Monitoring the effects of climate change and topography on vegetation health in Tharparkar, Pakistan

Climate change is challenging for almost all sectors around the globe. Climate changes have proved to be calamitous for many segments of society, but agriculture can be regarded as a real hard-hit. Climate change is an issue that affects the entire world and has the potential to significantly alter crop yields. There is estimated annual damage to agriculture from rising temperatures. Since the 20th century, human activities such as deforestation and changes in land cover have contributed 1.5 billion metric tons to greenhouse gas (GHG) emissions into the atmosphere, which is one of the main sources of the observed climatic changes (Das & Sharma 2023). These climate changes brought on by human activity also have an impact on the composition of the planet's atmosphere. This enhances the examination of how the climate alters throughout time.

Globally, climate change has in turn caused a decrease in plant cover, which has had significant economic consequences as well as serious losses in biodiversity and the ecosystem (Al-Kindi et al. 2023). An ecological element that is particularly vulnerable to climate change is vegetation upon which its impact varies by place and for different plant species. Regions with a delicate ecosystem and climate may experience a greater impact. For instance, GCMs predict warmer winter temperatures, especially in northern latitudes like northern Europe, but also more droughts in some areas due to less precipitation, like southern Europe (AR4 Climate Change 2007: The Physical Science Basis – IPCC n.d.). The loss of urban green space disturbs the ecological balance (Haq 2011). Rapid growth in urbanization also threatens vegetation and open green spaces in urban areas. The reason behind this is that due to urbanization land surface and air temperature increase in such areas (Smith et al. 2017). Metropolitan areas like Lahore have less vegetation due to their activities and expansion. Governmental initiatives like Clean and Green Pakistan are established to monitor the vegetation and cover in metropolitan areas. Green surface is the term used to describe the type of vegetation that grows on urban surfaces (Haq 2011).

Climate change is a complex systemic process that causes specific values of spatial variation in vegetated ecosystem responses. The structure and function of ecosystems may be significantly impacted by even small climate changes (Jing et al. 2022). Over the past century, a 0.5 °C change in temperature can be observed across different ecological systems including desert ecosystems (McCarty 2001). Major impacts like desertification, loss in productivity, decrease in cultivable land, soil loss, and water scarcity are caused by climate change in deserts (Goodier 2003). Rainfall patterns are also affected by climate change, which further affects livelihood in the deserts. The main source of livelihood in deserts is livestock keeping, which is dependent on rainfall and is affected by changes in rainfall patterns (Horn et al. 2003). Evapotranspiration is also increased by climate change which causes desert warming (Shove 2010).

In order to assist human development, observational data and model simulations offer a fundamental understanding of a variety of hot problems, such as climate change, natural ecosystems, urbanization, and human health (Overpeck et al. 2011). However, remote sensing (RS) has steadily risen to prominence as a top study technique in social development studies because it provides multitemporal data and spatially comprehensive images (Yang et al. 2013). RS and Geographic Information Systems (GIS) have been widely used to study the role of vegetation trend variation and its relation to the surrounding environment in the last few decades. For instance, Multitemporal images and LULC derived from satellite images along with GIS techniques are used to determine land suitability for agricultural use (AL-Taani et al. 2021), to monitor the urban growth and land use change detection (Hegazy & Kaloop 2015), and to identify urban vegetation stress factors (Cârlan et al. 2020).

Earth's climate will keep on changing over time. Researchers and policymakers have been searching for multiple ways to track the threat of climate change. Understanding climate change and then forecasting it is a bit difficult for geoscientists as it is determined by a complex set of physical, biological, and chemical interactions. Such type of research requires proper access to data and then managing it properly for further analysis. For this type of analysis, GIS and RS have found wide applications in climate change and adaptation (Eniolorunda 2014). For spatiotemporal analysis, RS imageries have made it possible to analyze the environmental elements and impact of human activities (Lillesand et al. 2015). A combined approach of RS and GIS tools can be used to capture and process relevant data for actions according to climate change events (Eniolorunda 2014).

In general, while previous studies on the relationship between vegetation and rainfall have focused solely on drought analysis, this study investigated the relationship between vegetation indices and environmental factors (climate and topography) in the southeastern part of Pakistan's Sindh province for the first time, which could be considered one of the research's innovations. The goal of the study was to analyze how climate change has been affecting Tharparkar's vegetation for the past 20 years by comparing and evaluating data on vegetation. This study aimed to assess the climatic and topographic parameters influencing vegetation cover over 20 years (2001–2020), and investigate the correlation between vegetation indices (Normalized Vegetation Index (NDVI), Stress-Related Vegetation Index (STVI), Optimized Soil-Adjusted Vegetation Index (OSAVI), Soil-Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index (EVI)) to topographical features using SRTM–Digital Elevation Model (SRTM–DEM), and climatic parameter (CHIRPS) and land surface temperature (LST). Finally the effect, by using a combined approach for all of the parameters using AHP (analytical hierarchy process).

