scholarly journals FUZZY LOGIC BASED BURNED SEVERITY CLASSIFICATION AND MAPPING WITH LANDSAT-8 DATA

2019 ◽  
Vol XLII-2/W16 ◽  
pp. 259-265
Author(s):  
B. Valipour Shokouhi ◽  
M. Eslami
Keyword(s):  
Fuzzy Logic ◽  
Urban Infrastructure ◽  
Training Data ◽  
Burn Severity ◽  
Burned Area ◽  
Landsat 8 ◽  
Area Index ◽  
Average Accuracy ◽  
Multi Temporal ◽  
Normalized Burn Ratio

<p><strong>Abstract.</strong> Wildfire has a strong effect on both land use and land cover so that every year, thousands of hectares of forests, farms, and urban infrastructure are destroyed. Mapping and estimation of damages are crucial for planning and decision making. The aim of this study is classification and mapping burn severity using multi-temporal Landsat data and well-known burn severity indices including Normalized Burn Ratio (NBR) and Burned Area Index (BAI) calculated for pre- and post- Landsat 8 images. Subtracted images such as dNBR (Difference Normalized Burn Ratio), RBR (Relativized Burn Ratio) and dBAI (Difference Burned Area Index) were produced on the bases of indices as classification input. Among classification methods, the fuzzy supervised classification was utilized with three classes. The result shows the strong performance of the Fuzzy Logic system (FLS) in the detection of the area with the limited number of training data so that the average accuracy of the classes is 85%; plus, from the human logic perspective, the result was meaningful so that recognition of the features and changes visually were understandable.</p>

scholarly journals LANDSAT-BASED DETECTION AND SEVERITY ANALYSIS OF BURNED SUGARCANE PLOTS IN TARLAC, PHILIPPINES USING DIFFERENCED NORMALIZED BURN RATIO (dNBR)

2016 ◽  
Vol XLII-4/W1 ◽  
pp. 173-179 ◽  
Author(s):  
A. B. Baloloy ◽  
A. C. Blanco ◽  
B. S. Gana ◽  
R. C. Sta. Ana ◽  
L. C. Olalia
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
The Philippines ◽  
Local Economy ◽  
Burn Severity ◽  
Burned Area ◽  
Landsat 8 ◽  
Potential Yield ◽  
Differenced Normalized Burn Ratio ◽  
Normalized Burn Ratio

The Philippines has a booming sugarcane industry contributing about PHP 70 billion annually to the local economy through raw sugar, molasses and bioethanol production (SRA, 2012). Sugarcane planters adapt different farm practices in cultivating sugarcane, one of which is cane burning to eliminate unwanted plant material and facilitate easier harvest. Information on burned sugarcane extent is significant in yield estimation models to calculate total sugar lost during harvest. Pre-harvest burning can lessen sucrose by 2.7% - 5% of the potential yield (Gomez, et al 2006; Hiranyavasit, 2016). This study employs a method for detecting burn sugarcane area and determining burn severity through Differenced Normalized Burn Ratio (dNBR) using Landsat 8 Images acquired during the late milling season in Tarlac, Philippines. Total burned area was computed per burn severity based on pre-fire and post-fire images. Results show that 75.38% of the total sugarcane fields in Tarlac were burned with post-fire regrowth; 16.61% were recently burned; and only 8.01% were unburned. The monthly dNBR for February to March generated the largest area with low severity burn (1,436 ha) and high severity burn (31.14 ha) due to pre-harvest burning. Post-fire regrowth is highest in April to May when previously burned areas were already replanted with sugarcane. The maximum dNBR of the entire late milling season (February to May) recorded larger extent of areas with high and low post-fire regrowth compared to areas with low, moderate and high burn severity. Normalized Difference Vegetation Index (NDVI) was used to analyse vegetation dynamics between the burn severity classes. Significant positive correlation, rho = 0.99, was observed between dNBR and dNDVI at 5% level (p = 0.004). An accuracy of 89.03% was calculated for the Landsat-derived NBR validated using actual mill data for crop year 2015-2016.

scholarly journals Landsat-8 and Sentinel-2 burned area mapping - A combined sensor multi-temporal change detection approach

2019 ◽  
Vol 231 ◽  
pp. 111254 ◽  
Author(s):  
David P. Roy ◽  
Haiyan Huang ◽  
Luigi Boschetti ◽  
Louis Giglio ◽  
Lin Yan ◽  
...  
Keyword(s):  
Change Detection ◽  
Temporal Change ◽  
Burned Area ◽  
Landsat 8 ◽  
Detection Approach ◽  
Burned Area Mapping ◽  
Multi Temporal ◽  
Combined Sensor ◽  
Sentinel 2

Detecting Burn Severity across Mediterranean Forest Types by Coupling Medium-Spatial Resolution Satellite Imagery and Field Data

