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

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.

Download Full-text

Combination of Landsat 8 OLI and Sentinel-1 SAR Time-Series Data for Mapping Paddy Fields in Parts of West and Central Java Provinces, Indonesia

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9110663 ◽

2020 ◽

Vol 9 (11) ◽

pp. 663

Author(s):

Sanjiwana Arjasakusuma ◽

Sandiaga Swahyu Kusuma ◽

Raihan Rafif ◽

Siti Saringatin ◽

Pramaditya Wicaksono

Keyword(s):

Spatial Data ◽

Vegetation Index ◽

Google Earth ◽

Paddy Fields ◽

Series Data ◽

Landsat 8 ◽

Spectral Indices ◽

Seamless Integration ◽

Synthetic Aperture Radar Data ◽

Central Java

The rise of Google Earth Engine, a cloud computing platform for spatial data, has unlocked seamless integration for multi-sensor and multi-temporal analysis, which is useful for the identification of land-cover classes based on their temporal characteristics. Our study aims to employ temporal patterns from monthly-median Sentinel-1 (S1) C-band synthetic aperture radar data and cloud-filled monthly spectral indices, i.e., Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Built-up Index (NDBI), from Landsat 8 (L8) OLI for mapping rice cropland areas in the northern part of Central Java Province, Indonesia. The harmonic function was used to fill the cloud and cloud-masked values in the spectral indices from Landsat 8 data, and smile Random Forests (RF) and Classification And Regression Trees (CART) algorithms were used to map rice cropland areas using a combination of monthly S1 and monthly harmonic L8 spectral indices. An additional terrain variable, Terrain Roughness Index (TRI) from the SRTM dataset, was also included in the analysis. Our results demonstrated that RF models with 50 (RF50) and 80 (RF80) trees yielded better accuracy for mapping the extent of paddy fields, with user accuracies of 85.65% (RF50) and 85.75% (RF80), and producer accuracies of 91.63% (RF80) and 93.48% (RF50) (overall accuracies of 92.10% (RF80) and 92.47% (RF50)), respectively, while CART yielded a user accuracy of only 84.83% and a producer accuracy of 80.86%. The model variable importance in both RF50 and RF80 models showed that vertical transmit and horizontal receive (VH) polarization and harmonic-fitted NDVI were identified as the top five important variables, and the variables representing February, April, June, and December contributed more to the RF model. The detection of VH and NDVI as the top variables which contributed up to 51% of the Random Forest model indicated the importance of the multi-sensor combination for the identification of paddy fields.

Download Full-text

Mapping Winter Wheat with Optical and SAR Images Based on Google Earth Engine in Henan Province, China

Remote Sensing ◽

10.3390/rs14020284 ◽

2022 ◽

Vol 14 (2) ◽

pp. 284

Author(s):

Changchun Li ◽

Weinan Chen ◽

Yilin Wang ◽

Yu Wang ◽

Chunyan Ma ◽

...

Keyword(s):

Winter Wheat ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Growth Period ◽

Google Earth ◽

Henan Province ◽

Sar Images ◽

Modis Ndvi ◽

Optical Images ◽

Google Earth Engine

The timely and accurate acquisition of winter wheat acreage is crucial for food security. This study investigated the feasibility of extracting the spatial distribution map of winter wheat in Henan Province by using synthetic aperture radar (SAR, Sentinel-1A) and optical (Sentinel-2) images. Firstly, the SAR images were aggregated based on the growth period of winter wheat, and the optical images were aggregated based on the moderate resolution imaging spectroradiometer normalized difference vegetation index (MODIS-NDVI) curve. Then, five spectral features, two polarization features, and four texture features were selected as feature variables. Finally, the Google Earth Engine (GEE) cloud platform was employed to extract winter wheat acreage through the random forest (RF) algorithm. The results show that: (1) aggregated images based on the growth period of winter wheat and sensor characteristics can improve the mapping accuracy and efficiency; (2) the extraction accuracy of using only SAR images was improved with the accumulation of growth period. The extraction accuracy of using the SAR images in the full growth period reached 80.1%; and (3) the identification effect of integrated images was relatively good, which makes up for the shortcomings of SAR and optical images and improves the extraction accuracy of winter wheat.

Download Full-text

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

International Journal of Wildland Fire ◽

10.1071/wf18146 ◽

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.

