Satellites Reveal How an Indian Himalayan City Swallowed Its Farmland

A satellite-based investigation spanning more than two decades has documented one of the most dramatic land transformations recorded for a medium-sized Himalayan city, revealing that Jammu City in northern India expanded its built-up footprint by 75 percent between 2002 and 2024, largely at the expense of productive farmland and dense vegetation. The study, published in the journal Discover Cities by geographers Rahoof Ahmed and Mohammad Taufique of Aligarh Muslim University, provides one of the most detailed long-term portraits yet of how urbanization is reshaping the landscapes at the edge of the Indian Himalayas, where topographic constraints funnel development pressure onto the very agricultural lands that sustain surrounding communities.

The researchers harnessed the Landsat satellite record, drawing on cloud-free imagery from Landsat 7’s Enhanced Thematic Mapper Plus in 2002, and Landsat 8’s Operational Land Imager in 2014 and 2024. All scenes were acquired under similar seasonal conditions to minimize the distortions that seasonal plant growth and phenology can introduce into land-cover comparisons. The team restricted its analysis to a fixed area of interest of 14,481 hectares, defined by the city’s earlier municipal boundary, ensuring that every change measured over the 22-year window reflected genuine land transformation rather than shifts in the study area itself. By holding the geographic frame constant, the analysis achieved a level of temporal consistency that many shorter-term mapping efforts lack.

At the technical heart of the study lies supervised Maximum Likelihood Classification, a statistical workhorse of remote sensing that treats the spectral response of each land-cover type as a normally distributed signature and assigns every 30-meter pixel to the category with the highest probability of membership. Six classes were mapped across all three epochs: agricultural land, built-up area, dense vegetation, sparse vegetation, fallow land and water bodies. Training areas were identified by cross-referencing the satellite scenes with historical high-resolution imagery from Google Earth and local knowledge of the landscape. In total, 4,976 observations were used to train the classifier, split in an 80:20 ratio with an independent reserve of 1,244 validation observations that were withheld entirely from the training process and used solely to test the resulting maps.

The accuracy assessment was deliberately rigorous. Using stratified random sampling, the researchers compiled confusion matrices for each study year and quantified user’s accuracy, producer’s accuracy, overall accuracy and the Kappa coefficient, a statistic that measures agreement between classified maps and reference data after correcting for chance agreement. The classified maps achieved overall accuracies of 94.37 percent in 2002, 95.10 percent in 2014 and 96.12 percent in 2024, with Kappa values of 0.919, 0.928 and 0.932 respectively. The only notable weakness appeared in the 2014 sparse vegetation class, where a user’s accuracy of 60 percent against a producer’s accuracy of 92.31 percent revealed commission errors stemming from the spectrally overlapping signatures of agricultural land and dense vegetation in medium-resolution imagery. Even so, the metrics comfortably exceed the thresholds generally accepted for reliable multi-temporal change detection.

The results tell a striking story. Built-up land grew from 3,326 hectares, or 23 percent of the study area, in 2002 to 5,825 hectares, or 40.2 percent, in 2024, a net addition of 2,499 hectares. Agricultural land followed a more complex trajectory, rising from 6,927 hectares in 2002 to a peak of 7,854 hectares in 2014 before collapsing to 5,181 hectares by 2024, leaving a net decline of 1,746 hectares over the full period. Dense vegetation fell from 9.4 percent of the area to just 5.1 percent, while fallow land plummeted by 86.7 percent. Perhaps most tellingly, sparse vegetation more than doubled, climbing 106.8 percent to cover 17.3 percent of the city by 2024, an increase the authors interpret not as environmental recovery but as the visible signature of degrading, fragmenting plant cover caught in the grip of advancing urbanization.

The study’s most methodologically valuable contribution comes from its transition matrix analysis, which goes beyond net change statistics to trace the specific pathways through which one land class converted into another. Of the agricultural land lost to development, a substantial 1,465.7 hectares was converted directly to built-up area, making farmland by far the largest reservoir of new urban land. Fallow land contributed 627 hectares to the growing city, sparse vegetation 243.6 hectares and dense vegetation 159 hectares. Meanwhile, 513.9 hectares of dense vegetation degraded into sparse cover before any construction occurred, revealing a two-stage process in which vegetation is first thinned and fragmented, then ultimately converted. This degradation pathway, often invisible in conventional net-change mapping, offers planners an early warning signal of land destined for development.

