Study on Estimation of Net Primary Productivity Based on High Resolution Remote Sensing Image and Its Influencing Factors in Yinchuan City
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摘要: 植被净初级生产力(NPP)是监测区域陆地生态系统碳循环过程,评估区域碳源、汇的重要因子。估算植被NPP,揭示其演变特征及其对自然人文因子的响应对于区域生态系统可持续发展具有重要意义。以银川市为例,基于CASA模型利用亚米级高分辨率遥感影像、气候、土地利用等数据估算研究区2015年和2021年不同季度植被NPP并分析其时空演变特征,采用梯度提升决策树(GBDT)模型揭示自然因子、人类活动以及土地利用对植被NPP的非线性响应和阈值效应。结果表明: 1)2021年银川市植被NPP在0-753.951 g C·m-2·a-1,高值区集中在黄河沿岸农业灌溉区。利用高分辨率影像估算植被NPP,可得到田块尺度和城市街区尺度植被NPP空间分布的细节图,提高估算精度。2)银川市植被NPP随季节的变化趋势与植被物候和农作物生长周期一致。2015-2021年第三季度植被NPP整体呈增加趋势,但城市扩张占用耕地导致局部NPP下降显著,而生态保护区内植被NPP上升显著。3) NDVI、土壤有机碳密度、高程、气温和降雨等自然因子对植被NPP存在显著阈值效益,其中NDVI的相对贡献最大,但超过0.6后,对植被NPP的提升作用不明显。不同土地利用类型对植被NPP的非线性影响存在差异,如旱地对NPP的影响阈值高于水田,建设用地中城市绿色基础达到一定比例时对植被NPP起正向作用,即调控土地利用结构和布局对于植被NPP的提升至关重要。当人类活动在一定范围内时对植被NPP有一定程度的促进作用,而当人口数超过阈值后,人口越大,生态承载力越低,植被NPP则会下降。Abstract: Objectives: Net primary productivity (NPP) of vegetation is an important factor to monitor the carbon cycle process of regional terrestrial ecosystem and regional carbon sources and sinks. Estimating NPP of vegetation and revealing its evolution characteristics and its response to natural and human factors are of great significance to the sustainable development of regional ecosystem. Methods: Taking Yinchuan as an example, based on Carnegie-Ames-Stanford Approach(CASA) model combined with sub-meter high-resolution remote sensing images, climate, land use and other data, the NPP of vegetation in different seasons from 2015 to 2021 in the study area are estimated and its temporal and spatial evolution characteristics are analyzed. Gradient Boosting Decision Tree (GBDT) model is used to reveal the nonlinear response and threshold effect of natural factors, human activities and land use on NPP of vegetation. Results: 1) The NPP of vegetation in Yinchuan was 0-753.951 g C·m-2·a-1, and the high value areas were concentrated in the agricultural irrigation areas along the Yellow River. The detailed spatial distribution maps of NPP of field scale and urban block scale were obtained with high-resolution images used in the estimation of NPP, which also improves the estimation accuracy. 2) The seasonal variation trend of vegetation NPP in Yinchuan was consistent with vegetation phenology and crop growth cycle. In the third quarter of 2015-2021, the NPP of vegetation showed an overall increasing trend, while the local NPP decreased significantly due to the occupation of cultivated land by urban expansion, while the NPP of vegetation in ecological protection areas increased significantly. 3) Natural factors such as NDVI soil organic carbon density, elevation, rainfall, etc. had significant threshold effects on NPP, and the relative contribution of NDVI is the largest, when it exceeded 0.6, it had no obvious promotion effect on NPP of vegetation. The threshold of NPP of dry land was higher than that of paddy field, and construction land played a positive role in NPP when urban green foundation in the construction land reaches a certain proportion. When human activities are in a certain range, they can promote NPP of vegetation to a certain extent. When the population exceeds the threshold, the larger the population, the lower the ecological carrying capacity, and the lower the NPP of vegetation. Conclusions: In this study, high-resolution images are used to estimate vegetation NPP, which improves the refine degree of spatial distribution of vegetation NPP. The seasonal characteristics of vegetation NPP in Yinchuan city are significant, and the changing trend of NPP with seasons is consistent with vegetation phenology and crop growth cycle. Natural factors, land use and human activities in Yinchuan city have significant effects on vegetation carbon sink with significant threshold effects. Natural factors have a greater relative contribution to vegetation NPP than the other two factors.
