基于无人机低空遥感的荒漠植被覆盖度与归一化植被指数验证及其对水热梯度的响应

doi:10.13287/j.1001-9332.202001.014

摘要/Abstract

摘要： 为了验证在荒漠地区MODIS-NDVI产品的精度以及为在气候变化背景下荒漠草地的科学管理提供依据,本文利用无人机低空遥感研究了干旱荒漠地区植被覆盖度(FVC)和归一化植被指数(NDVI)对水、热梯度的响应规律。在内蒙古阿拉善荒漠地区的100个样点采用GreenSeeker手持光谱仪获得NDVI值(NDVI_R),通过MODIS-NDVI数据产品提取每个样点的NDVI(NDVI_M),借助NDVI_R验证NDVI_M的精确度;通过无人机遥感手段获得每个采样点的FVC(FVC_U),利用像元二分模型反演每个样点的FVC(FVC_M),借助FVC_U验证FVC_M的精确度;并结合气象数据探讨基于无人机低空遥感的荒漠地区FVC和NDVI对水热梯度的响应。结果表明: MODIS-NDVI数据产品能够反映阿拉善地区的NDVI,精确度为84.2%,但比真实值高15.7%;FVC_M能够反映阿拉善地区的FVC状况,精确度为83.1%,但比真实值低14.8%;不同采集方式获得的NDVI受气象因子的影响程度不同,NDVI不仅受气温和降雨的影响,也受地温、蒸发量以及两者相互作用的影响,由于受大气影响程度不同, NDVI_M受地温、蒸发量、降水量的影响比NDVI_R大,NDVI_R受气温的影响比NDVI_M大。在阿拉善地区研究FVC随水热梯度的变化不仅要考虑降水量和气温,还应考虑蒸发量、地温以及气象因子之间相互作用的影响,其中,气温与降雨、蒸发量与地温以及气温与蒸发量之间相互作用对FVC_U的影响较大。

Abstract: To verify the accuracy of MODIS-NDVI data products in deserts and provide guidance for scientific management of desert grasslands in the context of climate change, we examined the responses of fractional vegetation cover (FVC) and normalized difference vegetation index (NDVI) to hydrothermal gradient in arid desert areas using unmanned aerial vehicle (UAV) remote sensing. In Alxa desert region of Inner Mongolia, GreenSeeker handheld spectrometer was used to obtain NDVI (NDVI_R) of 100 sampling points. NDVI was extracted by MODIS-NDVI data products (NDVI_M), and the accuracy of NDVI_M was verified by NDVI_R. FVCof each sampling point was obtained through unmanned aerial vehicle remote sensing (FVC_U), which was used to examine the FVC that was retrieved by the pixel binary model (FVC_M). In addition, combining meteorological data, we examined the responses of FVC and NDVI to hydrothermal gradient based on UAV remote sensing method. The results showed that MODIS-NDVI data products reflected the real NDVI in Alxa area with an accuracy of 84.2%, but NDVI_M were 15.7% higher than the actual values. FVC_M reflected the vegetation coverage of Alxa region with an accuracy of 83.1%, which were 14.8% lower than the real value. Effects of meteorological factors on NDVI was different, depending on the different acquisition methods. NDVI was affected not only by temperature and precipitation, but also by ground temperature, evaporation and the interaction between evaporation and ground temperature. Because of the different degree of atmospheric influence, NDVI_Mwas more affected by ground temperature, evaporation and precipitation than NDVI_R, while NDVI_R was more affected by temperature than NDVI_M. To examine the changes of vegetation coverage across hydrothermal gradient in desert area, we should consider not only precipitation and temperature, but also the interaction between evaporation, ground temperature and meteorological factors. The interaction between temperature and rainfall, evaporation and ground temperature, and between temperature and evaporation had greater impacts on FVC_U.

唐亮, 何明珠, 许华, 贾谱超. 基于无人机低空遥感的荒漠植被覆盖度与归一化植被指数验证及其对水热梯度的响应[J]. 应用生态学报, 2020, 31(1): 35-44.

TANG Liang, HE Ming-zhu, XU Hua, JIA Pu-chao. Validation of vegetation coverage and NDVI based on UAV remote sensing method and its response to hydrothermal gradient[J]. Chinese Journal of Applied Ecology, 2020, 31(1): 35-44.

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