Evaluating the Best Spectral Indices for the Detection of Burn Scars at Several Post-Fire Dates in a Mountainous Region of Northwest Yunnan, China

Remote mountainous regions are among the Earth’s last remaining wild spots, hosting rare ecosystems and rich biodiversity. Because of access difficulties and low population density, baseline information about natural and human-induced disturbances in these regions is often limited or nonexistent. Landsat time series offer invaluable opportunities to reconstruct past land cover changes. However, the applicability of this approach strongly depends on the availability of good quality, cloud-free images, acquired at a regular time interval, which in mountainous regions are often difficult to find. The present study analyzed burn scar detection capabilities of 11 widely used spectral indices (SI) at 1 to 5 years after fire events in four dominant vegetation groups in a mountainous region of northwest Yunnan, China. To evaluate their performances, we used M-statistic as a burned-unburned class separability index, and we adapted an existing metric to quantify the SI residual burn signal at post-fire dates compared to the maximum severity recorded soon after the fire. Our results show that Normalized Burn Ratio (NBR) and Normalized Difference Moisture Index (NDMI) are always among the three best performers for the detection of burn scars starting 1 year after fire but not for the immediate post-fire assessment, where the Mid Infrared Burn Index, Burn Area Index, and Tasseled Cap Greenness were superior. Brightness and Wetness peculiar patterns revealed long-term effects of fire in vegetated land, suggesting their potential integration to assist other SI in burned area detection several years after the fire event. However, in general, class separability of most of the SI was poor after one growing season, due to the seasonal rains and the relatively fast regrowth rate of shrubs and grasses, confirming the difficulty of assessment in mountainous ecosystems. Our findings are meaningful for the selection of a suitable SI to integrate in burned area detection workflows, according to vegetation type and time lag between image acquisitions.

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

MATEC Web of Conferences ◽

10.1051/matecconf/201822904012 ◽

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.

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

Remote Sensing ◽

10.3390/rs10121904 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1904 ◽

Cited By ~ 5

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.

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Sensitivity of Spectral Indices on Burned Area Detection using Landsat Time Series in Savannas of Southern Burkina Faso

Remote Sensing ◽

10.3390/rs13132492 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2492

Author(s):

Jinxiu Liu ◽

Eduardo Eiji Maeda ◽

Du Wang ◽

Janne Heiskanen

Keyword(s):

Time Series ◽

Burkina Faso ◽

Reference Data ◽

Vegetation Indices ◽

Model Fitting ◽

Burned Area ◽

Spectral Indices ◽

Harmonic Model ◽

Area Index ◽

Burned Area Mapping

Accurate and efficient burned area mapping and monitoring are fundamental for environmental applications. Studies using Landsat time series for burned area mapping are increasing and popular. However, the performance of burned area mapping with different spectral indices and Landsat time series has not been evaluated and compared. This study compares eleven spectral indices for burned area detection in the savanna area of southern Burkina Faso using Landsat data ranging from October 2000 to April 2016. The same reference data are adopted to assess the performance of different spectral indices. The results indicate that Burned Area Index (BAI) is the most accurate index in burned area detection using our method based on harmonic model fitting and breakpoint identification. Among those tested, fire-related indices are more accurate than vegetation indices, and Char Soil Index (CSI) performed worst. Furthermore, we evaluate whether combining several different spectral indices can improve the accuracy of burned area detection. According to the results, only minor improvements in accuracy can be attained in the studied environment, and the performance depended on the number of selected spectral indices.

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Burned Area Detection in the Brazilian Amazon using Spectral Indices and GEOBIA

Revista Brasileira de Cartografia ◽

10.14393/rbcv72n2-48726 ◽

2020 ◽

Vol 72 (2) ◽

pp. 253-269 ◽

Cited By ~ 1

Author(s):

Thales Vaz Penha ◽

Thales Sehn Körting ◽

Leila Maria Garcia Fonseca ◽

Celso Henrique Leite Silva Júnior ◽

Mikhaela Aloísia Jessie Santos Pletsch ◽

...

