Normalized Burn Ratio Research Articles

Unraveling the Relationship Between Stubble Burning and Air Quality Degradation in Punjab: A Temporal and Spatial Analysis (2019-2022)

Stubble burning in Punjab, India, poses significant environmental challenges, particularly impacting air quality. This study aims to examine the spatial and temporal patterns of stubble burning events and their potential effect on ambient air quality from 2019 to 2022. High-resolution Sentinel-2 satellite imagery was employed to delineate the spatial extent of stubble burning. Burnt areas were identified using the Normalised Burn Ratio (NBR). Air quality was evaluated based on PM2.5 and PM10 concentrations data obtained from the Punjab Pollution Control Board. The Inverse Distance Weighting (IDW) interpolation technique was used to estimate pollution values in areas lacking direct monitoring. The study revealed significant year-to-year variations in areas affected by stubble burning. The smallest burnt areas were recorded in October 2019 and 2021 (209 sq km), while the largest was in 2020 (755.38 sq km). In every year studied, the burnt area in November consistently exceeded that in October, with the largest area (10315 sq km) observed in 2021. PM2.5 and PM10 concentrations also showed annual fluctuations, with the highest recorded in 2020 and 2021. In particular, in October 2020, higher PM2.5 and PM10 levels were detected in the eastern region of Punjab. November consistently exhibited higher PM2.5 and PM10 concentrations than October for all years analysed, peaking in 2021. The spatial and temporal variations of stubble burning events and their relationship with air quality highlight the need for targeted interventions. Understanding these patterns is crucial for mitigating the adverse effects of stubble burning on air quality in Punjab, India. Future research should focus on evaluating the effectiveness of various mitigation strategies.

Quantifying the effectiveness of shaded fuel breaks from ground-based, aerial, and spaceborne observations

Shaded fuel breaks are treatments that aim to mitigate wildfires by establishing linearly aligned locations where wildfire suppression efforts can be more effective at stopping wildfires. Despite the potential of fuel breaks to alter fire behavior, there have been limited quantitative assessments of their effectiveness following exposure to wildfires. In addition, wildfires often occurin complex terrains that are difficult to access with ground vehicles and sensors, posing challenges for data acquisition. However, the use of Remote-controlled Aerial Vehicles (RAVs), such as drones, is becoming increasingly popular as a viable means of conducting high-resolution observations in areas of interest. This study presents the results from a unique opportunity to utilize three distinct observation scale platforms (in-situ, aerial, and spaceborne) to investigate the burn severity impacts across a prior shaded fuel break that serendipitously encountered the 2020 Creek Fire in the Sierra Nevada forests of California, USA. To provide a direct measure of fire severity, ground-based measurements determined the percentage crown volume (PCV) of scorch and char as a function of distance from the fuel break edge. Along five transects of the fuel break, we also utilized visible bands from drone imagery and digital photogrammetry, to generate georeferenced orthophotos and quantify vegetation health using the Green Leaf Index (GLI). We also quantified burn severity by computing the Delta Normalized Burn Ratio (dNBR) and vegetation health using the Normalized Difference Vegetation Index (NDVI) from Sentinel 2 spaceborne observations. Our results indicate that within the fuel break, the PCV of char is 2 × less than it was outside of it (with PCV char declining at a rate of 2% per 3 m into the fuel break). Burn severity is 5 × less, and vegetation health is approximately 3 × greater within the fuel break compared to directly outside. Furthermore, postfire vegetation health was only 1 × less within the fuel break compared to the pre fire condition, whereas it was 5 × less in the surrounding region. The results confirm that the fuel break altered the fire behavior, reducing the fire intensity, thereby proving effective at reducing fire burn severity and preserving vegetation health within the fuel break.

Comparison of Forest Restorations with Different Burning Severities Using Various Restoration Methods at Tuqiang Forestry Bureau of Greater Hinggan Mountains

