Evaluation of Vegetation Indices and Phenological Metrics Using Time-Series MODIS Data for Monitoring Vegetation Change in Punjab, Pakistan

Pingbo Hu,Faisal Mumtaz,Alireza Sharifi,Muhammad Naveed Tahir,Syed Hassan Iqbal Ahmad Shah,Lili Zhang,Aqil Tariq

doi:10.3390/w13182550

Abstract

In arid and semi-arid regions, it is essential to monitor the spatiotemporal variability and dynamics of vegetation. Among other provinces of Pakistan, Punjab has produced a significant number of crops. Recently, Punjab, Pakistan, has been described as a global hotspot for extremes of climate change. In this study, the soil adjusted vegetation index (SAVI), normalized vegetation difference index (NDVI), and enhanced vegetation index (EVI) were comprehensively evaluated to monitor vegetation change in Punjab, Pakistan. The time-series MODIS (Moderate Resolution Imaging Spectroradiometer) data of different periods were used. The mean annual variability of the above vegetation indices (VIs) from 2000 to 2019 was evaluated and analyzed. For each type of vegetation, two phenological metrics (i.e., for the start of the season and end of the season) were calculated and compared. The spatio-temporal image analysis of the mean annual vegetation indices revealed similar patterns and varying vegetation conditions. In the forests and vegetation areas with sparse vegetation, the EVI showed high uncertainty. The phenological metrics of all vegetation indices were consistent for most types of vegetation. However, the NDVI result had the greatest variance between the start and end of season. The lowest annual VI variability was mainly observed in the southern part of the study area (less than 10% of the study area) based on the statistical analysis of spatial variability. The mean annual spatial variability of NDVI was <20%, SAVI was 30%, and EVI ranged between 10–20%. More than 40% of the variability was observed in the NDVI and SAVI vegetation indices.

Highlights

Semi-arid regions cover about 15% of the Earth’s land surface and the spatial and temporal patterns of rainfall in these regions are very variable, which causes drastic variability in the spatiotemporal distribution, production, and development of vegetation [1,2].Over recent decades, many semi-arid and dryland habitats have faced greater pressures 4.0/).from human-induced activities and climate change [3,4,5]
The varying associations of normalized vegetation difference index (NDVI), enhanced vegetation index (EVI), and soil adjusted vegetation index (SAVI) were estimated in different vegetation forms
A minor correlation exists between NDVI, EVI, and SAVI, with Pearson’s correlation coefficients ranging from

Summary

Introduction

Semi-arid regions cover about 15% of the Earth’s land surface and the spatial and temporal patterns of rainfall in these regions are very variable, which causes drastic variability in the spatiotemporal distribution, production, and development of vegetation [1,2].Over recent decades, many semi-arid and dryland habitats have faced greater pressures 4.0/).from human-induced activities and climate change [3,4,5]. Semi-arid regions cover about 15% of the Earth’s land surface and the spatial and temporal patterns of rainfall in these regions are very variable, which causes drastic variability in the spatiotemporal distribution, production, and development of vegetation [1,2]. Many semi-arid and dryland habitats have faced greater pressures 4.0/). To better understand climatic change and anthropogenic impacts on dryland and semi-arid ecosystems, information on the vegetation’s spatiotemporal variability is the key source. To monitor semi-arid and dryland vegetation activity and spot changes in growth and phenology of vegetation [11,12,13], vegetation indices time-series have often been used [14,15,16,17]

Results

Discussion

Conclusion