X, Guo T D, et al. The impact of photovoltaic power construction on soil and vegetation[J]. Research of Soil and Water Conservation, 2016, 23(3):90-94.
[1] WANG Yelan, YANG Xin, HAO Lina. Spatio-temporal changes in the normalized difference vegetation index of vegetation in the western Sichuan Plateau during 2001—2021 and their driving factors[J]. Remote Sensing for Natural Resources, 2023, 35(3): 212-220.
[2] ZHONG Le, ZENG Yan, QIU Xinfa, SHI Guoping. Suitability regionalization of Myrica rubra planting in Zhejiang Province[J]. Remote Sensing for Natural Resources, 2023, 35(2): 236-244.
[3] BO Yingjie, ZENG Yelong, LI Guoqing, CAO Xingwen, YAO Qingxiu. Impacts of floating solar parks on spatial pattern of land surface temperature[J]. Remote Sensing for Natural Resources, 2022, 34(1): 158-168.
[4] ZHAO Longxian, DAI Jingjing, ZHAO Yuanyi, JIANG Qi, LIU Tingyue, FU Minghai. A study of mine site selection of the Duolong ore concentration area in Tibet based on RS and GIS technology[J]. Remote Sensing for Land & Resources, 2021, 33(2): 182-191.
[5] SANG Xiao, GUO Qiaozhen, QIAO Yue, WU Huanhuan, ZANG Jinlong. Research on livability in Changzhi City of Shanxi Province based on multi-source data[J]. Remote Sensing for Land & Resources, 2020, 32(3): 200-207.
[6] Xiaodong ZHANG, Xiangnan LIU, Zhipeng ZHAO, Dan WU, Wenzhong WU, Xiaodong CHU. Geological disaster hazard assessment in Yanchi County based on AHP[J]. Remote Sensing for Land & Resources, 2019, 31(3): 183-192.
[7] Qiao HUANG, Yuling PENG, Wenjie QIN. Research on land use suitability evaluation: A case study of Savan water economic zone in Laos[J]. Remote Sensing for Land & Resources, 2018, 30(4): 156-162.
[8] FAN Xieyu, XING Shihe, YANG Liyang, QIU Longxia, ZHANG Liming. Spatial information service of cultivated land based on OGC standards[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(3): 224-230.
[9] Fan Suying. Application of analytic hierarchy process method to ore-prospecting prognosis in northern Hebei[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(2): 125-131.
[10] WEN Lujun, PENG Wenfu, YANG Huarong, WANG Huaiying, DONG Lijun, SHANG Xue. An analysis of land surface temperature (LST) and its influencing factors in summer in western Sichuan Plateau: A case study of Xichang City[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(2): 207-214.
[11] LIU Jun, ZHAN Ran, SUN Wei. Evaluation of cultivated land suitability for Binhai New Area of Tianjin based on GIS[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(3): 160-165.
[12] YANG Bin, ZHAN Jinfeng, LI Maojiao. Evaluation of environmental vulnerability in the upper reaches of the Minjiang River[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(4): 138-144.
[13] MA Lili, TIAN Shufang, WANG Na. Ecological environment evaluation of the mining area based on AHP and fuzzy mathematics[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(3): 165-170.
[14] GE Jia, ZHANG Zi-ming, WU Cheng, ZHAN Qian, SUN Yong-jun. A Study of Automatical Information Extraction Method of Water-erosion Desertification[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(4): 88-94.
[15] HE Zheng-Min, YAN Yun-Peng, FENG Min, WANG Hong-Rui. The Design and Implementation of Qinghai-Tibet Plateau Environmental and Geological Integrated Appraisement System Based on RS Data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(s1): 30-34.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech