Spatio-temporal analysis of forest cover in Dausa District, Rajasthan (2015-2024) using remote sensing and GIS Techniques

The study examines forest-cover dynamics in Dausa District, Rajasthan, over a decade using Landsat‑8 surface reflectance data processed into annual NDVI composites. Its purpose is to develop cloud-free time-series data from 2015 to 2024, classify NDVI values into density‑based categories—very dense, dense, moderate, open and non‑forest—and map spatial-temporal changes in vegetation cover across the district. The authors downloaded and atmospherically corrected multi-temporal Landsat‑8 imagery within Google Earth Engine and applied NDVI calculations using near-infrared and red bands. Annual mean composites were then imported into ArcGIS to classify vegetation densities and assess changes. Results show that mean NDVI values ranged from sparse to dense vegetation, reflecting moderate greenness typical of semi-arid environments. Dense and moderately vegetated areas expanded slightly, while open and degraded scrub categories declined, yielding a net gain of approximately 60 km² of vegetated land between 2015 and 2024. Positive NDVI trends were observed in the southern uplands and eastern piedmont, associated with afforestation efforts and favourable rainfall, whereas negative trends occurred near urban fringes and mining clusters. The findings highlight how rainfall variability, geomorphology and human pressures jointly influence vegetation dynamics, demonstrating the value of remote sensing and GIS in guiding sustainable forest management.

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Very Dense Vegetation Cover: Area (2015) Km2 vs Area (2024) Km2Close ×

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Dense Vegetation Cover: Area (2015) Km2 vs Area (2024) Km2Close ×

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Introduction

Forests represent one of the most essential and dynamic ecosystems on the Earth's surface, playing a pivotal role in maintaining ecological stability, regulating hydrological and biogeochemical cycles, and supporting biodiversity and human livelihoods (Feng et al., 2024) [3] . They significantly influence regional and global climate systems through carbon sequestration, evapotranspiration, and albedo modulation (Vento et al., 2023) [13] . In developing nations such as India, forests also provide crucial ecosystem services-fuelwood, fodder, minor forest products, and soil protection-that directly bolster the rural economy (González De Andrés, 2019) [4] . However, rapid population growth, urban expansion, and unsustainable land-use practices have accelerated forest degradation and fragmentation, particularly in ecologically fragile regions (Prasad et al., 2001; Tiwari, 2008) [10,12] . Although India's forest cover has increased marginally over recent decades, it remains spatially uneven (Tiwari, 2008) [12] . The arid and semi-arid regions of western India, particularly Rajasthan, are characterized by low natural vegetation and significant anthropogenic pressure. Rajasthan comprises nearly 10 percent of India's geographical area but contains less than 5 percent of its total forest cover. In this context, continuous monitoring of forest dynamics is essential for evaluating environmental changes and formulating effective conservation strategies (Colantoni et al., 2020) [2] . Dausa District, situated in the northeastern region of Rajasthan, serves as a transitional ecological zone between the Aravalli uplands and the eastern plains. The district's forest resources are limited and fragmented, primarily located on undulating hillocks and degraded scrublands dominated by species such as Acacia nilotica, Prosopis juliflora, and Azadirachta indica. The district's land-cover composition has been significantly altered over the past few decades due to increasing agricultural encroachment, grazing pressure, mining activities, and infrastructural development (Saini & Mohd, 2025) [11] .

These changes have impacted vegetation density, surface runoff, and micro-climatic conditions, highlighting the necessity for precise spatio-temporal assessment. Remote Sensing (RS) and Geographic Information Systems (GIS) serve as robust methodologies for the detection, quantification, and mapping of environmental changes (Kandissounon et al., 2018) [6] . The utilization of multitemporal satellite imagery facilitates consistent monitoring of the Earth's surface, while vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), are instrumental in capturing variations in photosynthetic activity and canopy health (Obrecht et al., 2023) [9] . The Landsat series, particularly Landsat 8 Operational Land Imager (OLI) and Landsat 9, provides radiometrically calibrated, high-resolution surface reflectance data, which is well-suited for decadal-scale monitoring at the district level. (Jia et al., 2014; Kumar & Roy, 2017) [5,7] . The integration of NDVI with spatial analysis techniques facilitates the identification of forest-cover transitions and the detection of subtle ecological trends (Masek et al., 2006) [8] . In this context, the current study endeavors to perform a spatio-temporal analysis of forest cover in Dausa District from 2015 to 2024, utilizing Landsat-derived NDVI composites within a GIS framework. The specific objectives are as follows: to generate cloud-free NDVI datasets for the period 2015 to 2024 using Landsat 8 surface reflectance imagery; to classify NDVI values into density-based forest categories (very dense, dense, moderate, open, and nonforest) and quantify their areal extent; and to analyze spatial and temporal patterns of vegetation change, identifying zones of forest gain and loss. This study aims to provide empirical evidence on vegetation dynamics in a semi-arid environment and to illustrate the application of geospatial technologies for local-level environmental assessment. The findings will aid in evaluating the effectiveness of afforestation programs, watershed management initiatives, and land-use policies, thereby offering a scientific basis for sustainable forest management in Dausa and comparable districts across Rajasthan.

Study Area

Dausa District lies in the north-eastern part of Rajasthan between 26°20′-27°00′ N and 76°00′-77°20′ E, covering about 3,404 km². Physiographically, it forms a transitional terrain between the Aravalli uplands and the eastern alluvial plains, marked by gently undulating plateaus and scattered hillocks. The district experiences a semi-arid climate with hot summers (up to 45 °C), mild winters, and an average annual rainfall of 600-700 mm, mostly during the southwest monsoon. With a population exceeding 1.6 million, the economy is primarily agrarian, while natural resources include limited dry deciduous forests, minor mineral deposits, and groundwater aquifers supporting irrigation and rural livelihoods.

Materials and Methods

This study employed a combination of Remote Sensing and Geographic Information System (GIS) techniques to quantify and interpret the spatial and temporal dynamics of forest cover in Dausa District from 2015 to 2024. Multitemporal satellite imagery was analyzed to derive the Normalized Difference Vegetation Index (NDVI), which served as a proxy indicator for vegetation density and health.

Data Sources

The analysis employed Landsat-8 Operational Land Imager (OLI) Collection-2 Level-2 Surface Reflectance data, accessed via the Google Earth Engine (GEE) platform (Abdelsamie et al., 2024; Zafar et al., 2025) [1,14] . Two datasets, from the years 2015 and 2024, encompassing the entire district, were downloaded. Each scene was selected with less than 10 percent cloud cover to ensure radiometric clarity and temporal consistency. Ancillary data, including administrative boundaries, drainage, and elevation layers, were obtained from the Survey of India and FAO-GAUL databases to facilitate spatial contextualization in ArcGIS.

NDVI Derivation and Image Processing

The NDVI was calculated using the standard equation:

Where the NIR and RED bands correspond to Band 5 (0.85-0.88 µm) and Band 4 (0.64-0.67 µm) of Landsat-8 OLI, respectively. All images were pre-processed for atmospheric correction using the radiometric scaling factors provided in the Landsat metadata (0.0000275 * SR + (-0.2)). Cloud and shadow pixels were masked through the QA_PIXEL bitmask. For each year, multiple scenes were composited to form a mean NDVI image, which minimizes the influence of residual cloud, haze, or phenological anomalies and provides a stable representation of annual vegetation conditions. These mean composites were clipped to the Dausa district boundary and exported in GeoTIFF format for further spatial analysis.

Spatial Analysis in ArcGIS

The annual NDVI composites were imported into ArcGIS Pro 3.5 for statistical and cartographic processing. NDVI values were classified into five forest-density categories: very dense (> 0.70), dense (0.5-0.70), moderate (0.3-0.5), open (0.0-0.3), and non-forest (< 0.0). The area under each class was calculated in square kilometres using raster attribute tables and zonal statistics. Temporal overlays (2015 vs 2024) were generated to delineate zones of forest gain and loss. Spatial visualization was performed using a standardized green-to-brown colour ramp, and map layouts were prepared following cartographic conventions for scale, legend, and north orientation.

Analytical Framework

The workflow integrated temporal NDVI quantification (2015-2024) with spatial overlay analysis to detect vegetation dynamics. The resulting dataset enabled statistical correlation of NDVI changes with physiographic and anthropogenic factors, providing a scientific foundation for evaluating forest cover trends in Dausa's semi-arid environment.

Results

Results

NDVI Dynamics (2015-2024)

The decadal NDVI analysis of Dausa District reveals perceptible yet spatially uneven changes in vegetation density over the period 2015-2024. The annual mean NDVI values ranged between -0.19 and 0.817 indicating a predominance of sparse to dense vegetation typical of semi-arid environments. The inter-annual trend exhibits minor fluctuations corresponding to rainfall variability, with relatively higher NDVI values recorded during the good monsoon years. Spatially, the southern and eastern hill tracts of Mahwa and Baswa blocks exhibited relatively dense vegetation, whereas the central and north-western plains around Dausa city and Lalsot showed lower NDVI values due to intensive cultivation, settlement expansion, and mining activity. The spatial continuity of forest patches remains fragmented, although scattered greening is evident along seasonal stream channels and reforested community lands.

NDVI-Based Forest Density Classification

The NDVI composites were categorized into five forestdensity categories: very dense (> 0.70), dense (0.5-0.70), moderate (0.3-0.5), open (0.0-0.3), and non-forest (< 0.0). Table 1 summarizes the areal distribution of these classes during 2015 and 2024. Overall, the NDVI-based assessment demonstrates that while Dausa remains predominantly under non-forest and open-vegetation cover, there has been gradual ecological improvement during the last decade. However, the gains are spatially discontinuous and require further validation through ground observations and integration with land-use and rainfall datasets.

Forest Class	Area (2015) Km2	Area (2024) Km2	Change in Area Km2
Very Dense Vegetation Cover	0	0.01	0.01
Dense Vegetation Cover	37.13	60.83	23.7
Moderate Vegetation Cover	2472.75	2493.21	20.46
Low Vegetation Cover	905.29	860.22	-45.07
No Vegetation	0.49	1.42	0.93
Total	3415.66	3415.69	0.03

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NDVI-Derived Forest Cover in Dausa District

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Discussion

The Overall, the discussion highlights that while Dausa exhibits a modest greening trend over the last decade, the gains remain spatially fragmented and ecologically fragile.

Continued anthropogenic pressure, erratic rainfall, and overdependence on marginal lands threaten long-term sustainability. The integration of remote-sensing-based vegetation monitoring with participatory afforestation and watershed planning can provide a comprehensive strategy for enhancing forest resilience in the district. The findings thus demonstrate the efficacy of NDVI-based geospatial analysis as a diagnostic and policy-support tool for regional environmental governance.

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Dausa District Forest Cover (2015) using NDVI Value

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Dausa District Forest Cover (2024) using NDVI Value

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