Keyword(s):

Spatial Resolution ◽

Brazilian Amazon ◽

Amazon Region ◽

Burned Area ◽

Landsat 8 ◽

Spectral Indices ◽

Area Index ◽

Burned Areas ◽

Brazilian Amazon Region ◽

Suitable Index

Mapping refined burned areas (BA) in the Brazilian Amazon is still a challenge. The main difficulty of BA detection in large areas is the presence of cloud cover and water bodies. The use of different data sources of medium spatial resolution satellite images can provide a higher availability of cloud-free images. Besides that, it may decrease the uncertainties associated with coarse spatial resolution data (>250m), which can under or overestimate BA and hinder the detection of small BA patches (<0.1km²). In this study, we propose an innovative methodology based on spectral indices and geographic object-based image analysis (GEOBIA), using medium spatial resolution images to improve BA detection in the Brazilian Amazon region. Firstly, we assessed the performance of nine spectral indices in two study areas, derived from Landsat-8 OLI and Sentinel-2A MSI data to identify the most suitable index for BA detection in this region. Then, we refined this data through the GEOBIA-based model. The results showed that the Burned Area Index (BAI) was the most suitable index for BA mapping (M index >1.5) for both sensors. Our model allowed detecting more than 80% of small BA and also presented high Dice coefficient values (~0.70) with low omission and commission errors (0.22 and 0.32, respectively). Such combined approach corresponds to a novel contribution to the BA detection in the Brazilian Amazon region and for enhancing the operational product generation.

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Assessing Burned Areas in Wildfires and Prescribed Fires with Spectral Indices and SAR Images in the Margalla Hills of Pakistan

Forests ◽

10.3390/f12101371 ◽

2021 ◽

Vol 12 (10) ◽

pp. 1371

Author(s):

Aqil Tariq ◽

Hong Shu ◽

Alexandre S. Gagnon ◽

Qingting Li ◽

Faisal Mumtaz ◽

...

Keyword(s):

Vegetation Index ◽

Satellite Image ◽

Google Earth ◽

Burned Area ◽

Prescribed Fires ◽

Spectral Indices ◽

Sar Images ◽

Suitable Alternative ◽

Burn Scar ◽

Regeneration Time

The extent of wildfires cannot be easily mapped using field-based methods in areas with complex topography, and in those areas the use of remote sensing is an alternative. This study first obtained images from the Sentinel-2 satellites for the period 2015–2020 with the objective of applying multi-temporal spectral indices to assess areas burned in wildfires and prescribed fires in the Margalla Hills of Pakistan using the Google Earth Engine (GEE). Using those images, the Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR), which are often used to assess the severity of fires, were calculated for wildfires and prescribed fires. For each satellite image, spectral indices values were extracted for the 5th, 20th, 40th, 60th, 80th and 95th percentiles of pixels of each burned area. Then, boxplots representing the distribution of these values were plotted for each satellite image to identify whether the regeneration time subsequent to a fire, also known as the burn scar, and the severity of the fire differed between the autumn and summer wildfires, and with prescribed fires. A statistical test revealed no differences for the regeneration time amongst the three categories of fires, but that the severity of summer wildfires was significantly different from that of prescribed fire, and this, for both indices. Second, SAR images were obtained from the Sentinel-1 mission for the same period as that of the optical imagery. A comparison of the response of 34 SAR variables with official data on wildfires and prescribed fires from the Capital Development Authority revealed that the 95th percentile of the Normalized Signal Ratio (NSR p_95) was found to be the best variable to detect fire events, although only 50% of the fires were correctly detected. Nonetheless, when the occurrence of fire events according to the SAR variable NSR p_95 was compared to that from the two spectral indices, the SAR variable was found to correctly identify 95% of fire events. The SAR variable NSR p_95 is thus a suitable alternative to spectral indices to monitor the progress of wildfires and assess their severity when there are limitations to the use of optical images due to cloud coverage or smoke, for instance.

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Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest

Remote Sensing ◽

10.3390/rs13101966 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1966

Author(s):

Christopher W Smith ◽

Santosh K Panda ◽

Uma S Bhatt ◽

Franz J Meyer ◽

Anushree Badola ◽

...

Keyword(s):

Boreal Forest ◽

Ground Truth ◽

Burn Severity ◽

Classification Methods ◽

Spectral Indices ◽

Ground Truth Data ◽

Burn Scar ◽

Interior Alaska ◽

Remote Sensing Methods ◽

The Relationship

In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent.

