利用无人机遥感反演高潜水位矿区沉陷地玉米叶绿素含量
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摘要
有效识别采煤沉陷耕地损毁程度、面积及空间分布信息是矿区沉陷土地复垦、赔偿的客观需求。农作物受损后叶片叶绿素是很好的指示性指标,针对高潜水位矿区开采沉陷导致地面积水所引起的农作物渍害影响,基于无人机多光谱影像,在传统植被指数的基础上引入红边波段进行扩展,改进了现有的植被指数,结合田间同步实测叶绿素数据,采用经验模型法分别构建了单变量和多变量叶绿素反演模型,通过决定系数(R2)、均方根误差(RMSE)和估测精度(EA) 3个指标筛选出最佳模型。最终构建了多元线性回归MVI(red-edge),GNDVI,NDVI的叶片叶绿素定量反演模型,并采用野外对照区域样本对反演结果进行精度验证。结果显示:相对传统植被指数,引入红边后植被指数与叶片叶绿素的相关性得到显著增强,采用上述方法构建的多元线性回归模型的决定系数普遍提高0. 10～0. 20,达到了0. 73,均方根误差降低了0. 11～1. 98,为0. 938 SPAD,估测精度EA最终可达到83. 4%,说明红边波段对采煤沉陷区作物的叶绿素响应敏感,引入红边波段构建叶绿素反演模型,可以应用于采煤沉陷影响下的玉米叶片叶绿素无人机遥感反演。结果显示:采煤沉陷盆地内玉米叶绿素含量主要集中于52～61 SPAD,面积占整体的75. 1%,叶绿素含量在9～52 SPAD的作物面积占5. 7%,玉米整体长势受采煤扰动影响较严重,叶绿素含量呈现从沉陷盆地边缘往中心逐渐降低的趋势。该研究为土地损毁监测与评价,土地复垦与生态修复等提供基础数据与理论支撑。
Identif the extent,area,and spatial distribution information of cultivated land subsidence damage is an objective demand for land reclamation and compensation in mining areas.Leaf chlorophyll is a good indicator when cropsare damaged This paper focused on the impact of crop waterlogging caused by mining subsidence on high groundwater mining Dong Tan coalmine as an example.Based on the multi-spectral remote sensing image of UAV(Unmanned Aerial Vehicle),the traditional VI(Vegetation Index) was extended by adding the red edge band,and the new vegetation index was put forward.Then,based on the corresponding VI and combind ynchronous field measurements of chlorophyll data,nivariate and multivariate chlorophyll inversion models were constructed using empirical modelling methods,respectively,the remote sensing inversion models of chlorophyll in corn leaves were built,validated,and compared.Finally,the spatial distribution of chlorophyll in corn leaves was analyzed using the best model in the study area.The results showed that the correlation between the vegetation index and the leaf chlorophyll after introducing the red edge was significantly enhanced compared to the traditional vegetation index.In all univariate models,MVIred-edge(Red-edge Modified Vegetation Index) which were added red edge were selected as the best vegetation index. Using nivariate and MLR(Multivariate Stepwise Linear Regression) method,the precision of the models based on the MVIred-edgewas improved compared to the VI with the calibration coefficient of determination(R2) raised between 0.10 and 0.20,and the calibration RMSE(Root Mean Squares Error) reduced between 0.11 and 1.98,the validation R2 raised between 0.03 and 0.12,the validation RMSE reduced between 1.0 and 1.5,and finally the EA(Estimation Accuracy) reached83.4%. The MLR models composed of MVIred-edge,GNDVI(Green Normalized Difference Vegetation Index),NDVI(Normalized Difference Vegetation Index) obtained generally good performance with the EA greater than 80%.The main reasons improved the model precision were that red edge band are more ensitive to egetation hlorophyll esponses.Among them,when the vegetation biomass is large,the pigment content is high,and the growth is vigorous,the red edge will move to the long wave direction(red shift).When the vegetation suffers from diseases,pests,pollution,leaf aging and other factors,the red edge will move to the short wave direction(blue shift). It is indicated that the red-edge band is sensitive to the chlorophyll response of the crops in the coal mining subsidence area.The red edge band is introduced to construct the chlorophyll inversion model,which can be applied to remote sensing inversion of chlorophyll drones in maize leaves under the influence of coal mining subsidence.The study shows that the corn chlorophyll content in the coal mining subsidence basin is mainly concentrated in the 52 ～ 61 SPAD(Soil and Plant Analyzer Development),and the area accounts for 75.1% of the total. The chlorophyll content in the 9 ～ 52 SPAD crop area accounts for 5.7%.The overall growth of the corn is affected by the disturbance of coal mining. The chlorophyll content gradually decreased from the edge of the subsidence basin to the center.The study provides basic data and theoretical support for land damage monitoring and evaluation,land reclamation and ecological restoration.