Forest disturbances and restoration are key processes in carbon transmission between the terrestrial surface and the atmosphere. In boreal forests, fire is the most common and main disturbance. The reconstruction process for post-disaster vegetation plays an essential role in the restoration of a forest’s structure and function, and it also maintains the ecosystem’s health and stability. Remote sensing monitoring could reflect dynamic post-fire features of vegetation. However, there are still major differences in the remote sensing index in terms of regional feasibility and sensibility. In this study, the largest boreal primary coniferous forest area in China, the Greater Hinggan Mountains forest area, was chosen as the sampling area. Based on time series data from Landsat-5 TM surface reflectance (SR) and data obtained from sample plots, the burned area was extracted using the Normalized Burn Ratio (NBR). We used the pre- and post-fire difference values (dNBR) and compared them with survey data to classify the burn severity level. The Normalized Difference Vegetation Index (NDVI) (based on spectrum combination) and the Disturbance Index (DI) (based on Tasseled-Cap transformation) were chosen to analyze the difference in the degree of burn severity and vegetation restoration observed using various methods according to the sequential variation feature from 1986 to 2011. The results are as follows: (1) The two remote sensing indexes are both sensitive to fire and the burn severity level. When a fire occurred, the NDVI value for that year decreased dramatically while the DI value increased sharply. Alongside these findings, we observed that the rangeability and restoration period of the two indexes is significantly positively correlated with the degree of burn severity. (2) According to these two indexes, natural vegetation restoration was faster than the restoration achieved using artificial methods. However, compared with the NDVI, the DI showed a clearer improvement in restoration, as the restoration period the DI could evaluate was longer in two different ways: the NDVI illustrated great changes in the burn severity in the 5 years post-fire, while the DI was able to show the changes for more than 20 years. Additionally, from the DI, one could identify felling activities carried out when the artificial restoration methods were initially applied. (3) From the sample-plot data, there were few differences in forest canopy density—the average was between 0.55 and 0.6—between the diverse severity levels and restoration methods after 33 years of recovery. The average diameter at breast height (DBH) and height values of trees in naturally restored areas decreased with the increase in burn severity, but the values were obviously higher than those in artificially restored areas. This indicates that both the burn severity level and restoration methods have important effects on forest restoration, but the results may also have been affected by other factors.

An Approach Integrating Multi-Source Data with LandTrendr Algorithm for Refining Forest Recovery Detection

Disturbances to forests are getting worse with climate change and urbanization. Assessing the functionality of forest ecosystems is challenging because it requires not only a large amount of input data but also comprehensive estimation indicator methods. The object of the evaluation index of forest ecosystem restoration relies on the ecosystem function instead of the area. To develop the appropriate index with ecological implications, we built the hybrid assessment approach including ecosystem structure-function-habitat representatives. It was based on the Normalized Burn Ratio (NBR) spectral indicator and combined with the local forest management inventory (LFMI), Landsat, Light Detection and Ranging (LiDAR) data. The results of the visual interpretation of Google Earth’s historical imagery showed that the total accuracy of the hybrid approach was 0.94. The output of the hybrid model increased as the biodiversity index value increased. Furthermore, to solve the multi-source data availability problem, the random forest model (R2 = 0.78, RMSE = 0.14) with 0.77 total accuracy was built to generate an annual recovery index. A random forest model based on tree age is provided to simplify the hybrid approach while extending the results on time series. The recovery index obtained by the random forest model could facilitate monitoring the forest recovery rate of cold spots. The regional ecological recovery time could be predicted. These two results could provide a scientific basis for forest managers to make more effective forest restoration plans. From the perspective of space, it could ensure that the areas with slow recovery would be allocated enough restoration resources. From the perspective of time, the implementation period of the closed forest policy could also be estimated.

NBR Index-Based Fire Detection Using Sentinel-2 Images and GIS: A Case Study in Mosul Park, Iraq

Forest fires lead to severe damage to the environment and human health. Monitoring can be applied using remotely sensed data and in combination with Geographical Information Systems (GIS) based spatial analysis. Lately, Iraq subjected to many forest fires. In this study, the aim was to monitor and detect the burned areas in Mosul Park during the latest period which happened in June 2022. The hypothesis of the study was based on using Sentinel-2 images and the Normalized Burn Ratio (NBR) index. Two images have been used to compare burned areas; one during the fire events and another postfire. as well as, Normalized Difference Vegetation Index (NDVI) map has been used to identify the Park's characteristics. Moreover, Pearson's correlation (r) with Air Quality Index (AQI) was determined during the burning period. GIS-based processes resulted in detecting the area of burning where the burned area was 16.76 hectares and lay in the eastern part of the study area. Pearson correlation with AQI has resulted in 0.92, while the collinearity between the burned areas and AQI was 0.84. Accurate and prompt planning for fire-affected regions is essential for supporting fire affect assessment, calculating environmental losses, determining planning strategies, and monitoring vegetation recovery.

