scholarly journals Mapping Cropland Burned Area in Northeastern China by Integrating Landsat Time Series and Multi-Harmonic Model

2021 ◽  
Vol 13 (24) ◽  
pp. 5131
Author(s):  
Jinxiu Liu ◽  
Du Wang ◽  
Eduardo Eiji Maeda ◽  
Petri K. E. Pellikka ◽  
Janne Heiskanen
Keyword(s):  
Time Series ◽  
Spatial Resolution ◽  
Spatial Information ◽  
Majority Vote ◽  
Burned Area ◽  
Harmonic Model ◽  
Fire Dynamics ◽  
Area Index ◽  
Burned Areas ◽  
Straw Burning

Accurate cropland burned area estimation is crucial for air quality modeling and cropland management. However, current global burned area products have been primarily derived from coarse spatial resolution images which cannot fulfill the spatial requirement for fire monitoring at local levels. In addition, there is an overall lack of accurate cropland straw burning identification approaches at high temporal and spatial resolution. In this study, we propose a novel algorithm to capture burned area in croplands using dense Landsat time series image stacks. Cropland burning shows a short-term seasonal variation and a long-term dynamic trend, so a multi-harmonic model is applied to characterize fire dynamics in cropland areas. By assessing a time series of the Burned Area Index (BAI), our algorithm detects all potential burned areas in croplands. A land cover mask is used on the primary burned area map to remove false detections, and the spatial information with a moving window based on a majority vote is employed to further reduce salt-and-pepper noise and improve the mapping accuracy. Compared with the accuracy of 67.3% of MODIS products and that of 68.5% of Global Annual Burned Area Map (GABAM) products, a superior overall accuracy of 92.9% was obtained by our algorithm using Landsat time series and multi-harmonic model. Our approach represents a flexible and robust way of detecting straw burning in complex agriculture landscapes. In future studies, the effectiveness of combining different spectral indices and satellite images can be further investigated.

scholarly journals Exploitation of Sentinel-2 Time Series to Map Burned Areas at the National Level: A Case Study on the 2017 Italy Wildfires

2019 ◽  
Vol 11 (6) ◽  
pp. 622 ◽  
Author(s):  
Federico Filipponi
Keyword(s):  
Time Series ◽  
Spatial Resolution ◽  
Satellite Data ◽  
High Spatial Resolution ◽  
National Level ◽  
Burned Area ◽  
Commission Error ◽  
Burned Areas ◽  
Sentinel 2

Satellite data play a major role in supporting knowledge about fire severity by delivering rapid information to map fire-damaged areas in a precise and prompt way. The high availability of free medium-high spatial resolution optical satellite data, offered by the Copernicus Programme, has enabled the development of more detailed post-fire mapping. This research study deals with the exploitation of Sentinel-2 time series to map burned areas, taking advantages from the high revisit frequency and improved spatial and spectral resolution of the MSI optical sensor. A novel procedure is here presented to produce medium-high spatial resolution burned area mapping using dense Sentinel-2 time series with no a priori knowledge about wildfire occurrence or burned areas spatial distribution. The proposed methodology is founded on a threshold-based classification based on empirical observations that discovers wildfire fingerprints on vegetation cover by means of an abrupt change detection procedure. Effectiveness of the procedure in mapping medium-high spatial resolution burned areas at the national level was demonstrated for a case study on the 2017 Italy wildfires. Thematic maps generated under the Copernicus Emergency Management Service were used as reference products to assess the accuracy of the results. Multitemporal series of three different spectral indices, describing wildfire disturbance, were used to identify burned areas and compared to identify their performances in terms of spectral separability. Result showed a total burned area for the Italian country in the year 2017 of around 1400 km2, with the proposed methodology generating a commission error of around 25% and an omission error of around 40%. Results demonstrate how the proposed procedure allows for the medium-high resolution mapping of burned areas, offering a benchmark for the development of new operational downstreaming services at the national level based on Copernicus data for the systematic monitoring of wildfires.

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

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.

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

2020 ◽  
Vol 72 (2) ◽  
pp. 253-269 ◽  
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.

Assessment of different indices from Landsat time series in burned area mapping

2020 ◽  
Author(s):  
Jinxiu Liu
Keyword(s):  
Time Series ◽  
Burkina Faso ◽  
Land Surface ◽  
Vegetation Index ◽  
Landsat Imagery ◽  
Burned Area ◽  
Free Access ◽  
Landsat Images ◽  
Area Index ◽  
Burned Area Mapping