The climate of Tharparkar is defined by hot summers and mild winters, with an average annual temperature of around 27 °C. The majority of the precipitation occurs during the monsoon season, which lasts from July to September, and the region typically receives around 277 mm of rainfall per year, but this varies greatly from year to year (Usman & Nichol 2020). Given the region's dependence on agriculture and livestock rearing, crops such as wheat, cotton, and sorghum are grown in the area. The study area's unique environmental and socioeconomic characteristics make it an important region for studying the impacts of climate change and developing strategies for adaptation and mitigation. The region's vulnerable population and dependence on agriculture and livestock make it particularly susceptible to the effects of climate change, such as prolonged droughts and erratic precipitation patterns. Therefore, understanding the impact of climate change on vegetation cover in Tharparkar is crucial for sustainable development in the region.

Overall, the study area of Tharparkar presents an excellent opportunity for researchers to analyze and evaluate the impacts of climate change on vegetation indices and to develop strategies to mitigate those impacts. As such, this study's findings can be crucial for policymakers and stakeholders to develop and implement policies that promote sustainable development and adaptation to climate change in Tharparkar and other regions with similar environmental and socioeconomic characteristics.

Geospatial data for the study area, the Tharparkar region, was collected from various sources, including the US Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). Table 1 shows the details of all the datasets used in this study. Overall, the dataset includes NDVI, STVI, EVI, SAVI, and OSAVI extracted from MOD13A3 (1,000 m, monthly), LST from MOD11A2 (1,000 m, 8 days), rainfall data through CHIRPS (0.5° × 0.5°), and DIGITAL ELEVATION MODEL (DEM 30 × 30 m) for slope/elevation aspect extraction. The software used to perform the analysis was ArcGIS 10.4.1 using the multicriteria tools of Spatial Analyst Toolbox.

No formula was used in this calculation. Shapefile of Tharparkar consisting of points (stations) which were taken from Google Earth were added to ArcGIS to generate precipitation raster. Table 2 shows the longitudes and latitudes of all the stations.

The temperature of the land surface, which includes the soil and vegetation, is referred to as LST. As a reflection of the equilibrium of energy and water exchange between the land surface and the atmosphere, it is a significant factor in the Earth's climate system.

There was no need for a calculation since the EVI was already available in MODIS data bundle.

The NDVI data were also already available in the MODIS product when the data were downloaded, thus there was no need to calculate it numerically using the raster calculator in ArcMap software.

The indicator is based on the idea that compared to stressed vegetation, healthy vegetation reflects more near-infra-red light and less mid-infra-red and red radiation. Hence, STVI levels near 1 denote healthy vegetation, while STVI values near −1 denote stressed vegetation.

SAVI is especially valuable in arid and semi-arid areas where there is little plant cover and soil reflectance can significantly affect the values of the overall vegetation index.

The methodology involved the use of the AHP as a multicriteria decision-making method to determine the relative importance of different criteria and select the best option among a set of alternatives. Data for all factors involved in the decision-making process were gathered from various websites.

The first step involved converting the vector data, such as CHIRPS data, into raster format using the rasterization tool in ArcMap 10.4.1. Vegetation indices were then derived from MODIS images using various formulas from Equations (1)–(6). After calculating the LST and Vegetation Indices, the resulting raster data were reclassified into different categories ranging from poor to excellent vegetation cover based on the standard deviation of the data. Reclassification allowed for visual analysis of the patterns and changes in vegetation cover and temperature in the study area.

Next, the AHP tool was employed, which involved comparing each pair of all the criterion rasters and assigning relative importance to each criterion. A comparison table marked from 1 to 9 scale was used, where 1 represented equal importance, and 9 represented extreme importance.

Table 3 is used to show the relative importance of each criterion in determining vegetation cover suitability using rate, Normalized Rating Index (NRI) and weight index based on Multi-Criteria Decision Analysis (MCDA). Further, the final raster map resulting from the integration of all the factors shows results in the form of the vegetation map with four categories: no vegetation, very slight, moderate, and high vegetation. This final raster map is a visual representation of the vegetation cover in the study area based on different indices, LST, slope, elevation, and rainfall.

To determine the most significant contributing factors, a correlation analysis was performed between the satellite-based and ground-based data. The correlation was computed among rainfall, LST, EVI, NDVI, STVI, OSAVI, and SAVI, and the results indicate a significant correlation at the 0.01 level and 0.05 level (two-tailed). The use of correlation analysis helps in identifying the key factors that are responsible for the vegetation cover in the study area and can be used for future studies and policymaking.