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Keywords:
- Yinchuan City /
- net primary productivity /
- CASA model /
- nonlinear relationship
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[1] Le Q C, Raupach M R, Canadell J G, et al. Trends in the sources and sinks of carbon dioxide[J]. Nature Geoscience, 2009, 2(12):831-836
[2] Wang B, Gao P, Niu X, et al. Policy-driven China's grain to green program:Implications for ecosystem services[J]. Ecosystem Services, 2017, 27:38-47
[3] Wu D H, Piao S L, Zhu D, et al. Accelerated terrestrial ecosystem carbon turnover and its drivers[J]. Global Change Biology, 2020, 26(9):5052-5062
[4] Piao Shilong, Fang Jingyun, Guo Qinghua. Terrestrial net primary production and its spatio-temporal patterns in China during 1982-1999[J]. Acta Scicentiarum Naturalum Universitis Pekinesis, 2001, 37(4):563-569(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.) [5] Sun Jinke, Niu Haipeng, Yuan Ming. Spatial pattern change and analysis of NPP in terrestrial vegetation ecosystem in China[J]. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51(6):162-168(孙金珂, 牛海鹏, 袁鸣. 中国陆地植被生态系统NPP空间格局变迁分析[J]. 农业机械学报, 2020, 51(6):162-168) [6] Wang Lixia, ZHANG Haixu, LIU Zhao, et al. A coupling model of net primary productivity pattern simulation and prediction[J]. Geomatics and Information Science of Wuhan University, 2021, 46(11):1756-1765(王丽霞, 张海旭, 刘招, 张双成, 孔金玲, 高俪倩. 一种净初级生产力格局模拟及预测耦合模型[J]. 武汉大学学报(信息科学版), 2021, 46(11):1756-1765) [7] Ma Zhongxue, Cui Huijuan, Ge Quansheng. Prediction of net primary productivity change pattern in China based on vegetation dynamic models[J]. Acta Geographica Sinica, 2022, 77(07):1821-1836(马忠学, 崔惠娟, 葛全胜. 基于植被动态模式预估中国植被净初级生产力变化格局[J]. 地理学报, 2022, 77(07):1821-1836) [8] Bi Fan, Pan Jinghu. Estimation of temporal and spatial distribution of potential vegetation net primary productivity in China since 2000[J/OL]. Acta Ecologica Sinica, 2022, 42(24):1-9(毕凡, 潘竟虎. 2000年以来中国潜在植被NPP的时空分布模拟[J/OL]. 生态学报, 2022, 42(24):1-9) [9] Ruimy A, Saugier B, Dedieu G. Methodology for the estimation of terrestrial net primary production from remotely sensed data[J]. Journal of Geophysical Research, 1994, 99(D3):5263-5283
[10] Poter 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
[11] Chen Fujun, Shen Yanjun, Li Qian, et al. Spatio-temporal variation analysis of ecological systems NPP in China in past 30 years[J]. Scientia Geographica Sinica, 2011, 31(11):1409-1414(陈福军, 沈彦俊, 李倩, 等. 中国陆地生态系统近30年 NPP时空变化研究[J]. 地理科学, 2011, 31(11):1409- 1414) [12] Shi Yalin, Cao Yanping, Miao Shuling. Spatiotemporal dynamics of grassland net primary productivity and its diving mechanisms in the Yellow River Basin[J]. Acta Ecologica Sinica, 2023, 43(2):1-13(施亚林, 曹艳萍, 苗书玲. 河流域草地净初级生产力时空动态及其驱动机制研究[J]. 生态学报, 2023, 43(2):1-13) [13] Wang Lunche, Gong Wei, Zhang Miao, et al. Dynamic monitoring of vegetation NPP in Wuhan based on MODIS[J]. Geomatics and Information Science of Wuhan University, 2013, 38(5):548-552(王伦澈, 龚威, 张淼, 等. 武汉地区植被NPP动态监测研究[J]. 武汉大学学报(信息科学版), 2013, 38(5):548-552) [14] Zhang Yili, Qi Wei, Zhou Caiping, et al. Spatial and temporal variability in the net primary production (NPP) of alpine grassland on Tibetan Plateau from 1982 to 2009[J]. Acta Geographica Sinica, 2013, 68(9):1197-1211(张镱锂, 祁威, 周才平, 等. 青藏高原高寒草地净初级生产力(NPP)时空分异[J]. 地理学报, 2013, 68(09):1197-1211) [15] Yan Yan, Qin Jinhua, Fang Lei, et al. Spatiotemporal dynamics of vegetation net primary productivity and its relationships with climatic factors in Hunan Province[J]. Chinese Journal of Ecology, 2022, 41(8):1535-1544(闫妍, 覃金华, 房磊, 等.湖南省植被净初级生产力时空动态及其与气候因素的关系[J]. 生态学杂志, 2022, 41(8):1535-1544) [16] Guan Xiaobin, Shen Huanfeng, Gan Wenxia, et al. Estimation and spatiotemporal analysis of winter NPP in Wuhan based on Landsat TM/ETM+ images[J]. Remote Sensing Technology and Application, 2015, 30(5):884-890(管小彬, 沈焕锋, 甘文霞, 等. 基于Landsat TM/ETM+影像的武汉市冬季 NPP估算及其时空变化分析[J]. 遥感技术与应用, 2015, 30(05):884-890) [17] Zhang Y, Hu Q W, Zou F L. Spatio-temporal changes of vegetation Net Primary Productivity and its driving factors on the Qinghai-Tibetan Plateau from 2001 to 2017[J]. Remote Sensing, 2021, 13(8):1566-1587
[18] Ge W Y, Deng L Q, Wang F, et al. Quantifying the contributions of human activities and climate change to vegetation net primary productivity dynamics in China from 2001 to 2016[J]. Science of Total Environment, 2021, 773:145648
[19] Liu Feng, Zeng Yongnian. Spatial-temporal change in vegetation Net Primary Productivity and its response to climate and human activities in Qinghai Plateau in the past 16 years[J]. Acta Ecologica Sinica, 2019, 39(5):1528-1540(刘凤, 曾永年. 近16年青海高原植被NPP时空格局变化及气候与人为因素的影响[J]. 生态学报, 2019, 39(5):1528-1540) [20] Yang Dan, Wa Xiaofeng. Contribution of climatic change and human activities to changes in net primary productivity in the Loess Plateau[J]. Arid Zone Research, 2022, 39(02):584-593(杨丹, 王晓峰. 黄土高原气候和人类活动对植被NPP变化的影响[J]. 干旱区研究, 2022, 39(02):584-593) [21] Galster G C. Nonlinear and threshold effects related to neighborhood:Implications for planning and policy[J]. Journal of Planning Literature, 2018, 33(4):492-508
[22] Elith J, Leathwick J R, Hastie T. A working guide to boosted regression trees[J]. Journal of Animal Ecology, 2008, 77(4):802-813.