&lt;p&gt;Fire is recognized as an important land surface disturbance, as it influences terrestrial carbon cycle, climate and biodiversity. Accurate and efficient mapping of burned area is beneficial for social and environmental applications. Remote sensing plays a key role in detecting burned areas and active fires from reginal to global scales. Due to the free access to the Landsat archive, studies using dense time series of Landsat imagery for burned area mapping are appearing and increasing. However, the performance of Landsat time series when using different indices for burned area mapping has not been assessed. In this study, the objective was to identify which indices can detect burned area better when using Landsat time series in savanna area of southern Burkina Faso. We selected Burned Area Index (BAI), Normalized Burned Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Global Environmental Monitoring Index (GEMI) for comparison as they are commonly used indices for burned area detection. The algorithm was based on breakpoint identification and burned pixel detection using harmonic model fitting with different indices Landsat time series. It was tested in savanna area in southern Burkina Faso over 16 years with 281 Landsat images ranging from October 2000 to April 2016.The same reference data was used to evaluate the performance of burned area detection with different indices Landsat time series. The result demonstrated that BAI was the most accurate in burned area detection from Landsat time series, followed by NBR, GEMI and NDVI.&lt;/p&gt;

scholarly journals Estimation of grassland use intensities based on high spatial resolution LAI time series

2015 ◽  
Vol XL-7/W3 ◽  
pp. 285-291 ◽  
Author(s):  
S. Asam ◽  
D. Klein ◽  
S. Dech
Keyword(s):  
Time Series ◽  
Spatial Resolution ◽  
Management Practices ◽  
High Spatial Resolution ◽  
Vegetation Period ◽  
Grassland Management ◽  
Transfer Model ◽  
Land Use Patterns ◽  
Area Index ◽  
Intensively Managed

The identification and surveillance of agricultural management and the measurement of biophysical canopy parameters in grasslands is relevant for environmental protection as well as for political and economic reasons, as proper grassland management is partly subsidized. An ideal monitoring tool is remote sensing due to its area wide continuous observations. However, due to small-scaled land use patterns in many parts of central Europe, a high spatial resolution is needed. In this study, the feasibility of RapidEye data to derive leaf area index (LAI) time series and to relate them to grassland management practices is assessed. The study area is the catchment of river Ammer in southern Bavaria, where agricultural areas are mainly grasslands. While extensively managed grasslands are maintained with one to two harvests per year and no or little fertilization, intensive cultivation practices compass three to five harvests per year and turnover pasturing. <br><br> Based on a RapidEye time series from 2011 with spatial resolution of 6.5 meters, LAI is derived using the inverted radiation transfer model PROSAIL. The LAI in this area ranges from 1.5 to 7.5 over the vegetation period and is estimated with an RMSE between 0.7 and 1.1. The derived LAI maps cover 85 % of the study area’s grasslands at least seven times. Using statistical metrics of the LAI time series, different grassland management types can be identified: very intensively managed meadows, intensively managed meadows, intensively managed pastures, and extensively managed meadows and moor. However, a precise identification of the mowing dates highly depends on the coincidence with satellite data acquisitions. Further analysis should focus therefor on the selection of the temporal resolution of the time series as well as on the performance of further vegetation parameters and indices compared to LAI.

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 AN UNSUPERVISED METHOD BASED ON FIRE INDEX ENHANCEMENT AND GRNN FOR AUTOMATED BURNED AREA MAPPING FROM SINGLE-PERIOD REMOTE SENSING IMAGERY

2021 ◽  
Vol V-3-2021 ◽  
pp. 37-42
Author(s):  
Q. Zhang ◽  
Y. Xiao
Keyword(s):  
Remote Sensing ◽  
Forest Fires ◽  
Current Situation ◽  
General Regression Neural Network ◽  
Burned Area ◽  
Area Index ◽  
Remote Sensing Imagery ◽  
Burned Areas ◽  
Unsupervised Method ◽  
Burned Area Mapping

Abstract. In the current situation of frequent forest fires, the study of forest burned area mapping is important. However, there is still room for improvement in the accuracy of existing forest burning area mapping methods. Therefore, in this paper, an unsupervised method based on fire index enhancement and GRNN (General Regression Neural Network) is proposed for automated forest burned area mapping from single-date post-fire remote sensing imagery. The proposed method first uses adaptive spatial context information to enhance the generated fire index to improve its ability to indicate the burned areas. Then the uncertainty analysis is performed on the enhanced fire index to extract reliable burned samples and non-burned samples for subsequent classifier training. Finally, the improved GRNN model considering the spatial correlation of pixels is used as a classifier to binarize the enhanced fire index to generate the final burned area map. Based on two commonly used fire indexes, NBR (Normalized Burn Ratio) and BAI (Burned Area Index), this paper conducts burned area mapping experiments on a post-fire image of a forest area in Inner Mongolia, China to test the effectiveness of the proposed method, and two commonly used threshold methods (Otsu and Kmeans clustering) are also used to conduct burned area mapping based on threshold segmentation of fire index for comparison experiments. The experimental results prove the effectiveness and superiority of the proposed method. The proposed method is unsupervised and automated, so it has high application value and potential under the current situation of frequent forest fires.

scholarly journals A Comparison of Burned Area Time Series in the Alaskan Boreal Forests from Different Remote Sensing Products

Forests ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 363 ◽  
Author(s):  
Moreno-Ruiz ◽  
García-Lázaro ◽  
Arbelo
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Time Series ◽  
Spatial Resolution ◽  
Reference Data ◽  
Global Climate ◽  
Fire Service ◽  
Burned Area ◽  
Spatial And Temporal Patterns ◽  
Modis Data

