Estimation of agricultural burned affected area using NDVI and dNBR satellite-based empirical models

Detecting Burn Severity and Vegetation Recovery After Fire Using dNBR and dNDVI Indices: Insight from the Bosco Difesa Grande, Gravina in Southern Italy

Wildfires serve a paradoxical role in landscapes-supporting biodiversity and nutrient cycling while also threatening ecosystems and economies, especially as climate change intensifies their frequency and severity. This study investigates the impact of wildfires and vegetation recovery in the Bosco Difesa Grande forest in southern Italy, focusing on the 2017 and 2021 fire events. Using Google Earth Engine (GEE) accessed in January 2025, we applied remote sensing techniques to assess burn severity and post-fire regrowth. Sentinel-2 imagery was used to compute the Normalized Burn Ratio (NBR) and Normalized Difference Vegetation Index (NDVI); burn severity was derived from differenced NBR (dNBR), and vegetation recovery was monitored via differenced NDVI (dNDVI) and multi-year NDVI time series. We uniquely compare recovery across four zones with different fire histories-unburned, single-burn (2017 or 2021), and repeated-burn (2017 and 2021)-providing a novel perspective on post-fire dynamics in Mediterranean ecosystems. Results show that low-severity zones recovered more quickly than high-severity areas. Repeated-burn zones experienced the slowest and least complete recovery, while unburned areas remained stable. These findings suggest that repeated fires may shift vegetation from forest to shrubland. This study highlights the importance of remote sensing for post-fire assessment and supports adaptive land management to enhance long-term ecological resilience.

Air pollution and respiratory health risks in Jharkhand: a remote sensing and statistical approach

Air pollution has been worse since the Industrial Revolution and is now a significant health issue around the world. In this study, we have focused on assessing spatiotemporal changes in air pollution and its impact on public health status in Jharkhand using MODIS aerosol optical depth (AOD), concentration of particulate matter like PM2.5 and respirable suspended particulate matter (PM10), SO2, and NO2, along with detailed statistics of number of asthma patients collected from the Annual Health Survey, Census of India. The study reveals that most districts in the northern and eastern boundaries of the state have higher concentrations of PM2.5 and AOD values, while the southwestern part has lower concentrations. The highest decadal mean ± SD AOD was found in Godda district (0.432 ± 0.05), while the lowest was in Gumla district (0.202 ± 0.02). Potential regional variations in air quality are highlighted by the assessment of PM2.5 concentrations, which shows a distinct geographical pattern with both decadal and annual mean levels consistently increasing from the southwestern region to the eastern and northern portions of the study region. Aerosol loading increased by 13.95% and 2.945% over the study area from 2010 to 2013, respectively. The predominance of RSPM in-ground monitoring stations was observed, with annual concentrations higher than the national ambient air quality level due to proximity to mining zones or industrial activities. Jamshedpur and Dhanbad cities also showed higher annual concentrations of NO2. The study found that urban populations are 49.75% more affected than rural populations, with male-urban populations being more affected by 23.60% and female-urban populations by 58.26%. The average growth rate of total patients diagnosed with asthma was 92.84% and 90.13% from 2010 to 2013, respectively. Rural populations are more affected by air pollution due to their involvement in anthropogenic activities like wood and metal workshops, brick kilns, construction work, mining, and cement industries. Thus, both the short- and long-term studies infer that the variation in the mean concentration of PM2.5 and AOD throughout the study region is positively and significantly correlated with the number of persons who reported having asthma. The study provides a detailed insight into air pollution assessment and its impacts on public health, helping the government and policymakers adopt new policies to mitigate and manage air pollution levels and improve public health status in the study area.

