[1]Zhao Xuerui,Han Ling,Liu Ming,et al.Remote Sensing Estimation of Vegetation NPP in Shaanxi Province Based on Improved CASA Model[J].Research of Soil and Water Conservation,2024,31(03):247-256.[doi:10.13869/j.cnki.rswc.2024.03.024]
Copy
Remote Sensing Estimation of Vegetation NPP in Shaanxi Province Based on Improved CASA Model
Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume:
31
Number of periods:
2024 03
Page number:
247-256
Column:
Public date:
2024-04-30
- Title:
- Remote Sensing Estimation of Vegetation NPP in Shaanxi Province Based on Improved CASA Model
- Keywords:
- net primary productivity; CASA Model; TVDI; Shaanxi Province
- CLC:
- Q948; P966.1; TP79
- DOI:
- 10.13869/j.cnki.rswc.2024.03.024
- Abstract:
- [Objective] The aims of this study are to investigate the production status of vegetation communities in Shaanxi Province terrestrial ecosystems, to analyze the changes in spatial and temporal patterns of vegetation NPP and the factors affecting them, and to provide a reference basis for accurately assessing the carbon sources/sinks of the terrestrial ecosystems in Shaanxi Province, realizing the sustainable development of the region's ecology, and reaching the goal of carbon neutrality. [ Methods] Based on the CASA(Carnegie-Ames-Stanford Approach)model water stress factor improved by the Temperature Vegetation Dryness Index(TVDI), the improved CASA model was used to estimate the vegetation NPP in Shaanxi Province from 2010 to 2020, and then the spatial distribution pattern, inter-annual change trend and driving factors were analyzed by hotspot analysis, trend analysis, and GeoDetector. [Results](1)The spatial distribution of NPP in Shaanxi Province showed the characteristics of high level in the south and low level in the north, with obvious differences in cold and hot spot areas.(2)The average value of NPP in Shaanxi Province from 2010 to 2020 ranged from 331.02 to 416.34 gC/(m2·a), with the largest proportion of average NPP values between 100 and 600 gC/(m2·a), and less than 20 percent of the lowest and highest value intervals.(3)83.3 percent of the province's area had no significant change in vegetation NPP values from 2010 to 2020, 4.2 percent showed an increase, and 12.5 percent showed a decreasing trend in NPP values.(4)Precipitation was the single factor dominant driver of vegetation NPP change in Shaanxi Province, with solar radiation and land use type interacting to explain NPP change more strongly. [Conclusion] The CASA model improved by TVDI can effectively quantify the regional vegetation NPP, and there are obvious differences in the distribution of vegetation NPP between north and south in Shaanxi Province, with precipitation, land use type and solar radiation as the main influencing factors.
- References:
-
[1] 朴世龙,方精云,郭庆华.1982—1999年我国植被净第一性生产力及其时空变化[J].北京大学学报:自然科学版,2001,37(4):563-569.
Piao S L, Fang J Y, Guo Q H. Terrestrial net primary production and its spatio-temporal patterns in China during 1982—1999[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2001,37(4):563-569.
[2] Bao G, Bao Y H, Qin Z H, et al. Modeling net primary productivity of terrestrial ecosystems in the semi-arid climate of the Mongolian Plateau using LSWI-based CASA ecosystem model[J]. International Journal of Applied Earth Observation and Geoinformation, 2016,46(4):84-93.
[3] 陆逸.基于MODIS数据的三峡库区净初级生产力遥感估算研究[D].武汉:华中农业大学,2011.
Lu Y. Estimation of vegetation net primary production in the Three Gorges Reservoir Region based on the MODIS data[D]. Wuhan:Huazhong Agricultural University, 2011.
[4] 朱文泉,陈云浩,徐丹,等.陆地植被净初级生产力计算模型研究进展[J].生态学杂志,2005,24(3):296-300.
Zhu W Q, Chen Y H, Xu D, et al. Advances in terrestrial net primary productivity(NPP)estimation models[J]. Chinese Journal of Ecology, 2005,24(3):296-300.