Alaska’s boreal region stores large amounts of carbon both in its woodlands and in the grounds that sustain them. Any alteration to the fire system that has naturally regulated the region’s ecology for centuries poses a concern regarding global climate change. Satellite-based remote sensors are key to analyzing those spatial and temporal patterns of fire occurrence. This paper compiles four burned area (BA) time series based on remote sensing imagery for the Alaska region between 1982–2015: Burned Areas Boundaries Dataset-Monitoring Trends in Burn Severity (BABD-MTBS) derived from Landsat sensors, Fire Climate Change Initiative (Fire_CCI) (2001–2015) and Moderate-Resolution Imaging Spectroradiometer (MODIS) Direct Broadcast Monthly Burned Area Product (MCD64A1) (2000–2015) with MODIS data, and Burned Area-Long-Term Data Record (BA-LTDR) using Advanced Very High Resolution Radiometer LTDR (AVHRR-LTDR) dataset. All products were analyzed and compared against one another, and their accuracy was assessed through reference data obtained by the Alaskan Fire Service (AFS). The BABD-MTBS product, with the highest spatial resolution (30 m), shows the best overall estimation of BA (81%), however, for the years before 2000 (pre-MODIS era), the BA sensed by this product was only 44.3%, against the 55.5% obtained by the BA-LTDR product with a lower spatial resolution (5 km). In contrast, for the MODIS era (after 2000), BABD-MTBS virtually matches the reference data (98.5%), while the other three time series showed similar results of around 60%. Based on the theoretical limits of their corresponding Pareto boundaries, the lower resolution BA products could be improved, although those based on MODIS data are currently limited by the algorithm’s reliance on the active fire MODIS product, with a 1 km nominal spatial resolution. The large inter-annual variation found in the commission and omission errors in this study suggests that for a fair assessment of the accuracy of any BA product, all available reference data for space and time should be considered and should not be carried out by selective sampling.

scholarly journals 30 m Resolution Global Annual Burned Area Mapping Based on Landsat Images and Google Earth Engine

2019 ◽  
Vol 11 (5) ◽  
pp. 489 ◽  
Author(s):  
Tengfei Long ◽  
Zhaoming Zhang ◽  
Guojin He ◽  
Weili Jiao ◽  
Chao Tang ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Google Earth ◽  
Burned Area ◽  
Landsat 8 ◽  
Landsat Images ◽  
Burned Areas ◽  
Random Decision Forests ◽  
Burned Area Mapping ◽  
Google Earth Engine

Heretofore, global Burned Area (BA) products have only been available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or dense time-series change analysis, which requires very high temporal resolution. In this study, however, we focus on an automated global burned area mapping approach based on Landsat images. By utilizing the huge catalog of satellite imagery, as well as the high-performance computing capacity of Google Earth Engine, we propose an automated pipeline for generating 30-m resolution global-scale annual burned area maps from time-series of Landsat images, and a novel 30-m resolution Global annual Burned Area Map of 2015 (GABAM 2015) was released. All the available Landsat-8 images during 2014–2015 and various spectral indices were utilized to calculate the burned probability of each pixel using random decision forests, which were globally trained with stratified (considering both fire frequency and type of land cover) samples, and a seed-growing approach was conducted to shape the final burned areas after several carefully-designed logical filters (NDVI filter, Normalized Burned Ratio (NBR) filter, and temporal filter). GABAM 2015 consists of spatial extent of fires that occurred during 2015 and not of fires that occurred in previous years. Cross-comparison with the recent Fire_cci Version 5.0 BA product found a similar spatial distribution and a strong correlation ( R 2 = 0.74) between the burned areas from the two products, although differences were found in specific land cover categories (particularly in agriculture land). Preliminary global validation showed the commission and omission errors of GABAM 2015 to be 13.17% and 30.13%, respectively.

Remote sensing of burned areas in tropical savannas

2003 ◽  
Vol 12 (4) ◽  
pp. 259 ◽  
Author(s):  
José M. C. Pereira
Keyword(s):  
Remote Sensing ◽  
Canopy Cover ◽  
Stand Density ◽  
Fire Season ◽  
Burned Area ◽  
Tropical Savannas ◽  
Remotely Sensed Data ◽  
Area Index ◽  
Major Satellite ◽  
Burned Areas

Problematic aspects of fire in tropical savannas are reviewed, from the standpoint of their impact on the detection and mapping of burned areas using remotely sensed data. Those aspects include: the heterogeneity of savanna—resulting in heterogeneity of fire-induced spectral changes; fine fuels and low fuel loadings—resulting in short persistence of the char residue signal; tropical cloudiness—which makes multitemporal image compositing important; the frequent presence of extensive smoke aerosol layers during the fire season—which may obscure fire signals; and the potential problem of detecting burns in the understory of woody savannas with widely variable tree stand density, canopy cover and leaf area index. Finally, the capabilities and limitations of major satellite remote sensing systems for pan-tropical burned area mapping are addressed, considering the spatial, spectral, temporal and radiometric characteristics of the instruments.

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