Please wait a minute...
 
Remote Sensing for Natural Resources    2024, Vol. 36 Issue (4) : 260-271     DOI: 10.6046/zrzyyg.2023189
|
Impacts of changes in land cover on solar radiation absorption in northwestern China
SHI Ying1(), BIE Qiang1,2,3(), SU Xiaojie1, LI Xinzhang1
1. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
2. National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China
3. Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China
Download: PDF(8372 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

The changes in land cover types will affect the amount of solar radiant energy absorbed by the land surface and then influence the radiative equilibrium of the surface ecosystem. Under the background of dramatic changes in land cover, the patterns and changes of the solar radiation absorptivity of land exert significant influence on the thermal equilibrium of the land surface. Based on the MODIS MCD12Q1 land cover data, the MCD43A3 surface albedo data, and the MCD43A2 solar zenith angle data from 2001 to 2020, along with two adjacent phases of spatiotemporal changes in the surface cover types and the solar radiant energy absorbed by land surface across northwestern China, this study analyzed and explored the impacts of the changes in land cover types on solar radiation absorption. The results indicate that the changes in land cover types in the study area are primarily characterized by reduced bare land area and the expansion of other land cover types, with the largest areal change occurring in the shift from bare land to grassland. Different types of land cover display varying solar radiation absorptivities. Water bodies exhibit the greatest solar radiation absorptivity, followed by woodland, cultivated land, grassland, and construction land, with bare land and permanent ice and snow presenting the poorest solar radiation absorptivities. The conversion of land cover types will lead to different radiation absorptivities. Specifically, the transfer from grassland, cultivated land, bare land, and permanent ice and snow primarily exhibits an increasing trend in solar radiation absorptivity, while that of water bodies and forest land largely displays a decreasing trend. The same land cover type differs in the time series of solar radiation absorption, which primarily increases for construction land, grassland, cultivated land, bare land, and water bodies but decreases for woodland and permanent ice and snow. The results of this study will provide a scientific basis and reference for research on climatic change, ecological construction, and sustainable development in northwestern China.

Keywords change in surface cover      blue sky albedo      solar radiation absorptivity      northwestern China     
ZTFLH:  TP79  
Issue Date: 23 December 2024
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Ying SHI
Qiang BIE
Xiaojie SU
Xinzhang LI
Cite this article:   
Ying SHI,Qiang BIE,Xiaojie SU, et al. Impacts of changes in land cover on solar radiation absorption in northwestern China[J]. Remote Sensing for Natural Resources, 2024, 36(4): 260-271.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023189     OR     https://www.gtzyyg.com/EN/Y2024/V36/I4/260
Fig.1  Flow chart
Fig.2  Location of the study area
归并后 描述 对应IGBP地表覆被类型
林地 1 树木覆盖率>60% 1常绿针叶林、2常绿阔叶林、3落叶针叶林、4落叶阔叶林、5混交林、6郁闭灌木丛、7开放灌木丛
草地 2 草本植物为主,树木
覆盖率10%~60%
8多树草地、9稀树草地、10草地
耕地 3 种植农作物土地 12耕地、14农田/自然植被
水体 4 永久湿地与水域 11永久湿地、17水域
建设用地 5 居民点、交通等用地 13城市区和建筑用地