Identif the extent,area,and spatial distribution information of cultivated land subsidence damage is an objective demand for land reclamation and compensation in mining areas.Leaf chlorophyll is a good indicator when cropsare damaged This paper focused on the impact of crop waterlogging caused by mining subsidence on high groundwater mining Dong Tan coalmine as an example.Based on the multi-spectral remote sensing image of UAV(Unmanned Aerial Vehicle),the traditional VI(Vegetation Index) was extended by adding the red edge band,and the new vegetation index was put forward.Then,based on the corresponding VI and combind ynchronous field measurements of chlorophyll data,nivariate and multivariate chlorophyll inversion models were constructed using empirical modelling methods,respectively,the remote sensing inversion models of chlorophyll in corn leaves were built,validated,and compared.Finally,the spatial distribution of chlorophyll in corn leaves was analyzed using the best model in the study area.The results showed that the correlation between the vegetation index and the leaf chlorophyll after introducing the red edge was significantly enhanced compared to the traditional vegetation index.In all univariate models,MVIred-edge(Red-edge Modified Vegetation Index) which were added red edge were selected as the best vegetation index. Using nivariate and MLR(Multivariate Stepwise Linear Regression) method,the precision of the models based on the MVIred-edgewas improved compared to the VI with the calibration coefficient of determination(R2) raised between 0.10 and 0.20,and the calibration RMSE(Root Mean Squares Error) reduced between 0.11 and 1.98,the validation R2 raised between 0.03 and 0.12,the validation RMSE reduced between 1.0 and 1.5,and finally the EA(Estimation Accuracy) reached83.4%. The MLR models composed of MVIred-edge,GNDVI(Green Normalized Difference Vegetation Index),NDVI(Normalized Difference Vegetation Index) obtained generally good performance with the EA greater than 80%.The main reasons improved the model precision were that red edge band are more ensitive to egetation hlorophyll esponses.Among them,when the vegetation biomass is large,the pigment content is high,and the growth is vigorous,the red edge will move to the long wave direction(red shift).When the vegetation suffers from diseases,pests,pollution,leaf aging and other factors,the red edge will move to the short wave direction(blue shift). It is indicated that the red-edge band is sensitive to the chlorophyll response of the crops in the coal mining subsidence area.The red edge band is introduced to construct the chlorophyll inversion model,which can be applied to remote sensing inversion of chlorophyll drones in maize leaves under the influence of coal mining subsidence.The study shows that the corn chlorophyll content in the coal mining subsidence basin is mainly concentrated in the 52 ～ 61 SPAD(Soil and Plant Analyzer Development),and the area accounts for 75.1% of the total. The chlorophyll content in the 9 ～ 52 SPAD crop area accounts for 5.7%.The overall growth of the corn is affected by the disturbance of coal mining. The chlorophyll content gradually decreased from the edge of the subsidence basin to the center.The study provides basic data and theoretical support for land damage monitoring and evaluation,land reclamation and ecological restoration.
引文
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[3]肖武,李素萃,王铮,等.高潜水位煤矿区生态风险识别与评价[J].生态学报,2016,36(17):5611-5618.XIAO Wu,LI Sucui,WANG Zheng,et al. Ecological risk identification and assessment for a coal mine with a high groundwater table[J].Acta Ecologica Sinica,2016,36(17):5611-5619.
