基于Google Earth和MODIS陆地数据的农林地转换对地表温度的影响——以长江中下游及毗邻地区为例

doi:10.18306/dlkxjz.2019.05.007

Abstract

Abstract:

Revealing the impact of land conversion on land surface temperature is of great significance for understanding the climatic and environmental effects of human activities. Statistical analysis based on satellite remote sensing data is an important method to reveal the impact of land use/cover change on land surface temperature. However, in areas with high landscape fragmentation, the mixed pixel problem has become the main limiting factor for the use of this technology, especially in the Yangtze River Basin in southern China. In order to break through this limitation, 200 pairs of pure pixels of cropland and woodland were identified on the 1 km scale based on Google Earth high-definition images. Then, the differences of land surface temperature (LST), leaf area index (LAI), and albedo between cropland and woodland were compared and analyzed by MODIS land data products. The results show that the LST of cropland was higher than that of woodland, and the temperature differences between daytime and nighttime were about 2.75 ℃ and 1.15 ℃, respectively. Daytime temperature difference between cropland and woodland showed double peaks (May and October, with temperature differences about 3.18 ℃ and 3.33 ℃), and nighttime temperature difference showed a single peak (July, about 2.46 ℃). Temperature difference varied from place to place. The highest temperature difference was in the west—in the area bordering Shaanxi and Gansu Provinces, annual average temperature difference was about 3.83 ℃; and temperature difference was the smallest between central and southern Anhui Province (about 1.1 ℃). The difference of LST between cropland and woodland is mainly caused by the difference of evapotranspiration. The LAI of woodland is larger, the evapotranspiration is stronger, the latent heat flux from the surface to the atmosphere is higher, and the sensible heat used to directly heat the surface is relatively less, so the LST is relatively low. The above results show that the conversion of cropland to woodland in the Yangtze River Basin and adjacent areas has a cooling effect by increasing evaporation in recent years.

Key words: cropland and woodland conversion, land surface temperature, middle and lower reaches of the Yangtze River Basin

Caishan ZHAO, Gang ZENG, Lijuan ZHANG, Xuezhen ZHANG. Effects of cropland and woodland conversion on land surface temperature based on Google Earth and MODIS land data: A case study of the middle and lower reaches of the Yangtze River Basin and its adjacent areas[J].PROGRESS IN GEOGRAPHY, 2019, 38(5): 698-708.