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Short-Term Effects of Fire Severity on Vegetation Based on Sentinel-2 Satellite Data

Sustainability ◽

10.3390/su13010432 ◽

2021 ◽

Vol 13 (1) ◽

pp. 432

Author(s):

Aru Han ◽

Song Qing ◽

Yongbin Bao ◽

Li Na ◽

Yuhai Bao ◽

...

Keyword(s):

Forest Fire ◽

Forest Fires ◽

Vegetation Change ◽

Fire Severity ◽

Dynamic Change ◽

Burned Area ◽

Fire Event ◽

Vegetation Dynamic ◽

Area Index ◽

Sentinel 2

An important component in improving the quality of forests is to study the interference intensity of forest fires, in order to describe the intensity of the forest fire and the vegetation recovery, and to improve the monitoring ability of the dynamic change of the forest. Using a forest fire event in Bilahe, Inner Monglia in 2017 as a case study, this study extracted the burned area based on the BAIS2 index of Sentinel-2 data for 2016–2018. The leaf area index (LAI) and fractional vegetation cover (FVC), which are more suitable for monitoring vegetation dynamic changes of a burned area, were calculated by comparing the biophysical and spectral indices. The results showed that patterns of change of LAI and FVC of various land cover types were similar post-fire. The LAI and FVC of forest and grassland were high during the pre-fire and post-fire years. During the fire year, from the fire month (May) through the next 4 months (September), the order of areas of different fire severity in terms of values of LAI and FVC was: low > moderate > high severity. During the post fire year, LAI and FVC increased rapidly in areas of different fire severity, and the ranking of areas of different fire severity in terms of values LAI and FVC was consistent with the trend observed during the pre-fire year. The results of this study can improve the understanding of the mechanisms involved in post-fire vegetation change. By using quantitative inversion, the health trajectory of the ecosystem can be rapidly determined, and therefore this method can play an irreplaceable role in the realization of sustainable development in the study area. Therefore, it is of great scientific significance to quantitatively retrieve vegetation variables by remote sensing.

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Modeling long-term fire impact on ecosystem characteristics and surface energy using a process-based vegetation–fire model SSiB4/TRIFFID-Fire v1.0

Geoscientific Model Development ◽

10.5194/gmd-13-6029-2020 ◽

2020 ◽

Vol 13 (12) ◽

pp. 6029-6050

Author(s):

Huilin Huang ◽

Yongkang Xue ◽

Fang Li ◽

Ye Liu

Keyword(s):

Surface Energy ◽

Surface Fluxes ◽

Surface Energy Budget ◽

Biophysical Model ◽

Burned Area ◽

Area Index ◽

Fire Model ◽

Vegetation Height ◽

Fire Impact

Abstract. Fire is one of the primary disturbances to the distribution and ecological properties of the world's major biomes and can influence the surface fluxes and climate through vegetation–climate interactions. This study incorporates a fire model of intermediate complexity to a biophysical model with dynamic vegetation, SSiB4/TRIFFID (The Simplified Simple Biosphere Model coupled with the Top-down Representation of Interactive Foliage and Flora Including Dynamics Model). This new model, SSiB4/TRIFFID-Fire, updating fire impact on the terrestrial carbon cycle every 10 d, is then used to simulate the burned area during 1948–2014. The simulated global burned area in 2000–2014 is 471.9 Mha yr−1, close to the estimate of 478.1 Mha yr−1 in Global Fire Emission Database v4s (GFED4s), with a spatial correlation of 0.8. The SSiB4/TRIFFID-Fire reproduces temporal variations of the burned area at monthly to interannual scales. Specifically, it captures the observed decline trend in northern African savanna fire and accurately simulates the fire seasonality in most major fire regions. The simulated fire carbon emission is 2.19 Pg yr−1, slightly higher than the GFED4s (2.07 Pg yr−1). The SSiB4/TRIFFID-Fire is applied to assess the long-term fire impact on ecosystem characteristics and surface energy budget by comparing model runs with and without fire (FIRE-ON minus FIRE-OFF). The FIRE-ON simulation reduces tree cover over 4.5 % of the global land surface, accompanied by a decrease in leaf area index and vegetation height by 0.10 m2 m−2 and 1.24 m, respectively. The surface albedo and sensible heat are reduced throughout the year, while latent heat flux decreases in the fire season but increases in the rainy season. Fire results in an increase in surface temperature over most fire regions.