永久冰雪 6 冰与积雪 15永久冰雪
裸地 7 未利用土地 16裸地
Tab.1  Reclassify information
Fig.3-1  Spatial distribution of land cover classification
Fig.3-2  Spatial distribution of land cover classification
时间 类型
林地 草地 耕地 水体 建设用地 永久冰雪 裸地
2001年 68 418.15 1 135 276.41 151 978.12 16 918.54 8 129.77 15 056.37 1 613 118.60
2005年 71 313.26 1 135 107.04 159 211.47 16 846.20 8 192.02 22 361.85 1 595 864.12
2010年 76 573.25 1 135 563.62 170 331.43 17 215.41 8 288.61 21 068.10 1 579 855.52
2015年 79 796.14 1 132 188.33 180 304.04 17 715.99 8 474.08 22 984.58 1 567 432.80
2020年 87 243.72 1 146 912.84 180 416.95 18 555.95 8 690.67 25 400.34 1 541 675.48
Tab.2  Changes in the area of different land cover types(km2)
2001年 2020年
林地 草地 耕地 水体 建设用地 永久冰雪 裸地 合计
林地 2 691.18 24.9 41.43 0 60.75 79.21 2 897.47
草地 18 465.15 47 131.31 952.23 196.63 172.8 14 965.57 81 883.69
耕地 2 497.34 18 492.2 29.62 355.69 0 27.69 21 402.54
水体 98.74 291.72 12.88 0 309.32 752.16 1 464.82
建设用地 0 0 0 0 0 0 0
永久冰雪 0.64 62.04 0.86 10.52 0 1 600.49 1 674.55
裸地 661.15 71 982.99 2 671.43 2 068.45 8.59 11 475.65 88 868.26
合计 21 723.02 93 520.13 49 841.38 3 102.25 560.91 12 018.52 17 425.12 198 191.33
  
Fig.4-1  Spatial distribution of land cover classification changes
Fig.4-2  Spatial distribution of land cover classification changes
Fig.5  Changes in land cover type shifts during 2001—2020
Fig.6  Spatial distribution of solar radiation absorbed by the Earth’s surface
Fig.7  Numerical ranges of average annual absorption of solar radiation by different land cover types
Fig.8  Spatial distribution of surface albedo changes
Fig.9  Spatial distribution of changes in solar radiation absorption by different surface types
Fig.10  Changes in solar radiation absorption by different surface types
[1] 张莹, 姬潇然, 王谋. 国际气候治理中的公正转型议题:概念辨析与治理进展[J]. 气候变化研究进展, 2021, 17(2):245-254.
[1] Zhang Y, Ji X R, Wang M. Just transition in climate regime:Concept development and governance progress[J]. Climate Change Research, 2021, 17(2):245-254.
[2] Searchinger T, Hanson C, Ranganathan J, et al. Creating a sustainable food future:A menu of solutions to sustainably feed more than 9 billion people by 2050[J]. World Resources Report, 2013.
[3] IPCC. Fourth assessment report (AR4).Climate change 2007:The physical science basis[R]. New York: Cambridge University Press, 2007.
[4] 李义平, 蔡宏, 田鹏举, 等. 贵州省黎平县地表覆被变化引起的生态系统碳储量变化[J]. 水土保持通报, 2020, 40(2):92-99,325.
[4] Li Y P, Cai H, Tian P J, et al. Changes in ecosystem carbon stock caused by land cover conversion in Liping County of Guizhou Province[J]. Bulletin of Soil and Water Conservation, 2020, 40(2):92-99,325.
[5] Metz B, Davidson O R, Bosch P R, et al. Climate change 2007:Mitigation.contribution of working group III to the fourth assessment report of the intergovernmental panel on climate change[J]. Computational Geometry, 2007, 18(2):95-123.
[6] Searchinger T D, Wirsenius S, Beringer T, et al. Assessing the efficiency of changes in land use for mitigating climate change[J]. Nature, 2018, 564(7735):249-253.
[7] 杨骁. 宁夏中南部地区土地利用变化与生态脆弱性响应[D]. 银川: 宁夏大学, 2022.
[7] Yang X. Land use change and ecological vulnerability response in central and southern Ningxia[D]. Yinchuan: Ningxia University, 2022.
[8] 宋庆丰. 中国近40年森林资源变迁动态对生态功能的影响研究[D]. 北京: 中国林业科学研究院, 2015.
[8] Song Q F. Study on impact of forest resource dynamic change on forest ecological function in recent 40 years in China[D]. Beijing: Chinese Academy of Forestry, 2015.
[9] 毛江涛, 徐文婷, 谢宗强. 森林碳汇的研究热点与趋势——基于知识图谱分析[J]. 生态学报, 2023, 43(19):8241-8253.
