An improved combined vegetation difference index and burn scar index approach for mapping cropland burned areas using combined data from Landsat 8 multispectral and thermal infrared bands

2020 ◽  
Vol 29 (6) ◽  
pp. 499
Author(s):  
Shufu Liu ◽  
Shudong Wang ◽  
Tianhe Chi ◽  
Congcong Wen ◽  
Taixia Wu ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Land Surface ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Thermal Infrared ◽  
Haihe River Basin ◽  
Burned Area ◽  
Landsat 8 ◽  
Burn Scar ◽  
Burned Areas

The accurate extraction of agricultural burned area is essential for fire-induced air quality models and assessments of agricultural grain loss and wildfire disasters. The present study provides an improved approach for mapping uncontrolled cropland burned areas, which involves pre-classification using a difference vegetation index model for various agricultural land scenarios. Land surface temperature was analysed in burned and unburned areas and integrated into a previous burn scar index (BSI) model, and multispectral and thermal infrared information were combined to create a new temperature BSI (TBSI) to remove background noise. The TBSI model was applied to a winter wheat agricultural region in the Haihe River Basin in northern China. The extracted burned areas were validated using Gaofen-1 satellite data and compared with those produced by the previous BSI model. The producer and user accuracy of the new TBSI model were measured at 92.42 and 95.31% respectively, with an overall kappa value of 0.92, whereas those of the previous BSI model were 83.33, 87.30% and 0.86. The results indicate that the new method is more appropriate for mapping uncontrolled winter wheat burned area. Potential applications of this research include trace gas emission models, agricultural fire management and agricultural wildfire disaster assessment.

scholarly journals Estimating the area burned by agricultural fires from Landsat 8 Data using the Vegetation Difference Index and Burn Scar Index

2018 ◽  
Vol 27 (4) ◽  
pp. 217 ◽  
Author(s):  
Shudong Wang ◽  
Muhammad Hasan Ali Baig ◽  
Suhong Liu ◽  
Huawei Wan ◽  
Taixia Wu ◽  
...  
Keyword(s):  
Accurate Estimate ◽  
Central China ◽  
Background Information ◽  
Haihe River Basin ◽  
Burned Area ◽  
Model Parameters ◽  
Landsat 8 ◽  
Haihe River ◽  
Burn Scar ◽  
The Haihe River Basin

Obtaining an accurate estimate of the area of burned crops through remote sensing provides extremely useful data for the assessment of fire-induced trace gas emissions and grain loss in agricultural areas. A new method, incorporating the Vegetation Difference Index (VDI) and Burn Scar Index (BSI) models, is proposed for the extraction of burned crops area. The VDI model can greatly reduce the confounding effect of background information pertaining to green vegetation (forests and grasslands), water bodies and buildings; subsequent use of the BSI model could improve the accuracy of burned area estimations because of the reduction in the influence of background information. The combination of VDI and BSI enables the VDI to reduce the effect of non-farmland information, which in turn improves the accuracy and speed of the BSI model. The model parameters were established, and an effects analysis was performed, using a normalized dispersion value simulation based on a comparison of different types of background information. The efficacy of the VDI and BSI models was tested for a winter wheat planting area in the Haihe River Basin in central China. In comparison with other models, it was found that this method could effectively extract burned area information.

scholarly journals Application of Geo-informatics Technology to Access the Surface Temperature Using LANDSAT 8 OLI/TIRS Satellite Data: A Case Study in Ampara District in Sri Lanka

2020 ◽  
Author(s):  
Ibra Lebbe Mohamed Zahir
Keyword(s):  
Sri Lanka ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Thermal Infrared ◽  
Landsat 8 ◽  
Surface Emissivity ◽  
Land Surface Emissivity

Land Surface Temperature is a one of the key variable of Global climate changes and model which estimate radiating budget in heat balance as control of climate model. It is a major influenced factor by the ability of the surface emissivity. In this study, were used Landsat 8 satellite image that have Operational Land Imager and Thermal Infrared Sensor to calculate Land Surface Temperature through geospatial technology over Ampara district, Sri Lanka. The Land Surface Temperature was estimated with respect to Land Surface Emissivity and Normalized Difference Vegetation Index values determined from the Red and Near Infrared channels. Land Surface Emissivity was processed directly by the thermal Infrared bands. Pixels based calculation were used to effort at LANDSAT 8 images that thermal Band 10 various dates in this study. The results were achievable to compute Normalized Difference Vegetation Index, Land Surface Emissivity, and Land Surface Temperature with applicable manner to compare with land use/ land cover data. It determines and predicts the changes of surface temperature to favorable to decision making process for the society. Study area faces seasonal drought in Sri Lanka, the prediction method that how land can be efficiently used with the present condition. Therefore, the Land Surface Temperature estimation can prove whether new irrigation systems for agricultural activities or can transformed source of energy into useful form that introducing solar hubs for energy production in future.