[5] Goetz S J, Prince S D, Goward S N, et al. Satellite remote sensing of primary production:An improved production efficiency modeling approach[J]. Ecological Modelling, 1999,122(3):239-255.
[6] Monteith J L. Solar radiation and productivity in tropical ecosystems[J]. Journal of Applied Ecology, 1972,9(3):747-766.
[7] Potter C S, Randerson J T, Field C B, et al. Terrestrial ecosystem production:A process model based on global satellite and surface data[J]. Global Biogeochemical Cycles, 1993,7(4):811-841.
[8] Xiao F J, Liu Q F, Xu Y Q. Estimation of terrestrial net primary productivity in the Yellow River Basin of China using light use efficiency model[J]. Sustainability, 2022,14(12):7399.
[9] Fang P, Yan N N, Wei P P, et al. Aboveground biomass mapping of crops supported by improved CASA model and Sentinel-2 multispectral imagery[J]. Remote Sensing, 2021,13(14):2755.
[10] 朱文泉.中国陆地生态系统植被净初级生产力遥感估算及其与气候变化关系的研究[D].北京:北京师范大学,2005.
Zhu W Q. Estimation of net primary productivity of Chinese terrestrial vegetation based on remote sensing and its relationship with global climate change[D]. Beijing: Beijing Normal University, 2005.
[11] 郭艳菊,马晓静,许爱云,等.宁夏东部风沙区沙化草地土壤水分和植被的空间特征[J].生态学报,2022,42(4):1571-1581.
Guo Y J, Ma X J, Xu A Y, et al. Spatial characteristics of soil moisture and vegetation in desertification grassland in eastern Ningxia sand-wind area, China[J]. Acta Ecologica Sinica, 2022,42(4):1571-1581.
[12] Jin C, Qin Q M, Zhu L, et al. TVDI based crop yield prediction model for stressed surfaces[C].2007 Ieee International Geoscience and Remote Sensing Symposium, 2007:4656-4658.
[13] 王涛,崔晓临,杨强.陕西省气温降水变化特征的区域差异[J].云南地理环境研究,2013,25(6):27-34,57.
Wang T, Cui X L, Yang Q. Regional difference of temperature and precipitation characteristic in Shaanxi province[J]. Yunnan Geographic Environment Research, 2013,25(6):27-34,57.
[14] Sandholt I, Rasmussen K, Andersen J. A simple interpretation of the surface temperature vegetation index space for assessment of surface moisture status[J]. Remote Sensing of Environment, 2002,79(2/3):213-224.
[15] Getis A, Ord J K. The analysis of spatial association by use of distance statistics[J]. Perspectives on Spatial Data Analysis, 2008:127-145.
[16] 徐建华.现代地理学中的数学方法[M].北京:高等教育出版社,2002.
Xu J H. Mathematical methods in modern geography[M]. Beijng:Higher Education Press, 2002.
[17] 王劲峰,徐成东.地理探测器:原理与展望[J].地理学报,2017,72(1):116-134.
Wang J F, Xu C D. Geodetector:principle and prospective[J]. Acta Geographica Sinica, 2017,72(1):116-134.
[18] 刘文瑞,李晓婷,李彤,等.基于MODIS和CASA模型的伊春市森林植被NPP变化特征及其影响因子分析[J].生态学杂志,2022,41(1):150-158.
Liu W R, Li X T, Li T, et al. Spatiotemporal variations of forest NPP and related driving factors based on MODIS and CASA models in YIchun[J]. Chinese Journal of Ecology, 2022,41(1):150-158.
[19] Tishkov A A, Krenke A N, Titova S V, et al. Variations in the aboveground phytomass in northern Eurasia in the 21 st century[J]. Doklady Earth Science, 2021,497(2):348-353.
[20] 石志华,刘梦云,吴健利,等.基于CASA模型的陕西省植被净初级生产力时空分析[J].水土保持通报,2016,36(1):206-211,345.