[9] Mao J T, Xu W T, Xie Z Q. Trends and hotspots of forest carbon sinks based on the knowledge map[J]. Acta Ecologica Sinica, 2023, 43(19):8241-8253.
[10] Ma H Y, Zhang L L, Wei X Q, et al. Spatial and temporal variations of land use and vegetation cover in Southwest China from 2000 to 2015[J]. The Journal of Applied Ecology, 2021, 32(2):618-628.
[11] 吴晓, 闵敏, 董立新. 从Himawari08卫星估算晴空地表长波辐射及其日变化特征初探[J]. 气候与环境研究, 2018, 23(1):37-46.
[11] Wu X, Min M, Dong L X. Estimation and diurnal variation analysis of clear-sky surface longwave radiation fluxes from Himawari08 satellite[J]. Climatic and Environmental Research, 2018, 23(1):37-46.
[12] 申彦波, 赵宗慈, 石广玉. 地面太阳辐射的变化、影响因子及其可能的气候效应最新研究进展[J]. 地球科学进展, 2008, 23(9):915-923.
doi: 10.11867/j.issn.1001-8166.2008.09.0915
[12] Shen Y B, Zhao Z C, Shi G Y. The progress in variation of surface solar radiation,factors and probable climatic effects[J]. Advances in Earth Science, 2008, 23(9):915-923.
[13] 权维俊. 中国大陆地区地表净太阳辐射的卫星遥感研究[D]. 南京: 南京气象学院, 2003.
[13] Quan W J. Satellite remote sensing study on net surface solar radiation on the surface of Chinese mainland area[D]. Nanjing University of Information Science Technology, 2003.
[14] 陈峰云. 湖北省土地利用/覆被变化及其对自然环境要素的影响[D]. 武汉: 华中农业大学, 2009.
[14] Chen F Y. Study on land use/cover change and its effects of natural environment elements in Hubei Province[D]. Wuhan: Huazhong Agricultural University, 2009.
[15] Williamson S N, Barrio I C, Hik D S, et al. Phenology and species determine growing-season albedo increase at the altitudinal limit of shrub growth in the sub-Arctic[J]. Global Change Biology, 2016, 22(11):3621-3631.
doi: 10.1111/gcb.13297 pmid: 27158930
[16] Brovkin V, Raddatz T, Reick C H, et al. Global biogeophysical interactions between forest and climate[J]. Geophysical Research Letters, 2009, 36(7):L07405.
[17] 冯起, 白光祖, 李宗省, 等. 加快构建西北地区生态保护新格局[J]. 中国科学院院刊, 2022, 37(10):1457-1470.
[17] Feng Q, Bai G Z, Li Z X, et al. Accelerate construction of new pattern of ecological protection in Northwest China[J]. Bulletin of Chinese Academy of Sciences, 2022, 37(10):1457-1470.
[18] 曹永强, 路洁. 国内外气象干旱研究现状与前沿分析[J]. 中国防汛抗旱, 2021, 31(3):1-7.
[18] Cao Y Q, Lu J. Research status and frontier analysis of meteorological drought at domestic and abroad[J]. China Flood & Drought Management, 2021, 31(3):1-7.
[19] 韦振锋, 任志远, 张翀, 等. 西北地区植被覆盖变化及其与降水和气温的相关性[J]. 水土保持通报, 2014, 34(3):283-289.
[19] Wei Z F, Ren Z Y, Zhang C, et al. Changes of vegetation cover and its correlation with precipitation and temperature in Northwest China[J]. Bulletin of Soil and Water Conservation, 2014, 34(3):283-289.
[20] 翟俊, 刘荣高, 刘纪远, 等. 1990—2010年中国土地覆被变化引起反照率改变的辐射强迫[J]. 地理学报, 2013, 68(7):875-885.
[20] Zhai J, Liu R G, Liu J Y, et al. Radiative forcing over China due to albedo change caused by land cover change during 1990—2010[J]. Acta Geographica Sinica, 2013, 68(7):875-885.
[21] 王倩. 基于土地覆被变化的秦淮地区辐射强迫研究[D]. 西安: 西安科技大学, 2019.