Pearson's correlation coefficient is a statistical test that measures the mathematical relationship between two continuous variables. It provides details about the strength and direction of the association. This study utilized Pearson's correlation coefficient analysis to check the existence of a linear relationship between input variables. A strong association is indicated by a high correlation, while a weak relationship is shown by a low correlation. The magnitude of ‘r’ represents the strength of the association, whereas the sign of ‘r’ denotes the direction of the association. A correlation near 0 indicates no linear relationship between two continuous variables. Pearson's correlation coefficient analysis is frequently used to assess the strength of the relationship between two quantitative variables when statistical data are available (Sedgwick 2012; Sibley et al. 2014).

Table 4 displays the correlation chart for all the components, providing a comprehensive overview of the relationship between different factors and their impact on vegetation cover. This analysis evaluated the significance of these parameters in comparison to each other. Mainly two trends are observed, one related to rainfall and the other to temperature, both of which are influenced by topographic features such as slope and elevation.

The study examined the relationships between different factors in the study area, including rainfall, LST, elevation, and vegetation indices. Generally, two patterns are seen from the correlation analysis of these factors: one from the standpoint of rainfall and the other from that of temperature topographic features (slope and elevation) influence these climate parameters.

Rainfall, LST, EVI, NDVI, STVI, OSAVI, and SAVI showed a correlation significant at the 0.01 level (two-tailed). Overall, rainfall causes the vegetation indices (NDVI, EVI, SAVI) to increase positively and affect (LST, OSAVI, and STVI) negatively because the temperature of the area drops, and due to that stress level decreases (Castillo et al. 2007). The NDVI and rainfall association is quite substantial and favorable with a correlation coefficient of 0.495 (p = 0.01). It shares a similar relationship with both EVI (r = 0.533, p < 0.01) and SAVI (r = 0.345, p < 0.01) as well. However, there is not much significant link between LST and STVI, with a correlation coefficient of −0.127 (p = 0.334). When comparing LST to other indices, the results will be ambiguous; rainfall will have a negative effect on LST, but NDVI, EVI, and SAVI show significant values of 0.05 (Nwilo et al. 2021). LST and STVI have a bad correlation. The results will be contrary when comparing LST to other indices; there will be a negative response to rainfall, with NDVI, EVI, and SAVI exhibiting significant values of 0.05.

In general, temperature and rainfall served as the two key indicators in a study that shows the trends in this area. The findings suggested that, as time passed, rainfall and temperature variations had an impact on vegetation development. The growth of vegetation was observed to be stressed during times of low rainfall and high temperatures (Cotrina Cabello et al. 2023). When there is more rainfall, stress and temperature are lower, resulting in increased vegetation density, and vice versa (Joshi et al. 2019), similar effects are seen in our study that rainfall and temperature both had similar effect on NDVI and SAVI. Both vegetation indices had direct relation with the rainfall and inverse relation with temperature, similar effect was observed for EVI as well. Future research could incorporate additional criteria and factors for site suitability analysis to improve the accuracy of the results. Nonetheless, the results of this study provide valuable insights for decision-makers in the energy industry and can aid in the development of sustainable energy solutions.

This research underscores the vital link between climatic fluctuations and vegetation in Tharparkar, Pakistan. Through an examination of climatic and topographic factors alongside vegetation indices, it becomes evident that each factor influences the others in diverse ways. Specifically, our analysis reveals that temperature and precipitation (climatic factors), as well as slope and elevation (topographic factors), exert significant effects on EVI, NDVI, STVI, OSAVI, and SAVI (vegetation indices) in the Tharparkar region.

Moreover, our findings indicate that elevated areas having elevation ≤130 m experience heightened LST ≤41 °C and rainfall ≤490 mm exhibit moderate to high levels of vegetation cover. Conversely, regions with lower elevations having elevation ≥50 m having low rainfall ≥340 mm placing stress on vegetation and leading to reduced vegetation and canopy cover. Additionally, our study underscores the pivotal role of STVI in gauging low vegetation stress in high-elevation and high-temperature and high-rainfall zones, with stress levels gradually increasing as elevation and rainfall decreases. The combined map confirms that the upper part of the study area, characterized by high elevation and significant rainfall, is classified as having moderate to high vegetation cover, whereas the lower part, with lower elevation and less rainfall, exhibits the opposite.

Overall, this research underscores the imperative of investigating and addressing climate change in historically overlooked regions like Tharparkar. Our findings elucidate the significant impact of climatic factors on vegetation cover in this area, contributing to its susceptibility to drought and harsh environmental conditions. This study serves as a clarion call to prioritize measures for climate change mitigation and adaptation to safeguard the ecological health and resilience of Tharparkar and similar regions. Utilizing GIS technology, this approach identifies regions experiencing extreme temperatures, analyzes the impact of global warming, and evaluates changes in terrestrial cover due to climate change. It facilitates the identification of areas most vulnerable to climate change, aiding in better natural resource management. Additionally, advocating for adequate government funding and resources is crucial to support research in this field, which is essential for understanding and mitigating climate change's effects on vegetation.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.