1 西北大学城市与环境学院，陕西 西安 710127;
2 西北大学陕西省地表系统与环境承载力重点实验室，陕西 西安 710127; 3 青海省农牧业遥感中心，青海 西宁 810007； 4 农业部规划设计研究院，北京 100125
收稿日期: 2019-07-25
修回日期: 2019-12-27
网络出版日期: 2020-05-25
基金资助
国家自然科学基金（41401494）；陕西省教育厅基金（14JK1745）资助
Grassland yield change in Qinghai Province based on MODIS data
Received date: 2019-07-25
Revised date: 2019-12-27
Online published: 2020-05-25
青海省属于全国四大牧区之一，及时监测草地植被长势、准确估算牧草产量对青海牧区可持续发展与生态保护具有重要意义。草地产草量遥感估算主要基于植被指数与地面实测数据的统计关系，但是估算涉及植被指数、统计模型和建模指标等因素，不同组合建立的估算模型的精度不同。本文基于青海省MODIS数据与地面实测产草量数据，选择了6种植被指数（NDVI、EVI、RVI、DVI、RDVI、MSAVI）、5种统计模型（简单线性模型、二次多项式模型、幂函数模型、指数函数模型、对数函数模型）以及3种建模指标（植被指数年度最大值VImax、植被指数生长季累积值VIseason-cum、植被指数年度累积值VIannual-cum），研究不同组合下估算模型的精度差异，并从中选出最优产草量估算模型，用于估算青海省2015年和2016年的产草量。结果表明：（1）6种植被指数中，基于NDVI的产草量估算精度最高；非线性模型的估算精度高于线性模型，尤其是指数模型，适用于大多数草地类型产草量的估算；基于NDVI年度最大值的估算模型对大多数草地类型都具有最高的决定系数（R2）。（2）从干重来看，高产草量区（>1 200 kg·hm-2）主要位于青海东部的高寒草原，中等产草量区（600~1 200 kg·hm-2）位于青海南部和东部的高寒草原和禾草草原，低产草量区（<600 kg·hm-2）位于青海西部和北部的高寒草甸、高寒草原、高寒荒漠和盐生草甸。（3）与2015年相比，2016年青海省干草总产量减少31.60×104 t，减幅为1.36%。其中，禾草草原和高寒草甸的减产幅度最大，而荒漠草原和盐生草甸的产量则有所增加。本文可为草地产草量遥感估算的研究和实践提供参考。
黄鑫, 刘建红, 申克建, 刘咏梅, 王雷 . 基于MODIS的青海草地产草量变化遥感分析[J]. 干旱区地理, 2020 , 43(3) : 715 -725 . DOI: 10.12118/j.issn.1000-6060.2020.03.18
Qinghai Province is one of the four major pastoral areas in China.It is of great significance to monitor the growth of grassland vegetation and accurately estimate grass yields for the sustainable development and ecological protection of the Qinghai pastoral area.Remote sensing estimation of grass yields is mainly based on statistical relationship between vegetation index and ground measured data.However,the selection of different vegetation indexes,various statistical models,and optional modeling metrics affect the accuracy of the estimation results.Based on the ground measured grass yield data of the Qinghai Province and the Moderate Resolution Imaging Spectroradiometer (MODIS) data,this paper selected six vegetation indexes,five statistical models,and three modeling metrics to test the accuracy of different estimation models.The vegetation indexes included the Normalized Difference Vegetation Index (NDVI),Enhanced Vegetation Index (EVI),Ratio Vegetation Index (RVI),Difference Vegetation Index (DVI),Renormalized Difference Vegetation Index (RDVI),and Modified Soil Adjusted Vegetation Index (MSAVI).The statistical models consisted of a simple linear model,quadratic polynomial model,power function model,exponential function model,and logarithmic function model.The modeling metrics included the annual maximum value of vegetation index,cumulative value of vegetation index in growing season,and annual cumulative value of vegetation index.First,we explored which vegetation index and estimation model were the most suitable for estimating the grass yields of different grassland types in the Qinghai Province.Then,we compared the accuracy of estimated grass yields from the three modeling metrics.Finally,we selected the optimal grass yield estimation models to estimate grass yields of the Qinghai Province in 2015 and 2016 and analyzed grass yield change during 2015-2016.The results showed as follows:(1) among the six vegetation indexes,NDVI was the most suitable vegetation index to estimate grass yields in the Qinghai Province.The accuracy of nonlinear models was generally higher than that of linear models,especially the exponential models,which are suitable for estimating grass yields of most grassland types.The estimation models based on the annual maximum of NDVI showed the highest coefficient of determination (R2) for almost all grassland types.(2) from the perspective of hay yields,the high-yield area (>1 200 kg·hm-2) was mainly located in the alpine steppes in eastern Qinghai,and the middleclass area (600-1 200 kg·hm-2) was located in the grass steppes and the alpine steppes in the north and east of Qinghai.Grasslands with lowyield (<600 kg·hm-2) were located in the alpine meadows,alpine steppes,alpine deserts,and salt meadows in the western and northern part of Qinghai.(3) the total hay production in the Qinghai Province was 31.60 × 104 t in 2016,showing 1.36% decrease compared to 2015.Among all,the grass steppes and alpine meadows showed the highest yield loss,while in the desert steppes and salt meadows,the yield increased.This study not only provided a reference for ecological protection and sustainable development in the pastoral area of Qinghai,but also provided research and practices for estimating grassland yield using remote sensing technology.
Key words：
grass yield
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