Figures/Tables 7

Fig.1

Tab.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

References 36

1	董思言, 延晓冬, 熊喆, 等. 2015. 土地利用/覆盖变化对中国不同季节气温的影响[J]. 生态学报, 35(14): 4871-4879. doi: 10.5846/stxb201407071393
	[Dong S Y, Yan X D, Xiong Z, et al.2015. Impacts of land use/cover change in China on mean temperature. Acta Ecologica Sinica, 35(14): 4871-4879. ] doi: 10.5846/stxb201407071393
2	胡茂桂, 王劲峰. 2010. 遥感影像混合像元分解及超分辨率重建研究进展[J]. 地理科学进展, 29(6): 747-756. doi: 10.11820/dlkxjz.2010.06.015
	[Hu M G, Wang J F.2010. Mixed-pixel decomposition and super-resolution reconstruction of RS image. Progress in Geography, 29(6): 747-756. ] doi: 10.11820/dlkxjz.2010.06.015
3	胡琼, 张建, 徐保东, 等. 2013. Google Earth影像与同源Quick Bird影像在城市土地利用分类上的对比研究[J]. 华中师范大学学报(自然科学版), 47(2): 287-291.
	[Hu Q, Zhang J, Xu B D, et al.2013. A comparison of Google Earth imagery and the homologous Quick Bird imagery being used in land-use classification. Journal of Central China Normal University (Natural Sciences), 47(2): 287-291. ]
4	蓝金辉, 邹金霖, 郝彦爽, 等. 2018. 高光谱遥感影像混合像元分解研究进展[J]. 遥感学报, 22(1): 13-27.
	[Lan J H, Zou J L, Hao Y S, et al.2018. Research progress on unmixing of hyperspectral remote sensing imagery. Journal of Remote Sensing, 22(1): 13-27. ]
5	李升发, 李秀彬. 2016. 耕地撂荒研究进展与展望[J]. 地理学报, 71(3): 370-389.
	[Li S F, Li X B.2016. Progress and prospect on farmland abandonment. Acta Geographica Sinica, 71(3): 370-389. ]
6	李勇, 杨晓光, 代姝玮, 等. 2010. 长江中下游地区农业气候资源时空变化特征[J]. 应用生态学报, 21(11): 2912-2921.
	[Li Y, Yang X G, Dai S W, et al.2010. Spatiotemporal change characteristics of agricultural climate resources in middle and lower reaches of Yangtze River. Chinese Journal of Applied Ecology, 21(11): 2912-1921. ]
7	杨永可, 肖鹏峰, 冯学智, 等. 2014. 大尺度土地覆盖数据集在中国及周边区域的精度评价[J]. 遥感学报, 18(2): 453-475. doi: 10.11834/jrs.20143055
	[Yang Y K, Xiao P F, Feng X Z, et al.2014. Comparison and assessment of large-scale land cover datasets in China and adjacent regions. Journal of Remote Sensing, 18(2): 453-475. ] doi: 10.11834/jrs.20143055
8	袁智. 2013. 植物叶片蒸腾作用模拟 [D]. 合肥: 中国科学技术大学.
	[Yuan Z.2013. The simulation of the traspiration of the plant leaf. Hefei, China: University of Science and Technology of China. ]
9	张方方, 齐述华, 舒晓波, 等. 2010. 南方山地丘陵土地利用类型的地形影响GIS分析: 以江西省为例[J]. 地球信息科学学报, 12(6): 784-790.
	[Zhang F F, Qi S H, Shu X B, et al.2010. Study on the relationship between land use spatial patterns and topographical factors for mountainous region: Taking Jiangxi Province as an exampler. Journal of Geo-information Science, 12(6): 784-790. ]
10	张学珍, 刘纪远, 熊喆, 等. 2015. 20世纪末中国中东部耕地扩张对表面气温影响的模拟[J]. 地理学报, 70(9): 1423-1433.
	[Zhang X X, Liu J Y, Xiong Z, et al.2015. Simulated effects of agricultural development on surface air temperature over central and eastern China in the late 20th century. Acta Geographica Sinica, 70(9): 1423-1433. ]
11	张学珍, 赵彩杉, 董金玮, 等. 2019. 1992—2017 年基于荟萃分析的中国耕地撂荒时空特征[J]. 地理学报, 74(3): 411-420.
	[Zhang X Z, Zhao C S, Ding J W, et al.2019. Spatiotemporal pattern of cropland abandonment in China in the last three decades: A meta-analysis. Acta Geographica Sinica, 74(3): 411-420. ]
12	Arendt A, Luthcke S, Gardner A, et al.2013. Analysis of a GRACE global mascon solution for Gulf of Alaska glaciers[J]. Journal of Glaciology, 59: 913-924. doi: 10.3189/2013JoG12J197
13	Dong J W, Liu J Y, Yan H M, et al.2011. Spatio-temporal pattern and rationality of land reclamation and cropland abandonment in mid-eastern Inner Mongolia of China in 1990-2005[J]. Environmental Monitoring and Assessment, 179: 137-153. doi: 10.1007/s10661-010-1724-9
14	Essa W, Verbeiren B, Kwast J V D, et al.2017. Improved distrad for downscaling thermal MODIS imagery over urban areas[J]. Remote Sensing, 9(12), doi: 10.3390/rs9121243.
15	Foley J A, Ruth D, Gregory P A, et al.2005. Global consequences of land use[J]. Science, 309: 570-574. doi:10.1126/science.1111772. doi: 10.1126/science.1111772
16	Golestaneh P, Zekri M, Sheikholeslam F.2018. Fuzzy wavelet extreme learning machine[J]. Fuzzy Sets & Systems, 342: 90-108. doi: 10.1016/j.fss.2017.12.006.