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.

scholarly journals Multi-criteria modelling of drought: a study of Brandenburg Federal State, Germany

2021 ◽  
Author(s):  
Christopher Ihinegbu ◽  
Taiwo Ogunwumi
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Federal State ◽  
Landsat 8 ◽  
Gis And Remote Sensing ◽  
Drought Assessment

AbstractDrought is the absence or below-required supply of precipitation, runoff and or moisture for an extended time period. Modelling drought is relevant in assessing drought incidence and pattern. This study aimed to model the spatial variation and incidence of the 2018 drought in Brandenburg using GIS and remote sensing. To achieve this, we employed a Multi-Criteria Approach (MCA) by using three parameters including Precipitation, Land Surface Temperature and Normalized Difference Vegetation Index (NDVI). We acquired the precipitation data from Deutsche Wetterdienst, Land Surface Temperature and NDVI from Landsat 8 imageries on the USGS Earth Explorer. The datasets were analyzed using ArcGIS 10.7. The information from these three datasets was used as parameters in assessing drought prevalence using the MCA. The MCA was used in developing the drought model, ‘PLAN’, which was used to classify the study area into three levels/zones of drought prevalence: moderate, high and extreme drought. We went further to quantify the agricultural areas affected by drought in the study area by integrating the land use map. Results revealed that 92% of the study area was severely and highly affected by drought especially in districts of Oberhavel, Uckermark, Potsdam-Staedte, and Teltow-Flaeming. Finding also revealed that 77.54% of the total agricultural land falls within the high drought zones. We advocated for the application of drought models (such as ‘PLAN’), that incorporates flexibility (tailoring to study needs) and multi-criteria (robustness) in drought assessment. We also suggested that adaptive drought management should be championed using drought prevalence mapping.

Impact of LULC Changes on LST in Rajshahi District of Bangladesh: A Remote Sensing Approach

2020 ◽  
Vol 3 (1) ◽  
pp. 11-23 ◽  
Author(s):  
Abdulla Al Kafy ◽  
Abdullah Al-Faisal ◽  
Mohammad Mahmudul Hasan ◽  
Md. Soumik Sikdar ◽  
Mohammad Hasib Hasan Khan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface ◽  
Agricultural Land ◽  
Global Climate ◽  
Urban Expansion ◽  
Water Bodies ◽  
Landsat 8 ◽  
Lulc Changes ◽  
The Impact

Urbanization has been contributing more in global climate warming, with more than 50% of the population living in cities. Rapid population growth and change in land use / land cover (LULC) are closely linked. The transformation of LULC due to rapid urban expansion significantly affects the functions of biodiversity and ecosystems, as well as local and regional climates. Improper planning and uncontrolled management of LULC changes profoundly contribute to the rise of urban land surface temperature (LST). This study evaluates the impact of LULC changes on LST for 1997, 2007 and 2017 in the Rajshahi district (Bangladesh) using multi-temporal and multi-spectral Landsat 8 OLI and Landsat 5 TM satellite data sets. The analysis of LULC changes exposed a remarkable increase in the built-up areas and a significant decrease in the vegetation and agricultural land. The built-up area was increased almost double in last 20 years in the study area. The distribution of changes in LST shows that built-up areas recorded the highest temperature followed by bare land, vegetation and agricultural land and water bodies. The LULC-LST profiles also revealed the highest temperature in built-up areas and the lowest temperature in water bodies. In the last 20 years, LST was increased about 13ºC. The study demonstrates decrease in vegetation cover and increase in non-evaporating surfaces with significantly increases the surface temperature in the study area. Remote-sensing techniques were found one of the suitable techniques for rapid analysis of urban expansions and to identify the impact of urbanization on LST.

scholarly journals Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Scales ◽  
Detection Methods ◽  
Burned Area ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Burned Areas ◽  
Local Climate Zones ◽  
Sentinel 2