The spatial geography of expansion proved equally revealing. New growth concentrated overwhelmingly in the city’s southern and south-eastern peri-urban zones, following transport corridors in ribbon-like and dispersed patterns, while the rugged Shivalik foothills, the Tawi River, and extensive defence and institutional land holdings constrained development towards the north and north-west. The researchers argue that this corridor-oriented growth reflects the interplay of topography, road connectivity and land ownership, forces that have channeled Jammu’s expansion into its most productive agricultural fringe. Population dynamics amplify these pressures: the 2011 Census recorded more than 1.5 million inhabitants in Jammu district, and the city’s growth over recent decades has been shaped by natural increase, rural-to-urban migration and the large-scale displacement of people from the Kashmir Valley during the 1990s.

The environmental implications extend well beyond the loss of scenic greenery. Shrinking dense vegetation and declining water coverage, which fell from 0.2 percent to 0.1 percent of the area, can fragment wildlife habitats, reduce groundwater recharge and increase surface runoff, heightening exposure to urban flooding in a city that sits at the sensitive transition between hills and plains. The conversion of peri-urban farmland threatens local food production and the livelihoods of communities dependent on agriculture, a pattern the authors note has been documented in fast-urbanizing regions across India and globally. Comparable satellite-based studies from Delhi, Aligarh, Jamshedpur and other Indian cities report the same signature of built-up expansion driving agricultural loss and rising land-surface temperatures, suggesting Jammu is a particularly well-documented case of a nationwide phenomenon.

The authors propose a concrete planning agenda grounded in their findings. Stronger land-use zoning and enforcement are needed to shield productive farmland from unregulated conversion, while compact development and better use of existing infrastructure could curb the sprawl that now fragments the urban fringe. They call for embedding geospatial monitoring into routine city planning, expanding green belts, parks and ecological corridors to reverse vegetation decline, and adopting rainwater harvesting and sustainable urban drainage to counter the hydrological effects of sealing land under concrete. Looking forward, the team suggests that finer-resolution data from Sentinel-2 or drone platforms, combined with predictive models such as Cellular Automata-Markov and machine-learning approaches, could help Jammu and similar Himalayan cities simulate future growth scenarios and plan before the next 2,500 hectares disappear under construction.

The Landsat program, jointly operated by NASA and the U.S. Geological Survey since 1972, underpins studies of this kind because it offers the longest continuous, freely available archive of moderate-resolution satellite imagery of Earth’s land surface. The 30-meter spatial resolution of the sensors used here is fine enough to resolve individual fields, road corridors and neighborhood-scale development, yet broad enough to cover the entire municipal area in a single scene, which is why it remains the standard for multi-decadal land-change research in rapidly growing cities.

The choice of the Maximum Likelihood classifier reflects both practicality and comparability. Although newer machine-learning algorithms such as random forests and support vector machines often achieve marginally higher accuracies, parametric statistical classifiers remain widely used in operational mapping because they require relatively modest training samples, behave predictably across dates, and allow results from different studies to be compared on a common methodological footing. The near-identical Kappa values across all three epochs suggest the classification pipeline was stable over time, an important precondition for attributing observed differences to real land change rather than methodological drift.

Jammu’s position as winter capital of the Union Territory of Jammu and Kashmir adds institutional weight to these findings. Administrative functions, security establishments and transport investments concentrated in the city have historically attracted migration from surrounding rural districts and from the Kashmir Valley, compounding the demographic pressure that drives land conversion. The city’s subtropical climate, with hot summers and monsoon-concentrated rainfall, means that replacing vegetated and agricultural surfaces with impervious cover can sharply alter local thermal and hydrological regimes, intensifying both heat stress and runoff during storm events.

The study’s open-access publication also matters for practice. Because the underlying Landsat data are free and the methods rely on widely available GIS software, the analytical framework can be replicated by municipal planners, state agencies and academic groups in other medium-sized Himalayan and plains cities facing similar pressures, extending the evidence base for land-use policy well beyond a single case study.

Subject of Research: Long-term geospatial analysis of land use and land cover transformation and urban expansion in Jammu City, India

Article Title: Geospatial analysis of spatio-temporal land transformation and urban expansion in Jammu city

Article References: Ahmed, R., & Taufique, M. (2026). Geospatial analysis of spatio-temporal land transformation and urban expansion in Jammu city. Discover Cities, 3(1), Article 184. https://doi.org/10.1007/s44327-026-00368-z

Image Credits: AI Generated

DOI: 10.1007/s44327-026-00368-z

Keywords: land use land cover, urban expansion, remote sensing, GIS, Landsat, Jammu City, change detection, Maximum Likelihood Classification, per-urban agriculture, Himalayan foothills, transition matrix, sustainable urban planning