[21] Wang Q. Study on radiative forcing of land cover change in Qinhuai area[D]. Xi’an: Xi’an University of Science and Technology, 2019.
[22] 杜一博. 西北地区陆面过程与大气边界层相互作用及其对夏季风进退的响应[D]. 兰州: 兰州大学, 2018.
[22] Du Y B. The interaction between land surface and atmospheric boundary layer in northwest region and its response to the summer monsoon[D]. Lanzhou: Lanzhou University, 2018.
[23] 姜田亮, 粟晓玲, 郭盛明, 等. 西北地区植被耗水量的时空变化规律及其对气象干旱的响应[J]. 水利学报, 2021, 52(2):229-240.
[23] Jiang T L, Su X L, Guo S M, et al. Spatiotemporal variation of vegetation water consumption and its response to meteorological drought in Northwest China[J]. Journal of Hydraulic Engineering, 2021, 52(2):229-240.
[24] 陈少勇, 邢晓宾, 张康林, 等. 中国西北地区太阳总辐射的气候特征[J]. 资源科学, 2010, 32(8):1444-1451.
[24] Chen S Y, Xing X B, Zhang K L, et al. Climatic characteristics of gross solar radiation over Northwestern China[J]. Resources Science, 2010, 32(8):1444-1451.
[25] 李祥东. 西北干旱区土壤水分时空变异特征及其影响因素研究[D]. 北京: 中国科学院大学(中国科学院教育部水土保持与生态环境研究中心), 2019.
[25] Li X D. Spatial-temporal variability of soil moisture and influencing factors in northwest arid area of China[D]. Beijing: Research Center for Eco-Environmental Sciences,University of Chinese Academy of Sciences, 2019.
[26] 苗文辉. 西北地区植被变化对中国夏季气候的影响[D]. 兰州: 兰州大学, 2018.
[26] Miao W H. Effects of vegetation changes in Northwest China on summer climate in China[D]. Lanzhou: Lanzhou University, 2018.
[27] 李小军, 辛晓洲, 彭志晴. 2003—2012年中国地表太阳辐射时空变化及其影响因子[J]. 太阳能学报, 2017, 38(11):3057-3066.
[27] Li X J, Xin X Z, Peng Z Q. Change analysis of surface solar radiation in China from 2003 to 2012[J]. Acta Energiae Solaris Sinica, 2017, 38(11):3057-3066.
[28] 刘沁萍, 朱闯, 田洪阵, 等. 基于MODIS数据的中国耕地时空变化及其影响因素[J]. 兰州大学学报(自然科学版), 2022, 58(5):689-697.
[28] Liu Q P, Zhu C, Tian H Z, et al. Spatio-temporal changes in cultivated land and their influencing factors in China based on MODIS data[J]. Journal of Lanzhou University(Natural Sciences), 2022, 58(5):689-697.
[29] Peng S S, Piao S, Zeng Z, et al. Afforestation in China cools local land surface temperature[J]. Proceedings of the National Academy of Sciences of the United States of America, 2014, 111(8):2915-2919.
[30] Luyssaert S, Jammet M, Stoy P C, et al. Land management and land-cover change have impacts of similar magnitude on surface temperature[J]. Nature Climate Change, 2014, 4(5):389-393.
[31] Li Y, Zhao M, Motesharrei S, et al. Local cooling and warming effects of forests based on satellite observations[J]. Nature Communications, 2015, 6:6603.
doi: 10.1038/ncomms7603 pmid: 25824529
[32] Lewis P, Barnsley M J. Influence of the sky radiance distribution on various formulations of the Earth surface albedo[J]. International Symposium on Physical Measurements and Signatures in Remote Sensing Isprs,1994.
[33] Stokes G M, Schwartz S E. The atmospheric radiation measurement (ARM) program:Programmatic background and design of the cloud and radiation test bed[J]. Bulletin of the American Meteorological Society, 1994, 75(7):1201-1221.
[34] 曹美春, 林朝晖, 张贺. 太阳常数变化对冬季全球辐射强迫及气候影响的数值模拟研究[J]. 气象科技进展, 2014, 4(4):38-43.
