Modelling the relationship between groundwater depth and NDVI using time series regression with Distributed Lag Models

被引：11

作者：

Phiri, Margaret ^{[1
]}

Shiferaw, Yegnanew A. ^{[2
]}

Tesfamichael, Solomon G. ^{[1
]}

机构：

[1] Univ Johannesburg, Dept Geog Environm Management & Energy Studies, Auckland Pk Kingsway Campus,POB 524, ZA-2006 Johannesburg, South Africa

[2] Univ Johannesburg, Dept Stat, Auckland Pk Kingsway Campus,POB 524, ZA-2006 Johannesburg, South Africa

来源：

SOUTH AFRICAN JOURNAL OF GEOMATICS | 2018年 / 7卷 / 02期

关键词：

Groundwater depth; Landsat NDVI; Time-series analysis; Distributed Lag Models;

D O I：

10.4314/sajg.v7i2.4

中图分类号：

TP7 [遥感技术];

学科分类号：

081102 ; 0816 ; 081602 ; 083002 ; 1404 ;

摘要：

Groundwater plays a key role in hydrological processes, including in determining aboveground vegetal growth characteristics and species distribution. This study aimed at estimating time-series data of Normalized Difference Vegetation Index (NDVI) using groundwater depth as a predictor in two land cover types: grassland and shrubland. The study also investigated the significance of past (lagged) groundwater and NDVI in estimating the current NDVI. Results showed that lagged groundwater depth and vegetation conditions influence the amount of current NDVI. It was also observed that first lags of groundwater depth and NDVI were significant predictors of NDVI in grassland. In addition, first and second lags of NDVI were consistently significant predictors of NDVI in shrubland. This shows the importance of vegetation type when modelling the relationship between groundwater depth and NDVI.

引用

页码：147 / 163

页数：17

共 38 条

[1] NEW LOOK AT STATISTICAL-MODEL IDENTIFICATION [J].