Estimation of Rubber Yield Using Sentinel-2 Satellite Data

Rubber is a perennial plant grown to produce natural rubber. It is a raw material for industrial and non-industrial products important to the world economy. The sustainability of natural rubber production is, therefore, critical for smallholder livelihoods and economic development. To maintain price stability, it is important to estimate the yields in advance. Remote sensing technology can effectively provide large-scale spatial data; however, productivity estimates need to be processed from high spatial resolution data generated from satellites with high accuracy and reliability, especially for smallholder livelihood areas where smaller plots contrast with large farms. This study used reflectance data from Sentinel-2 satellite imagery acquired for the 12 months between December 2020 and November 2021. The imagery included 213 plots where data on rubber production in smallholder agriculture were collected. Six vegetation indices (Vis), namely Green Soil Adjusted Vegetation Index (GSAVI), Modified Simple Ratio (MSR), Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Normalized Green (NR), and Ratio Vegetation Index (RVI) were used to estimate the rubber yield. The study found that the red edge spectral band (band 5) provided the best prediction with R2 = 0.79 and RMSE = 29.63 kg/ha, outperforming all other spectral bands and VIs. The MSR index provided the highest coefficient of determination, with R2 = 0.62 and RMSE = 39.25 kg/ha. When the red edge reflectance was combined with the best VI, MSR, the prediction model only slightly improved, with a coefficient determination of (R2) of 0.80 and an RMSE of 29.42 kg/ha. The results demonstrated that the Sentinel-2 data are suitable for rubber yield prediction for smallholder farmers. The findings of this study can be used as a guideline to apply in other countries or areas. Future studies will require the use of reflectance and vegetation indices derived from satellite data in combination with meteorological data, as well as the application of complex models, such as machine learning and deep learning.

What is the color when black is burned? Quantifying (re)burn severity using field and satellite remote sensing indices

BackgroundTrends of increasing area burned in many regions worldwide are leading to more locations experiencing short-interval reburns (i.e., fires occurring two or more times in the same place within 1–3 decades). Field and satellite indices of burn severity are well tested in forests experiencing a single recent fire, but the reliability of these indices in short-interval reburns is poorly understood. We tested how a commonly used field index (the Composite Burn Index, CBI) and satellite index (the Relative differenced Normalized Burn Ratio, RdNBR) compared to eight individual field measures of burn severity in short-interval reburns vs. areas burned in one recent fire, and whether results depended on whether the first fire was stand replacing (fire that is lethal to most dominant trees).ResultsCorrespondence between both CBI and RdNBR with individual burn severity measures differed in short-interval reburns compared to single fires for some metrics of burn severity. Divergence in the relationship between both CBI and RdNBR vs. field measures was greatest when short-interval reburns followed a prior stand-replacing fire, and measures were more comparable to single fires when the first fire was non-stand replacing (i.e., lower severity). When short-interval reburns followed prior stand-replacing fires, CBI and RdNBR underestimated burn severity in the second fire for tree-canopy metrics (e.g., canopy cover loss, tree mortality), as young forests in early developmental stages are more sensitive to a second fire. Conversely, when short-interval reburns followed prior less-than-stand-replacing fires, both CBI and RdNBR overestimated burn severity for forest-floor metrics, as past low severity fires leave behind live fire-resistant trees and can stimulate resprouting understory vegetation. Finally, neither CBI nor RdNBR accurately detected deep wood charring—an important phenomenon that occurs in short-interval reburns.ConclusionOur findings inform interpretability of commonly used indices of burn severity in short-interval reburns by identifying how individual burn severity metrics can be under- or over-estimated, depending on the severity of the fire preceding a reburn. Adjustments to burn severity measurements made in short-interval reburns are particularly critical as reburned areas increase.

Fire intensity impacts on physiological performance and mortality in Pinus monticola and Pseudotsuga menziesii saplings: a dose-response analysis.

