History of land use in India during 1880–2010: Large-scale land transformations reconstructed from satellite data and historical archives

AbstractIn India, human population has increased six-fold from 200 million to 1200 million that coupled with economic growth has resulted in significant land use and land cover (LULC) changes during 1880–2010. However, large discrepancies in the existing LULC datasets have hindered our efforts to better understand interactions among human activities, climate systems, and ecosystem in India. In this study, we incorporated high-resolution remote sensing datasets from Resourcesat-1 and historical archives at district (N=590) and state (N=30) levels to generate LULC datasets at 5arc minute resolution during 1880–2010 in India. Results have shown that a significant loss of forests (from 89millionha to 63millionha) has occurred during the study period. Interestingly, the deforestation rate was relatively greater under the British rule (1880–1950s) and early decades after independence, and then decreased after the 1980s due to government policies to protect the forests. In contrast to forests, cropland area has increased from 92millionha to 140.1millionha during 1880–2010. Greater cropland expansion has occurred during the 1950–1980s that coincided with the period of farm mechanization, electrification, and introduction of high yielding crop varieties as a result of government policies to achieve self-sufficiency in food production. The rate of urbanization was slower during 1880–1940 but significantly increased after the 1950s probably due to rapid increase in population and economic growth in India. Our study provides the most reliable estimations of historical LULC at regional scale in India. This is the first attempt to incorporate newly developed high-resolution remote sensing datasets and inventory archives to reconstruct the time series of LULC records for such a long period in India. The spatial and temporal information on LULC derived from this study could be used by ecosystem, hydrological, and climate modeling as well as by policy makers for assessing the impacts of LULC on regional climate, water resources, and biogeochemical cycles in terrestrial ecosystems

A new two-decade (2001–2019) high-resolution agricultural primary productivity dataset for India

The present study describes a new dataset that estimates seasonally integrated agricultural gross primary productivity (GPP). Several models are being used to estimate GPP using remote sensing (RS) for regional and global studies. Using biophysical and climatic variables (MODIS, SBSS, ECWMF reanalysis etc.) and validated by crop statistics, the present study provides a new dataset of agricultural GPP for monsoon and winter seasons in India for two decades (2001–2019). This dataset (GPPCY-IN) is based on the light use efficiency (LUE) principle and applied a dynamic LUE for each year and season to capture the seasonal variations more efficiently. An additional dataset (NGPPCY-IN) is also derived from crop production statistics and RS GPP to translate district-level statistics at the pixel level. Along with validation with crop statistics, the derived dataset was also compared with in situ GPP estimations. This dataset will be useful for many applications and has been created for estimating integrated yield loss by taking GPP as a proxy compared to resource and time-consuming field-based methods for crop insurance

Landslide susceptibility mapping along road corridors in the Indian Himalayas using Bayesian logic regression models

Landslide susceptibility mapping (LSM) along road corridors in the Indian Himalayas is an essential exercise that helps planners and decision makers in determining the severity of probable slope failure areas. Logistic regression is commonly applied for this purpose, as it is a robust and straightforward technique that is relatively easy to handle. Ordinary logistic regression as a data-driven technique, however, does not allow inclusion of prior information. This study presents Bayesian logistic regression (BLR) for landslide susceptibility assessment along road corridors. The methodology is tested in a landslide-prone area in the Bhagirathi river valley in the Indian Himalayas. Parameter estimates from BLR are compared with those obtained from ordinary logistic regression. By means of iterative Markov Chain Monte Carlo simulation, BLR provides a rich set of results on parameter estimation. We assessed model performance by the receiver operator characteristics curve analysis, and validated the model using 50% of the landslide cells kept apart for testing and validation. The study concludes that BLR performs better in posterior parameter estimation in general and the uncertainty estimation in particular

Stochastic landslide vulnerability modeling in space and time in a part of the northern Himalayas, India

Little is known about the quantitative vulnerability analysis to landslides as not many attempts have been made to assess it comprehensively. This study assesses the spatio-temporal vulnerability of elements at risk to landslides in a stochastic framework. The study includes buildings, persons inside buildings, and traffic as elements at risk to landslides. Building vulnerability is the expected damage and depends on the position of a building with respect to the landslide hazard at a given time. Population and vehicle vulnerability are the expected death toll in a building and vehicle damage in space and time respectively. The study was carried out in a road corridor in the Indian Himalayas that is highly susceptible to landslides. Results showed that 26% of the buildings fall in the high and very high vulnerability categories. Population vulnerability inside buildings showed a value >0.75 during 0800 to 1000 hours and 1600 to 1800 hours in more buildings that other times of the day. It was also observed in the study region that the vulnerability of vehicle is above 0.6 in half of the road stretches during 0800 hours to 1000 hours and 1600 to 1800 hours due to high traffic density on the road section. From this study, we conclude that the vulnerability of an element at risk to landslide is a space and time event, and can be quantified using stochastic modeling. Therefore, the stochastic vulnerability modeling forms the basis for a quantitative landslide risk analysis and assessment