2020 ◽  
Vol 12 (4) ◽  
pp. 741 ◽  
Author(s):  
Luigi Saulino ◽  
Angelo Rita ◽  
Antonello Migliozzi ◽  
Carmine Maffei ◽  
Emilia Allevato ◽  
...  
Keyword(s):  
Fire Effects ◽  
Mediterranean Forest ◽  
Burn Severity ◽  
Landsat 8 ◽  
Mediterranean Countries ◽  
Medium Resolution ◽  
Severity Levels ◽  
The Mediterranean ◽  
Sentinel 2A ◽  
Normalized Burn Ratio

In Mediterranean countries, in the year 2017, extensive surfaces of forests were damaged by wildfires. In the Vesuvius National Park, multiple summer wildfires burned 88% of the Mediterranean forest. This unprecedented event in an environmentally vulnerable area suggests conducting spatial assessment of the mixed-severity fire effects for identifying priority areas and support decision-making in post-fire restoration. The main objective of this study was to compare the ability of the delta Normalized Burn Ratio (dNBR) spectral index obtained from Landsat-8 and Sentinel-2A satellites in retrieving burn severity levels. Burn severity levels experienced by the Mediterranean forest communities were defined by using two quali-quantitative field-based composite burn indices (FBIs), namely the Composite Burn Index (CBI), its geometrically modified version CBI (GeoCBI), and the dNBR derived from the two medium-resolution multispectral remote sensors. The accuracy of the burn severity map produced by using the dNBR thresholds developed by Key and Benson (2006) was first evaluated. We found very low agreement (0.15 < K < 0.21) between the burn severity class obtained from field-based indices (CBI and GeoCBI) and satellite-derived metrics (dNBR) from both Landsat-8 and Sentinel-2A. Therefore, the most appropriate dNBR thresholds were rebuilt by analyzing the relationships between two field-based (CBI and GeoCBI) and dNBR from Landsat-8 and Sentinel-2A. By regressing alternatively FBIs and dNBRs, a slightly stronger relationship between GeoCBI and dNBR metrics obtained from the Sentinel-2A remote sensor (R2 = 0.69) was found. The regressed dNBR thresholds showed moderately high classification accuracy (K = 0.77, OA = 83%) for Sentinel-2A, suggesting the appropriateness of dNBR-Sentinel 2A in assessing mixed-severity Mediterranean wildfires. Our results suggest that there is no single set of dNBR thresholds that are appropriate for all burnt biomes, especially for the low levels of burn severity, as biotic factors could affect satellite observations.

scholarly journals Mapping burned areas from landsat-8 imageries on mountainous region using reflectance changes

2018 ◽  
Vol 229 ◽  
pp. 04012
Author(s):  
Suwarsono ◽  
Hana Listi Fitriana ◽  
Indah Prasasti ◽  
Muhammad Rokhis Khomarudin
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Burned Area ◽  
Landsat 8 ◽  
Mountainous Region ◽  
Mountainous Regions ◽  
Burned Areas ◽  
Test And Evaluation ◽  
Normalized Burn Ratio ◽  
D Values

This research tried to detect a burned area that occurred in the mountainous region of Java Island. During this time, forest and land fires mostly occur in lowland areas in Sumatra and Kalimantan. However, it is possible that this phenomenon also occurs in mountainous regions, especially the mountainous regions of Java Island. The data used were Landsat-8, the latest generation of the Landsat series. The research location was on the Northeast slope of Mt. Ijen in East Java. The research methods include radiometric correction, data fusion, sample training retrieval, reflectance pattern analysis, Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR) extraction, separability analysis, parameter selection for burned area detection, parameter test, and evaluation. The results show that ρ5 and NBRL parameter shows the highest values of D-values (most sensitive), to detect the burned area. Then, compared to ρ5, NDVI and NBRS, Normalized Burn Ratio long (NBRL) provide better results in detecting burned areas.

1984–2010 trends in fire burn severity and area for the conterminous US

2016 ◽  
Vol 25 (4) ◽  
pp. 413 ◽  
Author(s):  
Joshua J. Picotte ◽  
Birgit Peterson ◽  
Gretchen Meier ◽  
Stephen M. Howard
Keyword(s):  
Time Series ◽  
Vegetation Classification ◽  
Cumulative Distribution ◽  
Burn Severity ◽  
Burned Area ◽  
Detectable Change ◽  
Historical Trends ◽  
Differenced Normalized Burn Ratio ◽  
Normalized Burn Ratio ◽  
In Fire