Fire is a major cause of tree injury and mortality worldwide, yet our current understanding of fire effects is largely based on ocular estimates of stem charring and foliage discoloration, which are error prone and provide little information on underlying tree function. Accurate quantification of physiological performance is a research and forest management need, given that declining performance could help identify mechanisms of-and serve as an early warning sign for-mortality. Many previous efforts have been hampered by the inability to quantify the heat flux that a tree experiences during a fire, given its highly variable nature in space and time. In this study, we used a dose-response approach to elucidate fire impacts by subjecting Pinus monticola var. minima Lemmon and Pseudotsuga menziesii (Mirb.) Franco var. glauca (Beissn.) Franco saplings to surface fires of varying intensity doses and measuring short-term post-fire physiological performance in photosynthetic rate and chlorophyll fluorescence. We also evaluated the ability of spectral reflectance indices to quantify change in physiological performance at the individual tree crown and stand scales. Although physiological performance in both P. monticola and P. menziesii declined with increasing fire intensity, P. monticola maintained a greater photosynthetic rate and higher chlorophyll fluorescence at higher doses, for longer after the fire. Pinus monticola also had complete survival at lower fire intensity doses, whereas P. menziesii had some mortality at all doses, implying higher fire resistance for P. monticola at this life stage. Generally, individual-scale spectral indices were more accurate at quantifying physiological performance than those acquired at the stand-scale. The Photochemical Reflectance Index outperformed other indices at quantifying photosynthesis and chlorophyll fluorescence, highlighting its potential use to quantify crown scale physiological performance. Spectral indices that incorporated near-infrared and shortwave infrared reflectance, such as the Normalized Burn Ratio, were accurate at characterizing stand-scale mortality. The results from this study were included in a conifer cross-comparison using physiology and mortality data from other dose-response studies. The comparison highlights the close evolutionary relationship between fire and species within the Pinus genus, assessed to date, given the high survivorship of Pinus species at lower fire intensities versus other conifers.

Benchmarking Sentinel-2-derived predictors for long-term burn severity modelling: the 2016–17 Chilean firestorm

ABSTRACT Remotely sensed burn severity modelling is commonly addressed by regressing fire-induced forest biophysical damage measured in the field, using the Composite Burn Index (CBI) method on a pre- to post-fire difference of the normalized burn ratio (∆NBR) representing early fire effects. Although less often addressed, it may also be modelled on a wider post-fire temporal range to assess longer-lasting ecological effects. However, this approach may involve a larger set of potential predictors, posing a high-dimensional statistical issue. This study examines the performance of different spectrotemporal combinations of remotely sensed predictors for long-term burn severity modelling of the two largest wildfires during the central Chilean firestorm in the summer of 2017, in order to assess their predictive contribution and to support their optimal selection. Sentinel-2 images acquired annually in 2016–2020 were used to create different spectrotemporal combinations of predictor sets, encompassing ∆NBR, differenced normalized difference water index (∆NDWI) and differenced normalized difference vegetation index (∆NDVI), as well as differenced bands. Thereafter, they were modelled by CBI data collected in the field, using conventional (least-squares-based) and ridge-regularized linear regressions, and assessed by spatial cross-validation. Among the multiple-variable models, ridge regression outperformed multiple linear regression, the highest accuracies being achieved by the multitemporal sets comprising any of the differenced index types. Among these, ∆NBRs reached the lowest average root mean square error (RMSEavg = 0.591). Results confirm the importance of ∆NBR as burn severity predictor in a long-term assessment context, top-performing for both, single- and multiple-variable models. They also highlight the value of tracking the temporal trajectory of this differenced index when enlarging the number of post-fire image dates involved, and hence, no a-priori knowledge about what predictor to select exists.

Characterizing spatial burn severity patterns of 2016 Chimney Tops 2 fire using multi-temporal Landsat and NEON LiDAR data

The Chimney Tops 2 wildfire (CT2) in 2016 at Great Smoky Mountains National Park (GSMNP) was recorded as the largest fire in GSMNP history. Understanding spatial patterns of burn severity and its underlying controlling factors is essential for managing the forests affected and reducing future fire risks; however, this has not been well understood. Here, we formulated two research questions: 1) What were the most important factors characterizing the patterns of burn severity in the CT2 fire? 2) Were burn severity measures from passive and active optical remote sensing sensors providing consistent views of fire damage? To address these questions, we used multitemporal Landsat- and lidar-based burn severity measures, i.e., relativized differenced Normalized Burn Ratio (RdNBR) and relativized differenced Mean Tree Height (RdMTH). A random forest approach was used to identify key drivers in characterizing spatial variability of burn severity, and the partial dependence of each explanatory variable was further evaluated. We found that pre-fire vegetation structure and topography both play significant roles in characterizing heterogeneous mixed burn severity patterns in the CT2 fire. Mean tree height, elevation, and topographic position emerged as key factors in explaining burn severity variation. We observed generally consistent spatial patterns from Landsat- and lidar-based burn severity measures. However, vegetation type and structure-dependent relations between RdNBR and RdMTH caused locally inconsistent burn severity patterns, particularly in high RdNBR regions. Our study highlights the important roles of pre-fire vegetation structure and topography in understanding burn severity patterns and urges to integrate both spectral and structural changes to fully map and understand fire impacts on forest ecosystems.