Temporal Variations of Atmospheric CO in Dehradun, India during 2009

The present study reports the temporal variations of CO 2 mixing ratio measured using Vaisala GMP-343 sensor (at 15 m height) in Dehradun (30.1 °N, 77.4 °E) during 2009. Being a valley station, the mixing ratios are controlled by biospheric processes but not by large scale transport phenomenon or local pollution. A distinct diurnal cycle varies from 317.9 ppm in the afternoon to 377.2 ppm in the morning (before sunrise). The minimum early morning (0700-1000 IST) drop and minimum afternoon (1300-1700 IST) trough observed during monsoon months are related to the enhanced vegetation activity due to rain at the site. The maximum night time (2200 IST to next day 0700 IST) build up of CO 2 observed during monsoon season is associated with the increase in heterotrophic respiration due to high moisture content in the soil. This is also confirmed by the positive coherence between night time CO 2 mixing ratio with soil respiration simulated from Carnagie-Ames-Standford Approach (CASA) model. The strong negative coherence with net ecosystem productivity (simulated from the same model) shows that observations captured the regional changes in emission and uptake of CO 2 in atmosphere

Modelling of sugarcane yield using LISS-IV data based on ground LAI and yield observations

Crop acreage estimation and forecasting the yield of agricultural crops before harvest is of prime importance to a nation with a well-organized food and agricultural economy. Discrimination vigor assessment and yield estimation are some of the main concern of remote sensing applications. Sugarcane in India is a high priority crop for the Government. The aim of this study is to discuss the possibility of sugarcane plant yield estimation using an empirical relationship derived leaf area index (LAI) and farm scale sugarcane plant yield. The ground measurements of sugarcane LAI have been taken with the help of Accupar LP-80 Ceptometer instrument. A strong exponential relation (R 2 = 0.861) is observed between ground measured LAI and LISS-IV sensor NDVI. The yield model is developed using regression analysis between plot-wise yield data with LISS-IV LAI data. This empirical yield model has been found to give a reasonably fair indication (R 2 = 0.714) of the expected yield of sugarcane in advance

Spatial distribution of forest biomass carbon (above and below ground) in Indian forests

Forest carbon (C) estimates are the key inputs to the understanding of the global C cycle. We report the estimates of forest carbon pool and its spatial distribution in the Indian forests for the years 1994 and 2010 at 5 km grid level. This study improves upon earlier spatial estimates of Indian forest biomass carbon by using data from a robustly designed National Forest Inventory (NFI). The realized sampling intensity has addressed the large heterogeneity of the Indian forest types and allowed the computation of 5 km grid level forest C, yielding a realistic estimate of forest biomass C in Indian forests. Forest cover density maps were intersected with 5 km mesh and estimates of forest area, forest carbon density for each Agro-ecological sub region and forest carbon pools were linked to the 5 km grid coverage of India. National forest carbon estimates for the years 1994 and 2010 are 3911.78 and 4368.03 TgC respectively, and these estimates showed a net increase of 456.25 TgC in 16 years. Uncertainty of the estimates has been addressed spatially. Mean forest carbon density increased from 61.14 Mg ha−1 in 1994 to 64.08 Mg ha−1 in 2010. C densities for dense and open forest in 1994 estimated as 77.08 and 38.47 Mg ha−1 with total C pools of 2895.28 TgC and 1016.50 TgC which has increased to 80.24 Mg ha−1 and 41.69 Mg ha−1 with total C pools of 3176.48 TgC and 1191.55 TgC in 2010. This study provides the first 5 km level C analysis for Indian forests. Spatial distribution of C shows large differences in C density over Indian forests indicating that estimates of the spatial distribution of C are even more important than the total C pool estimates of the countr

Satellite and in situ observations of a phytoplankton bloom from coastal Bay of Bengal: role in pCO₂ modulation

A phytoplankton bloom was monitored in coastal waters of Bay of Bengal and its influence in water column properties was investigated. Significant draw down of CO₂ was noted within the vicinity of the bloom associated with high chlorophyll biomass. Microscopic analysis revealed diatoms as the dominant population. Skeletonema costatum a diatom, reached cell density of 36,898 cells l⁻¹ within the bloom. The lowest surface pCO₂ observed was 287 μatm at the southern end of the transect covarying with surface chlorophyll of 1.090 μg l⁻¹ . At the northern end the surface pCO₂ went as low as 313 μatm. The pCO₂ levels below the mixed layer increased twice of that of surface value (∼600 μatm). The chlorophyll values observed by Ocean Colour Monitor-2 were modestly related with the in situ measurements. The primary productivity derived from growth rate, assimilation number and maximum surface chlorophyll was 160.6 mg C m⁻² day⁻¹ leading to a modest sequestration ∼of 0.08 Gg of carbon per day by the surface waters. Our observations reflects the potential role of diatom blooms on coastal carbon dynamics therefore should be carefully monitored in realm of anthropogenic changes