Burn severity products created by the Monitoring Trends in Burn Severity (MTBS) project were used to analyse historical trends in burn severity. Using a severity metric calculated by modelling the cumulative distribution of differenced Normalized Burn Ratio (dNBR) and Relativized dNBR (RdNBR) data, we examined burn area and burn severity of 4893 historical fires (1984–2010) distributed across the conterminous US (CONUS) and mapped by MTBS. Yearly mean burn severity values (weighted by area), maximum burn severity metric values, mean area of burn, maximum burn area and total burn area were evaluated within 27 US National Vegetation Classification macrogroups. Time series assessments of burned area and severity were performed using Mann–Kendall tests. Burned area and severity varied by vegetation classification, but most vegetation groups showed no detectable change during the 1984–2010 period. Of the 27 analysed vegetation groups, trend analysis revealed burned area increased in eight, and burn severity has increased in seven. This study suggests that burned area and severity, as measured by the severity metric based on dNBR or RdNBR, have not changed substantially for most vegetation groups evaluated within CONUS.

scholarly journals Burned-Area Detection in Amazonian Environments Using Standardized Time Series Per Pixel in MODIS Data

2018 ◽  
Vol 10 (12) ◽  
pp. 1904 ◽  
Author(s):  
Níckolas Santana ◽  
Osmar de Carvalho Júnior ◽  
Roberto Gomes ◽  
Renato Guimarães
Keyword(s):  
Time Series ◽  
Seasonal Difference ◽  
Thematic Mapper ◽  
Burned Area ◽  
Amazon Forest ◽  
Spectral Indices ◽  
Area Index ◽  
Commission Errors ◽  
Normalized Burn Ratio ◽  
Omission Errors

Fires associated with the expansion of cattle ranching and agriculture have become a problem in the Amazon biome, causing severe environmental damages. Remote sensing techniques have been widely used in fire monitoring on the extensive Amazon forest, but accurate automated fire detection needs improvements. The popular Moderate Resolution Imaging Spectroradiometer (MODIS) MCD64 product still has high omission errors in the region. This research aimed to evaluate MODIS time series spectral indices for mapping burned areas in the municipality of Novo Progresso (State of Pará) and to determine their accuracy in the different types of land use/land cover during the period 2000–2014. The burned area mapping from 8-day composite products, compared the following data: near-infrared (NIR) band; spectral indices (Burnt Area Index (BAIM), Global Environmental Monitoring Index (GEMI), Mid Infrared Burn Index (MIRBI), Normalized Burn Ratio (NBR), variation of Normalized Burn Ratio (NBR2), and Normalized Difference Vegetation Index (NDVI)); and the seasonal difference of spectral indices. Moreover, we compared the time series normalization methods per pixel (zero-mean normalization and Z-score) and the seasonal difference between consecutive years. Threshold-value determination for the fire occurrences was obtained from the comparison of MODIS series with visual image classification of Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) data using the overall accuracy. The best result considered the following factors: NIR band and zero-mean normalization, obtaining the overall accuracy of 98.99%, commission errors of 32.41%, and omission errors of 31.64%. The proposed method presented better results in burned area detection in the natural fields (Campinarana) with an overall accuracy value of 99.25%, commission errors of 9.71%, and omission errors of 27.60%, as well as pasture, with overall accuracy value of 99.19%, commission errors of 27.60%, and omission errors of 34.76%. Forest areas had a lower accuracy, with an overall accuracy of 98.62%, commission errors of 23.40%, and omission errors of 49.62%. The best performance of the burned area detection in the pastures is relevant because the deforested areas are responsible for more than 70% of fire events. The results of the proposed method were better than the burned area products (MCD45, MCD64, and FIRE-CCI), but still presented limitations in the identification of burn events in the savanna formations and secondary vegetation.

scholarly journals MOSEV: a global burn severity database from MODIS (2000–2020)

2021 ◽  
Vol 13 (5) ◽  
pp. 1925-1938
Author(s):  
Esteban Alonso-González ◽  
Víctor Fernández-García
Keyword(s):  
Correlation Coefficients ◽  
Burn Severity ◽  
Burned Area ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Global Database ◽  
Tiling System ◽  
Time To Build ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Difference

Abstract. To make advances in the fire discipline, as well as in the study of CO2 emissions, it is of great interest to develop a global database with estimators of the degree of biomass consumed by fire, which is defined as burn severity. In this work we present the first global burn severity database (MOSEV database), which is based on Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance and burned area (BA) products from November 2000 to near real time. To build the database we combined Terra MOD09A1 and Aqua MYD09A1 surface reflectance products to obtain dense time series of the normalized burn ratio (NBR) spectral index, and we used the MCD64A1 product to identify BA and the date of burning. Then, we calculated for each burned pixel the difference of the NBR (dNBR) and its relativized version (RdNBR), as well as the post-burn NBR, which are the most commonly used burn severity spectral indices. The database also includes the pre-burn NBR used for calculations, the date of the pre- and post-burn NBR, and the date of burning. Moreover, in this work we have compared the burn severity metrics included in MOSEV (dNBR, RdNBR and post-burn NBR) with the same ones obtained from Landsat-8 scenes which have an original resolution of 30 m. We calculated the Pearson's correlation coefficients and the significance of the relationships using 13 pairs of Landsat scenes randomly distributed across the globe, with a total BA of 6904 km2 (n=32 163). Results showed that MOSEV and Landsat-8 burn severity indices are highly correlated, particularly the post-burn NBR (R=0.88; P<0.001), and dNBR (R=0.74; P<0.001) showed stronger relationships than RdNBR (R=0.42; P<0.001). Differences between MOSEV and Landsat-8 indices are attributable to variability in reflectance values and to the different temporal resolution of both satellites (MODIS: 1–2 d; Landsat: 16 d). The database is structured according to the MODIS tiling system and is freely downloadable at https://doi.org/10.5281/zenodo.4265209 (Alonso-González and Fernández-García, 2020).