Evaluating the potential of burn severity mapping and transferability of Copernicus EMS data using Sentinel-2 imagery and machine learning approaches

ABSTRACT The abiotic and biotic conditions in forest ecosystems can be significantly influenced by forest fires. However, difficulties in policy decisions for restoration inevitably occur in the absence of information on the damaged forests, such as location, area, and burn severity. In this study, eight spectral indices calculated from Sentinel 2 MSI imagery and machine learning algorithms (Random Forest (RF) and Support Vector Machine (SVM)) were used for mapping burned areas and severity. Two study sites with similar meteorological environment (dry season) and species (coniferous vegetation) were tested, and dataset (EMSR448) from Copernicus Emergency Management Service (CEMS) was used as the reference truth. RF showed better performance for classifying pixels from classes with similar properties than SVM. Normalized Burn Ratio (NBR) and Green Normalized Difference Vegetation Index (GNDVI) showed high importance in assessing fire severity suggesting that it may be effective for identifying senescent plants. The results also confirmed that the CEMS dataset has transferability as a reference truth for fire damage classification in other regions. Implementation of this method enables fast and accurate mapping of the area and severity of destructive damage by forest fires, and also has applicability for other disasters.

Analysis of Spectral Separability for Detecting Burned Areas Using Landsat-8 OLI/TIRS Images under Different Biomes in Brazil and Portugal

Fire is one of the natural agents with the greatest impact on the terrestrial ecosystem and plays an important ecological role in a large part of the terrestrial surface. Remote sensing is an important technique applied in mapping and monitoring changes in forest landscapes affected by fires. This study presents a spectral separability analysis for the detection of burned areas using Landsat-8 OLI/TIRS images in the context of fires that occurred in different biomes of Brazil (dry ecosystem) and Portugal (temperate forest). The research is based on a fusion of spectral indices and automatic classification algorithms scientifically proven to be effective with as little human interaction as possible. The separability index (M) and the Reed–Xiaoli automatic anomaly detection classifier (RXD) allowed the evaluation of the spectral separability and the thematic accuracy of the burned areas for the different spectral indices tested (Burn Area Index (BAI), Normalized Burn Ratio (NBR), Mid-Infrared Burn Index (MIRBI), Normalized Burn Ratio 2 (NBR2), Normalized Burned Index (NBI), and Normalized Burn Ratio Thermal (NBRT)). The analysis parameters were based on spatial dispersion with validation data, commission error (CE), omission error (OE), and the Sørensen–Dice coefficient (DC). The results indicated that the indices based exclusively on the SWIR1 and SWIR2 bands showed a high degree of separability and were more suitable for detecting burned areas, although it was observed that the characteristics of the soil affected the performance of the indices. The classification method based on bitemporal anomalous changes using the RXD anomaly proved to be effective in increasing the burned area in terms of temporal alteration and performing unsupervised detection without relying on the ground truth. On the other hand, the main limitations of RXD were observed in non-abrupt changes, which is very common in fires with low spectral signal, especially in the context of using Landsat-8 images with a 16-day revisit period. The results obtained in this work were able to provide critical information for fire mapping algorithms and for an accurate post-fire spatial estimation in dry ecosystems and temperate forests. The study presents a new comparative approach to classify burned areas in dry ecosystems and temperate forests with the least possible human interference, thus helping investigations when there is little available data on fires in addition to favoring a reduction in fieldwork and gross errors in the classification of burned areas.

Fractional vegetation cover ratio estimated from radiative transfer modeling outperforms spectral indices to assess fire severity in several Mediterranean plant communities

The obtention of wall-to-wall fire severity estimates through reliable remote sensing-based techniques that align with management needs is a critical factor in post-fire decision-making processes. In this paper, we novelty proposed a multi-date change detection framework based on the variation in fractional vegetation cover (FCOVER), with enough ecological sense and physical basis to be generalizable across different plant communities and burned landscapes with varying environmental conditions. This framework meets the definition of fire severity operationally used in the field as a biophysical indicator when fire effects on the understory and overstory layers are linked. The FCOVER was retrieved from Sentinel-2 surface reflectance scenes by inverting PROSAIL-D radiative transfer model (RTM) simulations using the random forest regression algorithm. FCOVER retrievals were validated in the field using burned and unburned control plots. We computed the FCOVERr metric as the ratio of post-fire to pre-fire FCOVER. We tested the relationship of the FCOVERr and the most common bi-temporal spectral indices in the literature, i.e. the differenced Normalized Burn Ratio (dNBR), the Relative dNBR (RdNBR) and the Relativized Burn Ratio (RBR), with the Composite Burn Index (CBI) measured in field plots for validation purposes in two case-study wildfires in the western Mediterranean Basin. We also calculated the transferability of FCOVERr and the spectral indices between different plant communities within each site, as well as between sites. The predictive errors of pre and post-fire FCOVER retrievals were found to be low (RMSE ≈ 10%) for the two study sites. Overall, the FCOVERr metric provided more accurate CBI estimations (R2 = 0.87 ± 0.04) than spectral indices (R2 = 0.71 ± 0.13). The CBI was linearly related with the FCOVERr metric for both sites, whereas the type of relationship with spectral indices was not consistent, which translated into better transferability performance of the FCOVERr metric (nRMSE = 14.27% ± 3.75%) than that of the spectral indices (nRMSE = 21.97% ± 8.09%), not only between different Mediterranean plant communities within sites, but also between the two sites. Spectral indices underestimated moderate to high fire severity to a greater extent than FCOVERr in the CBI field plots, and misclassified fire severity in several areas with patchiness fire effects identified in the field. The FCOVERr product proposed in this study may be a sound choice for the operational identification of priority areas for post-fire management.