[34] Cao M C, Lin Z H, Zhang H. A modeling study of the response of radiative forcing and climate to solar constant variation in boreal winter[J]. Advances in Meteorological Science and Technology, 2014, 4(4):38-43.
[35] 刘斌, 张琴琴, 辛海强, 等. 塔里木河流域2000—2014年地表覆盖动态变化监测[J]. 测绘科学, 2018, 43(5):45-49.
[35] Liu B, Zhang Q Q, Xin H Q, et al. Dynamic change monitoring on surface cover in Tarim River Basin during 2000 to 2014[J]. Science of Surveying and Mapping, 2018, 43(5):45-49.
[36] 胡云锋, 商令杰, 张千力, 等. 基于GEE平台的1990年以来北京市土地变化格局及驱动机制分析[J]. 遥感技术与应用, 2018, 33(4):573-583.
doi: 10.11873/j.issn.1004-0323.2018.4.0573
[36] Hu Y F, Shang L J, Zhang Q L, et al. Land change patterns and driving mechanism in Beijing since 1990 based on GEE platform[J]. Remote Sensing Technology and Application, 2018, 33(4):573-583.
[37] 姚玉璧, 郑绍忠, 董宏昌, 等. 中国西北地区太阳辐射时空分异特征[J]. 干旱区研究, 2023, 40(6):863-873.
doi: 10.13866/j.azr.2023.06.02
[37] Yao Y B, Zheng S Z, Dong H C, et al. Anomaly temporal-spatial distribution of solar radiation in Northwest China[J]. Arid Zone Research, 2023, 40(6):863-873.
doi: 10.13866/j.azr.2023.06.02
[38] 高健, 冯鹏飞, 张绘芳, 等. 基于主被动遥感技术的地表覆被类型变化检测方法——以新疆西天山国家级自然保护区为例[J]. 西北林学院学报, 2020, 35(4):140-147.
[38] Gao J, Feng P F, Zhang H F, et al. Detection method of surface cover type change based on active and passive remote sensing technology:A case study of the west Tianshan national nature reserve in Xinjiang[J]. Journal of Northwest Forestry University, 2020, 35(4):140-147.
[39] 刘星雨, 种培芳. 基于GIS分析的西北地区土地利用与景观格局演变研究——以甘肃省兰州市为例[J]. 园林, 2021, 38(4):89-97.
[39] Liu X Y, Zhong P F. Study on the evolution of land use and landscape pattern in northwest China Based on GIS:Taking Lanzhou City of Gansu Province as an example[J]. Landscape Architecture Academic Journal, 2021, 38(4):89-97.
[40] 马梅, 张圣微, 魏宝成. 锡林郭勒草原近30年草地退化的变化特征及其驱动因素分析[J]. 中国草地学报, 2017, 39(4):86-93.
[40] Ma M, Zhang S W, Wei B C. Temporal and spatial pattern of grassland degradation and its determinants for recent 30 years in Xilingol[J]. Chinese Journal of Grassland, 2017, 39(4):86-93.
[41] 程倩豪. 城市天空可视因子对表面温度影响关系的尺度效应研究[D]. 北京: 北京建筑大学, 2022.
[41] Cheng Q H. A study of the scale effect of the relationship between urban sky view factor on surface temperature[D]. Beijing: Beijing University of Civil Engineering and Architecture, 2022.
[1] FENG Lei, WANG Yi, LI Wenji, WANG Yanzuo, ZHENG Xiangxiang, WANG Shanshan, ZHANG Ling. Development and application of 3D geological hazard identification information platform[J]. Remote Sensing for Natural Resources, 2024, 36(4): 321-327.
[2] ZHAO Hairong, MO Hongwei. Dynamic and natural driving factors of vegetation carbon sink in Hunan Province[J]. Remote Sensing for Natural Resources, 2024, 36(4): 304-313.