17	Jin D C, Guan Z Y.2017. Summer rainfall seesaw between Hetao and the middle and lower reaches of the Yangtze River and its relationship with the North Atlantic Oscillation[J]. Journal of Climate, 30(17): 6629-6643. doi: 10.1175/JCLI-D-16-0760.1
18	Karlsson I B, Sonnenborg T O, Refsgaard J C, et al.2016. Combined effects of climate models, hydrological model structures and land use scenarios on hydrological impacts of climate change[J]. Journal of Hydrology, 535: 301-317. doi: 10.1016/j.jhydrol.2016.01.069
19	Knyazikhin Y, Martonchik J V, Myneni R B, et al.1998. Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data[J]. Journal of Geophysical Research Atmospheres, 103(D24): 32257-32275. doi: 10.1029/98JD02462
20	Lee X H, Goulden M L, Hollinger D Y, et al.2011. Observed increase in local cooling effect of deforestation at higher latitudes[J]. Nature, 479: 384-387. doi: 10.1038/nature10588
21	Li J Y, Dodson J, Yan H, et al.2018. Quantitative Holocene climatic reconstructions for the lower Yangtze region of China[J]. Climate Dynamics, 50(3-4): 1101-1113. doi: 10.1007/s00382-017-3664-3
22	Li S F, He F N, Zhang X Z.2016. A spatially explicit reconstruction of cropland cover in China from 1661 to 1996[J]. Regional Environmental Change, 16(2): 417-428. doi: 10.1007/s10113-014-0751-4
23	Li X X, Zhang X Z, Zhang L J.2017. Observed effects of vegetation growth on temperature in the early summer over the Northeast China Plain[J]. Atmosphere, 8(6). doi: 10.3390/atmos8060097.
24	Ludwig A, Meyer H, Nauss T.2016. Automatic classification of Google Earth images for a larger scale monitoring of bush encroachment in South Africa[J]. International Journal of Applied Earth Observations & Geoinformation, 50: 89-94.
25	Mahmood R, Pielke R A, Hubbard K G, et al.2014. Land cover changes and their biogeophysical effects on climate[J]. International Journal of Climatology, 34(4): 929-953. doi: 10.1002/joc.2014.34.issue-4
26	Newbold T, Hudson L N, Hill S L L, et al.2015. Global effects of land use on local terrestrial biodiversity[J]. Nature, 520: 45-50. doi: 10.1038/nature14324
27	Pei F S, Wu C J, Qu A, et al.2017. Changes in extreme precipitation: A case study in the middle and lower reaches of the Yangtze River in China[J]. Water, 9(12). doi: 10.3390/w9120943.
28	Peng S S, Piao S L, Zeng Z Z, et al.2014. Afforestation in China cools local land surface temperature[J]. PNAS, 111(8): 2915-2919. doi: 10.1073/pnas.1315126111
29	Ramankutty N, Foley J A.1999. Estimating historical changes in global land cover: Croplands from 1700 to 1992[J]. Global Biogeochemical Cycles, 13: 997-1028. doi: 10.1029/1999GB900046
30	Schaaf C B, Gao F, Strahler A H, et al.2002. First operational BRDF, albedo nadir reflectance products from MODIS[J]. Remote Sensing of Environment, 83(1): 135-148. doi: 10.1016/S0034-4257(02)00091-3
31	Turner II B L, Lambin E F, Reenberg A.2007. The emergence of land change science for global environmental change and sustainability[J]. PNAS, 104(52): 20666-20671. doi: 10.1073/pnas.0704119104
32	Wan Z M, Li Z L.1997. A physics-based algorithm for retrieving land-surface emissivity and temperature from EOS/MODIS data[J]. IEEE Transactions on Geoscience and Remote Sensing, 35(4): 980-996. doi: 10.1109/36.602541
33	Wang Y Y, Li G.2014. Analysis of “furnace cities" in China using MODIS/LST product (MOD11A2)[C]// Geoscience and Remote Sensing Symposium. IEEE, 2014: 1817-1820.
34	Yan J Z, Yang Z Y, Li Z H, et al.2016. Drivers of cropland abandonment in mountainous areas: A household decision model on farming scale in Southwest China[J]. Land Use Policy, 57: 459-469. doi:10.1016/j.landusepol.2016.06.014. doi: 10.1016/j.landusepol.2016.06.014
35	Yebra M, Dijk A V, Leuning R, et al.2013. Evaluation of optical remote sensing to estimate actual evapotranspiration and canopy conductance[J]. Remote Sensing of Environment, 129(2): 250-261. doi: 10.1016/j.rse.2012.11.004
36	Zhang X Z, Tang Q H, Zheng, J Y, et al.2013. Warming/cooling effects of cropland greenness changes during 1982-2006 in the North China Plain[J]. Environmental Research Letters, 8(2). doi: 10.1088/1748-9326/8/2/024038.