GAN-based SAR and optical image translation for wildfire impact assessment using multi-source remote sensing data

Despite the popularity and success in burned area detection and assessment, multispectral satellite images are often affected by poor sunlight-illumination conditions, particularly at high latitudes. Given that Synthetic Aperture Radar (SAR) can effectively penetrate clouds and collect images in all-weather conditions during day and night, the complementary use of optical and SAR data can be helpful for remote-sensing measurements and assessments of burned sites. Nevertheless, the widely used burn-sensitive spectral indices hardly help analyze SAR data due to the inherent difference between optical and SAR sensors in physical imaging mechanisms. In this study, we aim to leverage multi-source data for burned area mapping and burn severity assessment by translating SAR images into optical images using ResNet-based Pix2Pix model. Experiments were performed on 8669 pairs of bitemporal Sentinel-1 SAR and Sentinel-2 optical patches over 304 large wildfire events in Canada with a wide range of land covers. The translated optical images from SAR data occupied similar spectral properties to real optical observations and the corresponding generated spectral indices (i.e., delta Normalized Burn Ratio (dNBR), relative dNBR, and Relativized Burn Ratio) also showed high agreement with real optical indices. In terms of burned area detection using the generated indices, their medium values of the area under the receiver operating characteristics curve (AUC) were over 85%, achieving promising performance that outperformed the SAR-based index. Burn severity maps derived from multi-source data achieved a relatively high Kappa coefficient of 0.77. Results showed the feasibility and effectiveness of GAN-based SAR-to-optical translation for wildfire impact assessment, paving the way for the combined use of optical and SAR data.

MAPPING OF FOREST AND LAND FIRE HAZARDOUS USING LANDSAT 8 SATELLITE IMAGERY WITH LAND SURFACE TEMPERATURE (LST) AND NORMALIZED BURN RATIO (NBR) METHODS

This study aims (1) to determine the distribution of Land Surface Temperature (LST) in the Baso District in 2022 (2) to determine the Normalized Burn Ratio (NBR) in Baso District in 2022 (3) to map areas prone to forest and land fires by utilizing the Land Surface Temperature (LST) and Normalized Burn Ratio (NBR) algorithms in Baso District in 2022.
 This study uses the Land Surface Temperature (LST) method to determine the distribution of land surface temperatures in the Baso District in 2022. The Normalized Burn Ratio (NBR) method is used to identify areas that are burned and then weighted overlay using Arcgis to obtain data on land and forest fire vulnerability. in Baso District.
 The results of this study are (1) showing a minimum temperature value of 13.6oC maximum temperature of 34.5oC and an average temperature of 26oC (2) showing the results of the distribution of areas with a value of -1 which are identified as burnt or those with bad vegetation of 2.5 and areas with a value of 0 indicating vegetation a good area of ​​7,636 Ha (3) on the mapping of areas prone to forest and land fires after the Weighted Overlay was carried out found 4 classes of vulnerability levels not prone to forest and land fires, moderately prone, prone and very prone to forest and land fires.