[3] NIU Zhensheng, YANG Xin, CHEN Chao, LIAO Xiang, ZHANG Xiaoxuan. Analysis of changes in the economic development characteristics of the Chengdu-Chongqing urban agglomeration using remote sensing data on nighttime light[J]. Remote Sensing for Natural Resources, 2024, 36(4): 272-281.
[4] ZHANG Chungui, WU Zuohang, WANG Jing, CHEN Wenjia. Analysis and application of spatiotemporal variation in sea surface temperature in the Taiwan Strait based on Himawari-8 data[J]. Remote Sensing for Natural Resources, 2024, 36(4): 175-184.
[5] LONG Wenqin, ZHI Lu, GUO Yadi, ZOU Bin, ZENG Lizhi, GAO Hao. Spatiotemporal evolution and origin of carbon stock in the West Dongting Lake National Nature Reserve over the last two decades[J]. Remote Sensing for Natural Resources, 2024, 36(4): 185-192.
[6] ZHAO Jinling, HUANG Jian, LIANG Zijun, ZHAO Xuedan, JIN Tao, GE Hanghang, WEI Xiaoyan, SHAO Yuanzheng. BDANet-based assessment of building damage from earthquake disasters[J]. Remote Sensing for Natural Resources, 2024, 36(4): 193-200.
[7] SU Tengfei. A comparative study on semantic segmentation-orientated deep convolutional networks for remote sensing image-based farmland classification: A case study of the Hetao irrigation district[J]. Remote Sensing for Natural Resources, 2024, 36(4): 210-217.
[8] ZHUANG Huifu, WANG Peng, SU Yanan, ZHANG Xiang, FAN Hongdong. Dynamic monitoring of flood inundation in Zhuozhou, Hebei Province based on multi-temporal SAR data[J]. Remote Sensing for Natural Resources, 2024, 36(4): 218-228.
[9] WEI Xiao, ZHANG Lifeng, HE Yi, CAO Shengpeng, SUN Qiang, GAO Binghai. 2000-2020 spatiotemporal variations of different vegetation types in the Yellow River basin and influencing factors[J]. Remote Sensing for Natural Resources, 2024, 36(4): 229-241.
[10] ZHAO Yuling, YANG Jinzhong, SUN Weidong, YU Hao, XING Yu, CHEN Dong, MA Xinying, WANG Tixin, WANG Cong. Evaluation and analysis of geological environment of mines in the Ili Valley and countermeasures for ecological restoration and management[J]. Remote Sensing for Natural Resources, 2024, 36(4): 23-30.
[11] LU Junjing, SUN Leigang, ZUO Lu, LIU Jianfeng, MA Xiaoqian, HAO Qingtao. A functional zoning-based study of the spatiotemporal evolutionary characteristics and influencing factors of vegetation fractional cover in the Beijing-Tianjin-Hebei region[J]. Remote Sensing for Natural Resources, 2024, 36(4): 242-253.
[12] HAN Jing, YANG Shuai, YANG Tao, ZHU Nannan, MA Yudong, ZHANG Wenlong. Identification of potential landslide hazards in the Ankang area using L-band differential interferometric SAR satellite[J]. Remote Sensing for Natural Resources, 2024, 36(4): 254-259.
[13] YU Wenxuan, LI Yimin, JI Peikun, FENG Xianjie, XIANG Qianying. Time series surface deformation of the upper reaches of Huangdeng hydropower station in Lanping County based on ascending and descending SAR data[J]. Remote Sensing for Natural Resources, 2024, 36(4): 282-294.
[14] MIAO Chenyang, CHEN Jian, MA Benhao. Inversion of aerosol optical depth in Nanjing City based on data field method[J]. Remote Sensing for Natural Resources, 2024, 36(4): 295-303.
[15] QI Changwei, DONG Ji’en, CHENG Xu, YE Gaofeng, HE Shuyue, DAI Wei, WANG Bing. Application of ZY-1 02D hyperspectral data to altered mineral mapping and ore prospecting in desert areas along the northern margin of the Qaidam Basin[J]. Remote Sensing for Natural Resources, 2024, 36(4): 31-42.
Viewed
Full text


Abstract

Cited

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