Assessing cropping system dynamics over three decades: remote sensing and GIS insights in Murshidabad-Jiaganj Block

Agriculture is a significant contributor to the country's economic development. We used multiple Landsat images from 1990 to 2021 in the Murshidabad-Jiaganj Block to assess changes in the agricultural system and their underlying causes. The Rabi season saw a 10.99% growth in agrarian regions from 1990 to 2000 and an 8.86% increase in 2010, yet it declined by 28.12% in 2021. During the summer, the cultivated lands diminished by 26.63%, 19.43%, and 19.64%, while in the Kharif season, they declined by 21.78%, 15.68%, and 11.99% from 1990 in the years 2000, 2010, and 2021, respectively. The agricultural area had 36.82%, 34.16%, and 19.01% increases between 1990 and 2021, respectively. Regarding direction, farmland acreage decreased in all zones except the SSE, which had a 0.95% increase. Mono-, double-, and triple-cropping systems have decreased in area, while multi-cropping systems have experienced increases of 43.51%, 4.50%, and 18.49% in 1990-2021, respectively. The multi-cropping system has a good correlation with all agroclimatic factors. The reduction of irrigated lands post-2009 significantly affected the agriculture system. The fall in agricultural employment in recent decades is attributable to migration seeking higher-paying occupations. The advancement of accurate remote sensing-based modeling is crucial for mitigating food security risks, particularly those posed by climate change, and informing policy decisions.

Spatial and statistical analysis of burned areas with Landsat-8/9 and Sentinel-2 satellites: 2023 Çanakkale forest fires

Forest fires are one of the most dangerous disasters that threaten the natural environment, life, and diversity worldwide. The frequency of these fires and the size of the impact area have been increasing in recent years. Remote sensing methods are frequently used to detect areas affected by forest fires, to map the burned areas, to follow the course of fires, and to reveal verious statistical data. In this study, forest fires that occurred on 16.07.2023 and 22.08.2023 in Çanakkale province were analyzed using Landsat-8/9 and Sentinel-2 satellite images and various remote sensing indices. By using the images before and after the fires, the burned areas were determined and the performance of different indices were compared. The areas affected by fires were revealed using dNBR (Differenced Normalized Burn Ratio), RBR (Relative Burn Ratio), and dNDVI (Differenced Normalized Difference Vegetation Index) indices. The fire-affected areas were calculated as 3,244.41 hectares (ha) and 4,292.37 ha for the July and August fires with Landsat-8/9 images, respectively; and 3,312.08 ha and 4,445.03 ha with Sentinel-2 images, respectively. In addition, the accuracy analysis of the areas calculated using different indices was performed. By comparing the results of the analysis and accuracy assessment, the performances of Landsat-8/9 and Sentinel-2 images were determined. According to the results obtained, the Overall Accuracy values of the areas affected by fires were between 0.76 - 0.89, Kappa statistical values were between 0.52 - 0.78, and the highest value in the calculation of the burned areas was the dNBR index for both Landsat-8/9 and Sentinel-2 images.

Mapping burned areas in Thailand using Sentinel-2 imagery and OBIA techniques

Monitoring burned areas in Thailand and other tropical countries during the post-harvest season is becoming increasingly important. High-resolution remote sensing data from Sentinel-2 satellites, which have a short revisit time, is ideal for accurately and efficiently mapping burned regions. However, automating the mapping of agriculture residual on a national scale is challenging due to the volume of information and level of detail involved. In this study, a Sentinel-2A Level-1C Multispectral Instrument image (MSI) from February 27, 2018 was combined with object-based image analysis (OBIA) algorithms to identify burned areas in Mae Chaem, Chom Thong, Hod, Mae Sariang, and Mae La Noi Districts in Chiang Mai, Thailand. OBIA techniques were used to classify forest, agricultural, water bodies, newly burned, and old burned regions. The segmentation scale parameter value of 50 was obtained using only the original Sentinel-2A band in red, green, blue, near infrared (NIR), and Normalized Difference Vegetation Index (NDVI). The accuracy of the produced maps was assessed using an existing burned area dataset, and the burned area identified through OBIA was found to be 85.2% accurate compared to 500 random burned points from the dataset. These results suggest that the combination of OBIA and Sentinel-2A with a 10 m spatial resolution is very effective and promising for the process of burned area mapping.