Estimation of the burned area with severity and its influencing factors for wildfire using Sentinel-2 satellite imagery

Wildfire is a natural disaster that harms human and animal habitats and the socio-economy. Remote sensing techniques are commonly used in the research of natural disasters, natural resources and monitoring. Timely and accurate estimation of the location of forest fires is particularly important for post-fire management and decision-making. Sentinel-2 satellite images of the European Space Agency ‘ESA’ were used to estimate the area affected by forest fires at Bayan-Uul and Bayandun soums in Dornod province, and classified the burn severity levels and comparison with other influencing factors in this study. The normalized burn ratio ‘NBR' and indices on pre-fire and post-fire were calculated. The total burned area was calculated as 58,131.6 ha, and low, moderate-low, moderate-high, and high burn severity levels cover 15,423.7 ha (26.3%), 29,529.4 ha (50.4%), 13,160.2 ha (22.5%), and 18.3 ha (0.03%), respectively. The 87.6% of the burned area is situated in Mongolian territory, while the remaining area (12.4%) belongs to the Russian Federation. Comparing 10 natural and geographical factors that can influence the burn severity and calculating the correlation coefficients by Pearson. Four of them related a positive lower, and six of them related negative lower. The weak relationships of Normalized Difference Vegetation Index ‘NDVI’, elevation were 0.4 and 0.23. However, the precipitation correlation was -0.22 (negative weak). The distribution of wildfire is strongly influenced by the wind, and the correlation coefficient demonstrates a negative correlation with no effect on combustion. The conditions of socio-economic and ecological disastrous consequences such as loss of plant species and resources, changes in plant structure, depletion of pasture resources, extinction of rare animals and plants, reduction of forest resources, and large-scale air pollution resulting in the loss of human and animal in post-fire. Therefore, this research is important to due for studying the burning, distribution and, coverage area of the fire, and create conditions for the prevention of future risks.

DYNAMIC OF CHANGING AREA OF SUSPENDED SOLID BY UTILIZING LANDSAT 8 OIL IMAGES IN LAKE SINGKARAK, WEST SUMATRA PROVINCE, 2017 and 2022

TSS is suspended materials (diameter > 1 µm) retained on a millipore filter with a pore diameter of 0.45 µm. TSS consists of silt and fine sand and micro-organisms. The main cause of TSS in waterways is soil erosion or soil erosion that is carried into water bodies. If the TSS concentration is too high, it will inhibit the penetration of light into the water and result in disruption of the photosynthesis process (Effendi in Lestari, 2009:4). Many activities cause turbidity that affects the penetration of sunlight into water bodies, so it can hinder the process of photosynthesis and primary production of waters. Turbidity usually consists of an organic particle originating from watershed erosion and resuspension from the lake bottom. Keywords : Normalized Difference Vegetation Index, Normalized Burn Ratio, Landsat 8, Severity Level of Forest and Land Fires. Based on the results of the study, researchers have obtained TSS values ​​in 2017 and 2022 at Lake Singkarak with the Landsat 8 image data processing method using the Syarif Budiman algorithm with several stages, namely first combining image data bands from band 1 to band 7 then cropping which serves to determine the area to examine then performs masking which functions to separate land from water and then enter the Syarif Budiman algorithm formula then classify the TSS values ​​in Lake Singkarak. It can be seen that the predicted TSS concentration has not been too much different from the TSS concentration in the field. researchers have obtained TSS values ​​in 2017 and 2022 at Lake Singkarak with the Landsat 8 image data processing method using the Syarif Budiman algorithm with several stages, namely first combining image data bands from band 1 to band 7 then cropping which functions to determine the area which will be examined then do masking which functions to separate land from water and then enter the Syarif Budiman algorithm formula then classify the TSS values ​​in Lake Singkarak. It can be seen that the predicted TSS concentration has not had too much difference in the concentration in the field. researchers have obtained TSS values ​​in 2017 and 2022 at Lake Singkarak with the Landsat 8 image data processing method using the Syarif Budiman algorithm with several stages, namely first combining image data bands from band 1 to band 7 then cropping which functions to determine the area which will be examined then do masking which functions to separate land from water and then enter the Syarif Budiman algorithm formula then classify the TSS values ​​in Lake Singkarak. It can be seen that the predicted TSS concentration has not to have o much difference with wififrameS concentration in the field.