[23] Cui Xu, Yu Bingjie, Yang Linchuan, et al. Spatio-temporal characteristics and non-linear influencing factors of urban rail transit:The case of Chengdu using the gradient boosting decision tree. Economic Geography, 2021, 41(7):61-72(崔叙,喻冰洁,杨林川等.城市轨道交通出行的时空特征及影响因素非线性机制——基于梯度提升决策树的成都实证[J]. 经济地理, 2021, 41(07):61-72) [24] Tong Zhaomin, An Rui, Liu Yaolin. Impact of the built environment on residents' commuting mode choices:A case study of urban village in Wuhan City[J]. Progress in Geography, 2021, 40(12):2048-2060(仝照民, 安睿, 刘耀林. 建成环境对居民通勤方式选择的影响:以武汉市城中村为例[J]. 地理科学进展, 2021, 40(12):2048-2060) [25] Zhu Wenquan, Pan Yaozhong, Zhang Jinshui. Estimation of net primary productivity of Chinese terrestrial vegetation based on remote sensing[J]. Chinese Journal of Plant Ecology, 2007, 31(3):413-424(朱文泉, 潘耀忠, 张锦水. 中国陆地植被净初级生产力遥感估算[J]. 植物生态学报, 2007, 31(3):413-424) [26] Mao Yaping, Fang Shifeng. Research of reference evapotranspiration's simulation based on machine learning[J]. Journal of Geo-information Science, 2020, 22(8):1692-1701(毛亚萍, 房世峰. 基于机器学习的参考作物蒸散量估算研究[J]. 地球信息科学学报, 2020, 22(8):1692-1701) [27] ChungY S. Factor complexity of crash occurrence:An empirical demonstration using boosted regression trees[J]. Accident Analysis & Prevention, 2013, 61:107-118
[28] Friedman J H. Greedy function approximation:A gradient boosting machine[J]. The Annals of Statistics, 2001, 29(5):1189-1232
[29] Li Jianchun, Yuan Wenhua. Assessment of urban land ecological security in Yinchuan city based on the grid method[J]. Journal of Natural Resources, 2017, 32(6):988-1001(李建春, 袁文华. 基于GIS格网模型的银川市土地生态安全评价研究[J]. 自然资源学报, 2017, 32(06):988-1001) [30] Yun Yinjuan. Spatial-temporal simulation of vegetation carbon sink and its influential factors in Shiyang River Basin from 2000 to 2015[D]. Xi'an:Northwest Normal University. 2018, 9-10(贠银绢. 2000-2015年石羊河流域植被碳汇时空变化及影响因子研究[D]. 西北师范大学, 2018, 9-10) [31] Liu Chunyu, Dong Xiaofeng, Liu Yingying, et al. Spatial differences of net primary productivity in Gansu Province[J]. China Population Resources and Environment, 2014, 24(1):163-170(刘春雨, 董晓峰, 刘英英, 等. 甘肃省净初级生产力时空变化特征[J]. 中国人口:资源与环境, 2014, 24(1):163-170) [32] Pan Jinghu, Wen Yan. Estimation and spatial-temporal characteristics of carbon sink in the arid region of Northwest China[J]. Acta Ecologica Sinica, 2015, 35(23):7718-7728(潘竟虎, 文岩. 中国西北干旱区植被碳汇估算及其时空格局[J]. 生态学报, 2015, 35(23):7718-7728) [33] Liu Gang, Sun Rui, Xiao Zhiqiang, et al. Analysis of spatial and temporal variation of net primary productivity and climate controls in China from 2001 to 2014[J]. Acta Ecologica Sinica, 2017, 37(15):4936-4945(刘刚, 孙睿, 肖志强, 等. 2001-2014年中国植被净初级生产力时空变化及其与气象因素的关系[J]. 生态学报, 2017, 37(15):4936-4945)
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