Download Full-text

Recovery Rates of Wetland Vegetation Greenness in Severely Burned Ecosystems of Alaska Derived from Satellite Image Analysis

Remote Sensing ◽

10.3390/rs10091456 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1456 ◽

Cited By ~ 9

Author(s):

Christopher Potter

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Satellite Image ◽

Burned Area ◽

Recovery Rates ◽

Wetland Ecosystems ◽

Modis Ndvi ◽

High Severity ◽

Green Cover ◽

Moderate Resolution Imaging Spectroradiometer

The analysis of wildfire impacts at the scale of less than a square kilometer can reveal important patterns of vegetation recovery and regrowth in freshwater Arctic and boreal regions. For this study, NASA Landsat burned area products since the year 2000, and a near 20-year record of vegetation green cover from the MODIS (Moderate Resolution Imaging Spectroradiometer) satellite sensor were combined to reconstruct the recovery rates and seasonal profiles of burned wetland ecosystems in Alaska. Region-wide breakpoint analysis results showed that significant structural change could be detected in the 250-m normalized difference vegetation index (NDVI) time series for the vast majority of wetland locations in the major Yukon river drainages of interior Alaska that had burned at high severity since the year 2001. Additional comparisons showed that wetland cover locations across Alaska that have burned at high severity subsequently recovered their green cover seasonal profiles to relatively stable pre-fire levels in less than 10 years. Negative changes in the MODIS NDVI, namely lower greenness in 2017 than pre-fire and incomplete greenness recovery, were more commonly detected in burned wetland areas after 2013. In the years prior to 2013, the NDVI change tended to be positive (higher greenness in 2017 than pre-fire) at burned wetland elevations lower than 400 m, whereas burned wetland locations at higher elevation showed relatively few positive greenness recovery changes by 2017.

Download Full-text

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 Sensing ◽

10.3390/rs10081196 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1196 ◽

Cited By ~ 11

Author(s):

Davide Fornacca ◽

Guopeng Ren ◽

Wen Xiao

Keyword(s):

Time Lag ◽

Burned Area ◽

Time Interval ◽

Mountainous Region ◽

Spectral Indices ◽

Area Index ◽

Burn Scar ◽

Mountainous Regions ◽

Class Separability ◽

Northwest Yunnan

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.

Download Full-text

A Validation Experiment of the Reflectance Products of KOMPSAT-3A Based on RadCalNet Data and Its Applicability to Vegetation Indexing

Remote Sensing ◽

10.3390/rs12233971 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3971 ◽

Cited By ~ 1

Author(s):

Kwangseob Kim ◽

Kiwon Lee

Keyword(s):

High Resolution ◽

Field Measurement ◽

Vegetation Index ◽

Satellite Image ◽

Vegetation Indices ◽

Atmospheric Correction ◽

Measurement Data ◽

Google Earth ◽

Landsat 8 ◽

Validation Experiment

Surface reflectance products obtained through the absolute atmospheric correction of multispectral satellite images are useful for precise scientific applications. For broader applications, the reflectance products computed using high-resolution images need to be validated with field measurement data. This study dealt with 2.2-m resolution Korea Multi-Purpose Satellite (KOMPSAT)-3A images with four multispectral bands, which were used to obtain top-of-atmosphere (TOA) and top-of-canopy (TOC) reflectance products. The open-source Orfeo Toolbox (OTB) extension was used to generate these products. Next, these were subsequently validated by considering three sites (i.e., Railroad Valley Playa, NV, USA (RVUS), Baotou, China (BTCN), and La Crau, France (LCFR)) in RadCalNet, as well as a calibration and validation portal for remote sensing. We conducted the validations comparing satellite image-based reflectance products and field measurement reflectance based on data sets acquired at different times. The experimental results showed that the overall trend of validation accuracy of KOPSAT-3A was well fitted in all the RadCalNet sites and that the accuracy remained quite constant. Reflectance bands showing the minimum and maximum differences between the sets of experimental data are presented in this paper. The vegetation indices (i.e., the atmospherically resistant vegetation index (ARVI) and the structure insensitive pigment index (SIPI)) and three TOC reflectance bands obtained from KOMPSAT-3A were computed as a case study and used to achieve a detailed vegetation interpretation; finally, the correspondent results were compared with those obtained from Landsat-8 images (downloaded from the Google Earth Engine (GEE)). The validation and the application scheme presented in this study can be potentially applied to the generation of analysis ready data from high-resolution satellite sensor images.