UTILIZING LANDSAT 8 IMAGERY FOR MAPPING OF BURNED AREAS USING THE NORMALIZE DIFFERENCE VEGETATION INDEX (NDVI) AND NORMALIZE BURN RATIO (NBR) METHODS

This study aims (1) to map changes in the area of ​​forest land in the western part of Bengkalis Regency in 2016 and 2021, (2) to determine the distribution of the area of ​​forest burned in the western part of Bengkalis Regency, (3) to determine the severity of forest fires in the District of Bengkalis West Bengkalis.This study used the NDVI (Normalized Difference Vegetation Index) method by Huete et. Al by compositing band 5 (NIR) and band 4 (Red) on Landsat 8 imagery which was processed using ArcGIS software before and after a forest fire. As well as the NBR (Normalized Burn Ratio) and dNBR (Difference Normalized Burn Ratio) methods by Eidenshink et al by compositing band 5 (NIR) and band 7 (SWIR) on Landsat 8 images processed using QGIS software. For sampling using random sampling method and accuracy test using overall accuracy, user's accuracy, producer's accuracy, and kappa analysis. The results of this study are (1) the area of ​​forest land in Bengkalis Regency continues to decrease every year, in 2016 the area of ​​forest land903,920 ha and 2021 the total forest area is463,441 ha. (2)The area of ​​forest land burned due to forest fires in Bengkalis Regency, which burned the least was 267.43 ha, while it was 1468.93 ha and the most extensive was 2186.53 ha.(3) Based on one forest fire distribution map, it is divided into 7 fire severity classes, namely high post-fire regrowth, low post-fire regrowth, no burning, low, medium-high and very high and the most dominant forest fire level is low-high.

Landslide Susceptibility Assessment of a Part of the Western Ghats (India) Employing the AHP and F-AHP Models and Comparison with Existing Susceptibility Maps

Landslides are prevalent in the Western Ghats, and the incidences that happened in 2021 in the Koottickal area of the Kottayam district (Western Ghats) resulted in the loss of 10 lives. The objectives of this study are to assess the landslide susceptibility of the high-range local self-governments (LSGs) in the Kottayam district using the analytical hierarchy process (AHP) and fuzzy-AHP (F-AHP) models and to compare the performance of existing landslide susceptible maps. This area never witnessed any massive landslides of this dimension, which warrants the necessity of relooking into the existing landslide-susceptible models. For AHP and F-AHP modeling, ten conditioning factors were selected: slope, soil texture, land use/land cover (LULC), geomorphology, road buffer, lithology, and satellite image-derived indices such as the normalized difference road landslide index (NDRLI), the normalized difference water index (NDWI), the normalized burn ratio (NBR), and the soil-adjusted vegetation index (SAVI). The landslide-susceptible zones were categorized into three: low, moderate, and high. The validation of the maps created using the receiver operating characteristic (ROC) technique ascertained the performances of the AHP, F-AHP, and TISSA maps as excellent, with an area under the ROC curve (AUC) value above 0.80, and the NCESS map as acceptable, with an AUC value above 0.70. Though the difference is negligible, the map prepared using the TISSA model has better performance (AUC = 0.889) than the F-AHP (AUC = 0.872), AHP (AUC = 0.867), and NCESS (AUC = 0.789) models. The validation of maps employing other matrices such as accuracy, mean absolute error (MAE), and root mean square error (RMSE) also confirmed that the TISSA model (0.869, 0.226, and 0.122, respectively) has better performance, followed by the F-AHP (0.856, 0.243, and 0.147, respectively), AHP (0.855, 0.249, and 0.159, respectively), and NCESS (0.770, 0.309, and 0.177, respectively) models. The most landslide-inducing factors in this area that were identified through this study are slope, soil texture, LULC, geomorphology, and NDRLI. Koottickal, Poonjar-Thekkekara, Moonnilavu, Thalanad, and Koruthodu are the LSGs that are highly susceptible to landslides. The identification of landslide-susceptible areas using diversified techniques will aid decision-makers in identifying critical infrastructure at risk and alternate routes for emergency evacuation of people to safer terrain during an exigency.

Remote sensing technology for rapid extraction of burned areas and ecosystem environmental assessment.

Forest fires are one of the significant disturbances in forest ecosystems. It is essential to extract burned areas rapidly and accurately to formulate forest restoration strategies and plan restoration plans. In this work, we constructed decision trees and used a combination of differential normalized burn ratio (dNBR) index and OTSU threshold method to extract the heavily and mildly burned areas. The applicability of this method was evaluated with three fires in Muli County, Sichuan, China, and we concluded that the extraction accuracy of this method could reach 97.69% and 96.37% for small area forest fires, while the extraction accuracy was lower for large area fires, only 89.32%. In addition, the remote sensing environment index (RSEI) was used to evaluate the ecological environment changes. It analyzed the change of the RSEI level through the transition matrix, and all three fires showed that the changes in RSEI were stronger for heavily burned areas than for mildly burned areas, after the forest fire the ecological environment (RSEI) was reduced from good to moderate. These results realized the quantitative evaluation and dynamic evaluation of the ecological environment condition, providing an essential basis for the restoration, decision making and management of the affected forests.