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Sensitivity and predictive uncertainty of the ACASA model at a spruce forest site

Biogeosciences ◽

10.5194/bg-7-3685-2010 ◽

2010 ◽

Vol 7 (11) ◽

pp. 3685-3705 ◽

Cited By ~ 20

Author(s):

K. Staudt ◽

E. Falge ◽

R. D. Pyles ◽

K. T. Paw U ◽

T. Foken

Keyword(s):

Latent Heat ◽

Goodness Of Fit ◽

Net Ecosystem Exchange ◽

Heat Fluxes ◽

Transfer Model ◽

Forest Site ◽

Area Index ◽

Predictive Uncertainty ◽

Mountainous Regions ◽

Generalized Likelihood Uncertainty Estimation

Abstract. The sensitivity and predictive uncertainty of the Advanced Canopy-Atmosphere-Soil Algorithm (ACASA) was assessed by employing the Generalized Likelihood Uncertainty Estimation (GLUE) method. ACASA is a stand-scale, multi-layer soil-vegetation-atmosphere transfer model that incorporates a third order closure method to simulate the turbulent exchange of energy and matter within and above the canopy. Fluxes simulated by the model were compared to sensible and latent heat fluxes as well as the net ecosystem exchange measured by an eddy-covariance system above the spruce canopy at the FLUXNET-station Waldstein-Weidenbrunnen in the Fichtelgebirge Mountains in Germany. From each of the intensive observation periods carried out within the EGER project (ExchanGE processes in mountainous Regions) in autumn 2007 and summer 2008, five days of flux measurements were selected. A large number (20000) of model runs using randomly generated parameter sets were performed and goodness of fit measures for all fluxes for each of these runs were calculated. The 10% best model runs for each flux were used for further investigation of the sensitivity of the fluxes to parameter values and to calculate uncertainty bounds. A strong sensitivity of the individual fluxes to a few parameters was observed, such as the leaf area index. However, the sensitivity analysis also revealed the equifinality of many parameters in the ACASA model for the investigated periods. The analysis of two time periods, each representing different meteorological conditions, provided an insight into the seasonal variation of parameter sensitivity. The calculated uncertainty bounds demonstrated that all fluxes were well reproduced by the ACASA model. In general, uncertainty bounds encompass measured values better when these are conditioned on the respective individual flux only and not on all three fluxes concurrently. Structural weaknesses of the ACASA model concerning the soil respiration calculations and the simulation of the latent heat flux during dry conditions were detected, with improvements suggested for each.

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Integration of AWiFS and MODIS active fire data for burn mapping at regional level using the Burned Area Synergic Algorithm (BASA)

International Journal of Wildland Fire ◽

10.1071/wf07081 ◽

2009 ◽

Vol 18 (4) ◽

pp. 404 ◽

Cited By ~ 13

Author(s):

Federico González-Alonso ◽

Silvia Merino-de-Miguel

Keyword(s):

Forest Fires ◽

Burned Area ◽

Wide Field ◽

Imaging Spectrometer ◽

Area Index ◽

North West ◽

Active Fire ◽

Moderate Resolution ◽

Field Sensor ◽

Speed Accuracy

The present paper presents an algorithm that synergistically combines data from four different parts of the spectrum (near-, shortwave, middle- and thermal infrared) to produce a reliable burned-area map. It is based on the use of a modified version of the BAIM (MODIS – Moderate Resolution Imaging Spectrometer – Burned Area Index) together with active fire information. The following study focusses in particular on an image from the AWiFS (Advanced Wide Field Sensor) sensor dated 21 August 2006 and MODIS active fires detected during the first 20 days of August as well as ancillary maps and information. The methodology was tested in Galicia (north-west Spain) where hundreds of forest fires occurred during the first 20 days of August 2006. Burned area data collected from the present work was compared with official fire statistics from both the Spanish Ministry of the Environment and the Galician Forestry Service. The speed, accuracy and cost-effectiveness of this method suggest that it would be of great interest for use at both regional and national levels.

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