[4] CHRISTAIN F,WOLF G B. Monitoring of environmental changes caused by hard coalmining,remote sensing for environmental monitoring,GIS applications,and geology[J].Proceeding of SPIE,2002,78(46):4545-4560.[%] VENKATARAMAN G,KUMAR S P,RATHA D S,et al. Open cast mine monitoring and environmental impact studie through remote sensing—A case study from Goa,India[J]. Geocarto International,1997,12(2):39-53.[&]吴国伟.高潜水位矿区土地损毁程度评价方法研究[D].北京:中国矿业大学(北京),2016.WU Guowei. Reasearch on evaluation method of land damage in high groundwater elevation coal area[D].Beijing:China University of Mining and Technology(Beijing),2016.[’]肖武,陈佳乐,胡振琪.等.高潜水位采煤沉陷地构建平原水库可行性分析与实践[J].煤炭科学技术,2017,45(7):184-189.XIAO Wu,CHEN Jiale,HU Zhenqi,et al. Feasibility analysis and practice of constructing plain reservoirs in high underground water mining subsidence area[J]. Coal Science and Technology,2017,45(7):184-189.
[8] SELLERS P J,LOS S O,TUCKER C J,et al.A revised land surface parameterization(SiB2)for atmospheric GCMS-part two:the generation of global fields of terrestrial biophysical parameters from satellite data[J].Journal of Climate,1996,9(4):676-705.
[9] IVERSON L R.Land-use changes in Illinois,USA-The influence of landscape attributes oncurrent and historic land use[J]. Landscape Ecology,1988,2(1):45-61.
[10]商华艳.基于RS和GIS的矿山地质环境质量评价[D].北京:中国地质大学(北京),2011.SHANG Huayan. Mine geological enviroment quality evaluation based on RS and GIS[D]. Beijing:China University of Geoscience(Beijing),2011.
[11]吴雪茜,周大伟,安士凯.等.淮南潘谢矿区土地与水域演变趋势及治理对策[J].煤炭学报,2015,40(12):2927-2932.WU Xueqian,ZHOU Dawei,AN Shikai,et al. Evolvement trend of land and water systems in Huainan Panxie mining area and its countermeasures[J]. Journal of China Coal Society,2015,40(12):2927-2932.
[12]徐嘉兴,李钢,陈国良,等.矿区土地生态质量评价及动态变化[J].煤炭学报,2013,38(S1):180-185.XU Jiaxing,LI Gang,CHEN Guoliang,et al.Evaluation and dynamic change of ecological quality of mining land[J].Journal of China Coal Society,2013,38(S1):180-185.
[13]赵虎,杨正伟,李霖,等.作物长势遥感监测指标的改进与比较分析[J].农业工程学报,2011,27(1):243-249.ZHAO Hu,YANG Zhengwei,LI Lin,et al. Improvement and comparative analysis of indices of crop growth condition monitoring by remote sensing[J].Transactions of the CSAE,2011,27(1):243-249.
[14]田明璐,班松涛,常庆瑞,等.基于低空无人机成像光谱仪影像估算棉花叶面积指数[J].农业工程学报,2016,32(21):102-108.TIAN Minglu,BAN Songtao,CHANG Qingrui,et al. Use of hyperspectral images from UAV-based imaging spectroradiometer to estimate cotton leaf area index[J]. Transactions of the CSAE,2016,32(21):102-108.
[15]赵晓庆,杨贵军,刘建刚,等.基于无人机载高光谱空间尺度优化的大豆育种产量估算[J].农业工程学报,2017,33(1):110-116.ZHAO Xiaoqing,YANG Guijun,LIU Jiangang,et al. Estimation of soybean breeding yield based on optimization of spatial scale of UAV hyperspectral image[J]. Transactions of the CSAE,2017,33(1):110-116.
[16]肖武,胡振琪,张建勇,等.无人机遥感在矿区监测与土地复垦中的应用前景[J].中国矿业,2017,26(6):71-78.XIAO Wu,HU Zhenqi,ZHANG Jianyong,et al. The status and prospect of UAV remote sensing in mine monitoring and land reclamation[J].China Mining Magazine,2017,26(6):71-78.
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