Shi Z H, Liu M Y, Wu J L, et al. Spatial-temporal analysis of vegetation net primary productivity in Shaanxi province based on CASA model[J]. Bulletin of Soil and Water Conservation, 2016,36(1):206-211,345.
[21] 李萌萌.基于CASA模型的陕西省NPP反演及其与气象因子的关系[D].南京:南京信息工程大学,2015.
Li M M. NPP retrieval based on CASA model and their correlation with climatic factors in Shaanxi province[D]. Nanjing:Nanjing University of Information Science and Technology, 2015.
[22] 倪向南,郭伟,乔凯.陕北风沙过渡带植被净初级生产力变化特征及原因[J].中国沙漠,2018,38(4):889-898.
Ni X N, Guo W, Qiao K. Spatial and temporal patterns of net primary productivity and their attribution in wind drift sand region in northern Shaanxi[J]. Journal of Desert Research, 2018,38(4):889-898.
[23] Zhu Z Y, Mei Z K, Li S L, et al. Evaluation of ecological carrying capacity and identification of its influencing factors based on remote sensingand geographic information system:A case study of the Yellow River Basin in Shaanxi[J]. Land, 2022,11(7):1080.
[24] 王娟,何慧娟,董金芳,等.黄河流域植被净初级生产力时空特征及自然驱动因子[J].中国沙漠,2021,41(6):213-222.
Wang J, He H J, Dong J F, et al. Spatio-temporal distribution of vegetation net primary productivity in the Yellow River Basin in 2000-2019 and its natural driving factors[J]. Journal of Desert Research, 2021,41(6):213-222.
[25] 王钊,李登科.2000—2015年陕西植被净初级生产力时空分布特征及其驱动因素[J].应用生态学报,2018,29(6):1876-1884.
Wang Z, Li D K. Spatial-temporal distribution of vegetation net primary productivity and its driving factors from 2000 to 2015 in Shaanxi, China[J]. Chinese Journal of Applied Ecology, 2018,29(6):1876-1884.
[26] 梁程,苏惠敏,张伊琳,等.2000—2019年关中平原城市群植被NPP时空变化及影响因素[J].水土保持研究,2023,30(4):293-300.
Liang C, Su H M, Zhang Y L, et al. Spatiotemporal changes and influencing factors of net primary productivity of Guanzhong plain city cluster from 2000 to 2019[J]. Research of Soil and Water Conservation, 2023,30(4):293-300.
[27] 董庆栋,陈超男,殷浩然,等.秦巴山地植被绿度特征及其对地表水热的响应[J].生态学报,2023,43(3):1090-1101.
Dong Q D, Chen C N, Yin H R, et al. Vegetation greenness characteristics in Qinling-Daba Mountains and its response to surface hydrothermal[J]. Acta Ecologica Sinica, 2023,43(3):1090-1101.
[28] 冉琼,张增祥,张国平,等.温度植被干旱指数反演全国土壤湿度的DEM订正[J].中国水土保持科学,2005,3(2):32-36.
Ran Q, Zhang Z X, Zhang G P, et al. DEM correction using TVDI to evaluate soil moisture status in China[J]. Science of Soil and Water Conservation, 2005,3(2):32-36.
[29] Michaletz S T, Cheng D L, Kerkhoff A J, et al. Convergence of terrestrialplant production across global climate gradients[J]. Nature, 2014,512:39-43.
[30] Wei X D, Yang J, Luo P P, et al. Assessment of the variation and influencing factors of vegetation NPP and carbon sink capacity under different natural conditions[J]. Ecological Indicators, 2022,138:108834.
[31] 王娟,何慧娟,卓静,等.基于MODIS数据的陕西省植被净初级生产力与实际蒸散的变化关系分析[J].水土保持通报,2017,37(3):264-269.
Wang J, He H J, Zhuo J, et al. Analysis on relationship between net primary productivity and evapotranspiration based on MODIS data in Shaanxi Province[J]. Bulletin of Soil and Water Conservation, 2017,37(3):264-269.
- Similar References:
Memo
-
Last Update:
2024-04-30