Urbanization in India is rapidly increasing. Dispersed development along highways or surrounding cities and in rural areas is resulting in serious loss of agricultural land, open space, waterbodies, and ecologically sensitive habitats. The management and monitoring of such resources require an understanding of change in land use and land cover. Vadodara City in Gujarat, India, has been chosen as the study area because it is a rapidly developing city with increasing urbanization and industrialization. Rapid growth of the population and haphazard expansion of local planning has led to substantial loss of agricultural land, resulting in several land-use changes. Such changes have been analysed using remotely sensed data in conjunction with conventional data. This study was carried out to detect the changes that have occurred in the study area over a period of 129 years (1880–2009) at the Vadodara Mahanagar Seva Sadan (VMSS) level, i.e. the inner city level, and for 126 years (1880–2006) at the Vadodara Urban Development Authority (VUDA) level, i.e. the greater city area. The results reveal that the city expanded from 9.14 km² in 1880 to 136.68 km² in 2006, exhibiting a high rate of urbanization. Estimation of various variables, such as Shannon's entropy (SE), the urban sprawl index (USI), land consumption rate (LCR), and land absorption coefficient (LAC) related to urbanization, brought out interesting facts about the city. The results show that sprawl is high. This sprawl has evenly dispersed distribution across the space with absorption of land increasing over a period of time, but at the same time the absorbed land requires more planning and proper utilization as evident from lower LCR values. Such an understanding is a prerequisite for the sustainable planning required to counteract the perceived negative social, economic, and environmental impacts of urban sprawl.

Forest degradation is considered to be one of the major threats to forests over the globe, which has considerably increased in recent decades. Forests are gradually getting fragmented and facing biodiversity losses because of climate change and anthropogenic activities. Future prediction of forest degradation spatiotemporal dynamics and fragmentation is imperative for generating a framework that can aid in prioritizing forest conservation and sustainable management practices. In this study, a random forest algorithm was developed and applied to a series of Landsat images of 1998, 2008, and 2018, to delineate spatiotemporal forest cover status in the sanctuary, along with the predictive model viz. the Cellular Automata Markov Chain for simulating a 2028 forest cover scenario in Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, India. The model’s predicting ability was assessed using a series of accuracy indices. Moreover, spatial pattern analysis—with the use of FRAGSTATS 4.2 software—was applied to the generated and predicted forest cover classes, to determine forest fragmentation in SWS. Change detection analysis showed an overall decrease in dense forest and a subsequent increase in the open and degraded forests. Several fragmentation metrics were quantified at patch, class, and landscape level, which showed trends reflecting a decrease in fragmentation in forest areas of SWS for the period 1998 to 2028. The improvement in SWS can be attributed to the enhanced forest management activities led by the government, for the protection and conservation of the sanctuary. To our knowledge, the present study is one of the few focusing on exploring and demonstrating the added value of the synergistic use of the Cellular Automata Markov Chain Model Coupled with Fragmentation Statistics in forest degradation analysis and prediction.

Information on phenological metrics of individual plant species is meager. Phenological metrics generation for a specific plant species can prove beneficial if the species is ecologically or economically important. Teak, a dominating tree in most regions of the world has been focused on in the present study due to its multiple benefits. Forecasts on such species can attain a substantial improvement in their productivity. MODIS NDVI time series when subjected to statistical smoothing techniques exhibited good output with Tukey’s smoothing (TS) with a low RMSE of 0.042 compared to single exponential (SE) and double exponential (DE). Phenological metrics, namely, the start of the season (SOS), end of the season (EOS), maximum of the season (MAX), and length of the season (LOS) were generated using Tukey-smoothed MODIS NDVI data for the years 2003–2004 and 2013–2014. Post shifts in SOS and EOS by 14 and 37 days respectively with a preshift of 28 days in MAX were observed in the year 2013–2014. Preshift in MAX was accompanied by an increase in greenness exhibiting increased NDVI value.LOS increased by 24 days in the year 2013–2014, showing an increase in the duration of the season of teak. Dates of these satellite-retrieved phenological occurrences were validated with ground phenological data calculated using crown cover assessment. The present study demonstrated the potential of a spatial approach in the generation of phenometrics for an individual plant species, which is significant in determining productivity or a crucial trophic link for a given region.