scholarly journals APPLICATION OF SUPPORT VECTOR MACHINES FOR FODDER CROP ASSESSMENT

2018 ◽  
Vol IV-5 ◽  
pp. 415-420
Author(s):  
S. Kala ◽  
M. Singh ◽  
S. Dutta ◽  
N. Singh ◽  
S. Dwivedi
Keyword(s):  
Crop Production ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Training Data ◽  
Support Vector ◽  
Landsat 8 ◽  
Sensing Technology ◽  
Fodder Crops ◽  
Multi Temporal ◽  
Livestock Productivity

<p><strong>Abstract.</strong> Identification of crop and its accuracy is an important aspect in predicting crop production using Remote Sensing technology. This study investigates the ability of Support Vector Machine (SVM) algorithm in discriminating fodder crops and estimating its area using moderate resolution multi-temporal Landsat-8 OLI data. SVM is a non-parametric statistical learning method and its accuracy is dependent on the parameters and the kernels used. The objective was to evaluate the feasibility of SVM in fodder classification and compare the results with traditional parametric Maximum Likelihood Classification (MLC). Fodder crops are available over small fields in the study area thus having large number of pure fodder pixels over small area is difficult. Hence, SVM has an advantage over MLC as it works well with less training data sets also. Three kernels (linear, polynomial and radial based function) were used with SVM classification. Comparative analysis showed that higher overall accuracy was observed in SVM in comparison to MLC. Temporal change in the spectral properties of the crops derived through Normalized Difference Vegetation Index (NDVI) from multi-temporal Landsat-8 was found to be the most important information that affects accuracy of classification. The classification accuracies for SVM with radial based function, polynomial, linear kernel and MLC were 90.09%, 89.9%, 88.9% and 82.4% respectively. The result suggested that SVM including three kernels performed significantly better than MLC. India has low livestock productivity due to unavailability of fodder hence this study could help in strengthening the fodder productivity.</p>

scholarly journals ANALYSIS OF RESPONSE AND RECOVERY OF VEGETATION TO FOREST FIRE

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1207-1212
Author(s):  
A. Gong ◽  
J. Li ◽  
Y. Yang ◽  
Y. Chen ◽  
T. Zeng ◽  
...  
Keyword(s):  
Forest Fire ◽  
Recovery Time ◽  
The United States ◽  
Forest Vegetation ◽  
Mitigation Strategies ◽  
Burn Severity ◽  
Burned Area ◽  
Area Index ◽  
Burned Areas ◽  
Response And Recovery

Abstract. To analyse the response and recovery characteristics of forest to forest fire, this paper selected the forest fire in the Greater Khingan Mountains (GKM) in China in 1987 and the forest fire in the Yellowstone National Park (YSP) in the United States in 1988. We first used Landsat-5 TM images before and after the fire to extract the burned area and calculate burn severity based on the Differential Normalized Burn Ratio (dNBR). Next, we analysed the response of forest vegetation to forest fire with different burn severity using the anomaly value of Leaf area index (LAI) derived from Global Land Surface Satellite (GLASS) products. And the recovery of forest vegetation after forest fire were revealed using time – series LAI data and MODIS Land cover data. The results showed that the LAI decreased rapidly after the forest fire, and the greater the burn severity, the higher the decreasing amplitude of LAI. The maximum decreasing amplitude of LAI in the burned areas with high burn severity were 1.3–3.8 times higher than that in low burn severity areas. The recovery time of LAI is affected by burn severity and manual interference. The recovery time of LAI in burned areas in the GKM is about 5–10 years, which in the burned areas with high burn severity is 2 times than that with low burn severity. The recovery time of LAI in the burned areas with low burn severity in the YSP is at least 20 years, while that with high burn severity will take longer time to recovery. And the manual interference accelerated the recovery of LAI in the GKM. Our research on the response and recovery of vegetation is helpful for formulating and implementing adaptation and mitigation strategies in response to forest fire.

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