地表参数	一级		二级		三级		四级		五级
地表参数	耕地	林地	耕地	林地	耕地	林地	耕地	林地	耕地	林地
LST(昼)	56.43	51.63	22.86	26.21	47.53	45.82	-	-	-	-
LST(夜)	56.95	51.66	23.22	27.05	46.07	44.03	-	-	-	-
LAI	91.95	71.80	1.72	11.43	0	0	6.34	15.76	0	1.00
Albedo	8.22	11.08	44.90	51.38	51.08	42.22	-	-	-	-

[1]	SHI Xiao, WANG Guojie, SUN Ming, LI Yvtao, WANG Boni, SHEN Jie. Evaluation of the long-term high-resolution infrared radiation sounder land surface temperature during 1980-2009 in Jiangsu Province, China [J]. PROGRESS IN GEOGRAPHY, 2020, 39(8): 1283-1295.
[2]	GAO Jing, GONG Jian, LI Jingye. Effects of source and sink landscape pattern on land surface temperature: An urban heat island study in Wuhan City [J]. PROGRESS IN GEOGRAPHY, 2019, 38(11): 1770-1782.
[3]	Yinghui JIANG, Limin JIAO, Boen ZHANG. Scale effect of the spatial correlation between urban land surface temperature and NDVI [J]. PROGRESS IN GEOGRAPHY, 2018, 37(10): 1362-1370.
[4]	Bin LI, Huimin WANG, Mingzhou QIN, Pengyan ZHANG. Comparative study on the correlations between NDVI, NDMI and LST [J]. PROGRESS IN GEOGRAPHY, 2017, 36(5): 585-596.
[5]	Yuanzheng LI, Ke YIN, Hongxuan ZHOU, Xiaolin WANG, Dan HU. Progress in urban heat island monitoring by remote sensing [J]. PROGRESS IN GEOGRAPHY, 2016, 35(9): 1062-1074.
[6]	JIA Yuanyuan,LI Zhaoliang. Progr ess in Land Sur face Temper atur e Retr ieval from Passive Microwave Remotely Sensed Data [J]. PROGRESS IN GEOGRAPHY, 2006, 25(3): 96-105.
[7]	LIU Zhiwu, DANG Anrong, LEI Zhidong, HUANG Yugang. A Retrieval Model of Land Surface Temperature With ASTER Data and Its Application Study [J]. PROGRESS IN GEOGRAPHY, 2003, 22(5): 507-514.

Effects of cropland and woodland conversion on land surface temperature based on Google Earth and MODIS land data: A case study of the middle and lower reaches of the Yangtze River Basin and its adjacent areas

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 7

References 36

Related Articles 7

Metrics

Comments

Recommended 0