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

scholarly journals Comparison of LAI Estimates from High Resolution Satellite Observations Using Different Biophysical Processors

2021 ◽  
Vol 3 (1) ◽  
pp. 5
Author(s):  
Federico Filipponi
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Agricultural Land ◽  
Spectral Characteristics ◽  
Earth Observation ◽  
Estimation Accuracy ◽  
Landsat 8 ◽  
Biophysical Parameters ◽  
Area Index ◽  
Satellite Sensors

Earth observation provides timely and spatially explicit information about crop phenology and vegetation dynamics that can support decision making and sustainable agricultural land management. Vegetation spectral indices calculated from optical multispectral satellite sensors have been largely used to monitor vegetation status. In addition, techniques to retrieve biophysical parameters from satellite acquisitions, such as the Leaf Area Index (LAI), have allowed to assimilate Earth observation time series in numerical modeling for the analysis of several land surface processes related to agroecosystem dynamics. More recently, biophysical processors used to estimate biophysical parameters from satellite acquisitions have been calibrated for retrieval from sensors with different high spatial resolution and spectral characteristics. Virtual constellations of satellite sensors allow the generation of denser LAI time series, contributing to improve vegetation phenology estimation accuracy and, consequently, enhancing agroecosystems monitoring capacity. This research study compares LAI estimates over croplands using different biophysical processors from Sentinel-2 MSI and Landsat-8 OLI satellite sensors. The results are used to demonstrate the capacity of virtual satellite constellation to strengthen LAI time series to derive important cropland use information over large areas.

scholarly journals A new method for estimating of evapotranspiration and surface soil moisture from optical and thermal infrared measurements: the simplified triangle

2020 ◽  
Author(s):  
Toby N. Carlson ◽  
George Petropoulos
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Heat Fluxes ◽  
Surface Model ◽  
Landsat 8 ◽  
Simple Method ◽  
Practical Applications ◽  
Infrared Measurements

Earth Observation (EO) provides a promising approach towards deriving accurate spatiotemporal estimates of key parameters characterizing land surface interactions, such as latent (LE) and sensible (H) heat fluxes as well as soil moisture content. This paper proposes a very simple method to implement, yet reliable to calculate evapotranspiration fraction (EF) and surface moisture availability (Mo) from remotely sensed imagery of Normalized Difference Vegetation Index (NDVI) and surface radiometric temperature (Tir). The method is unique in that it derives all of its information solely from these two images. As such, it does not depend on knowing ancillary surface or atmospheric parameters, nor does it require the use of a land surface model. The procedure for computing spatiotemporal estimates of these important land surface parameters is outlined herein stepwise for practical application by the user. Moreover, as the newly developedscheme is not tied to any particular sensor, it can also beimplemented with technologically advanced EO sensors launched recently or planned to be launched such as Landsat 8 and Sentinel 3. The latter offers a number of key advantages in terms of future implementation of the method and wider use for research and practical applications alike.

scholarly journals The Effects of Land Use Zoning and Densification on Changes in Land Surface Temperature in Seoul

2019 ◽  
Vol 11 (24) ◽  
pp. 7056 ◽  
Author(s):  
Jae-Ik Kim ◽  
Myung-Jin Jun ◽  
Chang-Hwan Yeo ◽  
Ki-Hyun Kwon ◽  
Jun Yong Hyun
Keyword(s):  
Land Use ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Descriptive Analysis ◽  
Landsat 8 ◽  
Land Use Type ◽  
Coverage Ratio

This study investigated how changes in land surface temperature (LST) during 2004 and 2014 were attributable to zoning-based land use type in Seoul in association with the building coverage ratio (BCR), floor area ratio (FAR), and a normalized difference vegetation index (NDVI). We retrieved LSTs and NDVI data from satellite images, Landsat TM 5 for 2004 and Landsat 8 TIRS for 2014 and combined them with parcel-based land use information, which contained data on BCR, FAR, and zoning-based land use type. The descriptive analysis results showed a rise in LST for the low- and medium-density residential land, whereas significant LST decreases were found in high-density residential, semi-residential, and commercial areas over the time period. Statistical results further supported these findings, yielding statistically significant negative coefficient values for all interaction variables between higher-density land use types and a year-based dummy variable. The findings appear to be related to residential densification involving the provision of more high-rise apartment complexes and government efforts to secure more parks and green spaces through urban redevelopment and renewal projects.

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