Download Full-text

Ecological Monitoring with Spy Satellite Images—The Case of Red Wood Ants in Romania

Remote Sensing ◽

10.3390/rs13030520 ◽

2021 ◽

Vol 13 (3) ◽

pp. 520

Author(s):

Dietrich Klimetzek ◽

Petru Tudor Stăncioiu ◽

Marius Paraschiv ◽

Mihai Daniel Niță

Keyword(s):

Satellite Images ◽

Vegetation Index ◽

Satellite Image ◽

Canopy Cover ◽

Ecological Monitoring ◽

Google Earth ◽

Ecosystem Dynamics ◽

Small Scale ◽

Habitat Conditions ◽

Sources Of Information

Dynamics of habitat conditions drive important changes in distribution and abundance of animal species making monitoring an important but also a challenging task when data from the past are scarce. We compared the distribution of ant mounds in the 1960s with recent inventories (2018), looking at changes in canopy cover over time, in a managed forest. Both historical and recent sources of information were used. Habitat suitability at present was determined using a Normalized Difference Vegetation Index (NDVI) image as a proxy for stand canopy cover. The NDVI product was obtained using Google Earth Engine and Sentinel 2 repository. For past conditions (no spectral information available), presence of edges and more open canopies was assessed on a Corona spy-satellite image and based on information from old forest management plans. A threshold distance of 30 m was used to assess location of ant nests compared to favorable habitats. Both old and new information sources showed that ants prefer intermediate canopy cover conditions in their vicinity. Nests remained clustered because of the heterogeneous habitat conditions, but spatial distribution has changed due to canopy alteration along time. The analysis on the NDVI was effective for 82% of cases (i.e., nests occurred within 30 m from favorable habitats). For all the remaining nests (18%), the Google Earth high resolution satellite image revealed in their vicinity the presence of small canopy gaps (undetected by the NDVI). These results show that historical satellite images are very useful for explaining the long-term dynamics of ant colonies. In addition, the use of modern remote sensing techniques provides a reliable and expedite method in determining the presence of favorable small-scale habitat, offering a very useful tool for ecological monitoring across large landscapes and in very different areas, especially in the context of ecosystem dynamics driven and exacerbated by climate change.

Download Full-text

A Research on Identify Building Objects from Satellite Images

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f1180.0986s319 ◽

2019 ◽

Vol 8 (6S3) ◽

pp. 1091-1094

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Detection Method ◽

Satellite Image ◽

Google Earth ◽

Geographical Information ◽

Travel Planning ◽

Land Changes ◽

Periodic Changes

Paper presents an innovative Building Object Detection method from the satellite image, which is using the method - Normalized Difference Vegetation Index (NDVI). Remote Sensing and Geographical Information System methods are used frequently in the field of planning and determination of land changes. Recently, Google earth images are used in most of the applications like urban and travel planning etc. The NDVI method is the key aspect to detect the building object automatically. The idea of the proposed method is to detect and identify the building object and periodic changes in the area were detected automatically.

Download Full-text

Study of the dynamic natural forest groups of Lebanese rice Cedrus libani A. Richard in Syria: دراسة ديناميكية المجموعات الحرجية الطبيعية للأرز اللبناني Cedrus libani A. Richard في سورية

Journal of engineering sciences and information technology - مجلة العلوم الهندسية و تكنولوجيا المعلومات ◽

10.26389/ajsrp.a090917 ◽

2017 ◽

Vol 1 (3) ◽

Author(s):

Abeer Ahmed Ibrahim

Keyword(s):

Health Status ◽

High Resolution ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Satellite Image ◽

Google Earth ◽

Landsat Images ◽

Cedrus Libani ◽

Ndvi Index ◽

Natural Stands

The aim of this study is to assess the dynamics of the forest stands of Cedrus libani A. Richard in its only natural area in Syria - Slenfeh and Jawbat Burghal. The spatial and temporal change of the natural stands of Cedrus libani during the period 1984-2011 and their health status during the period 1984-2014 were assessed using Remote Sensing and Geographic Information Systems (GIS). A high-resolution satellite image was used in 2011 and 17 Landsat images Landsat various sensors; Landsat_4, 5 and 8 and the NDVI Index were used during 1984-2014, high-resolution Google Earth (2 m). The direction and amount of the NDVI index of the Cedrus libani samples studied during the years of study were determined using ANOVA in the SPSS. The results showed a clear decrease in the Cedrus libani area size in both study sites Slenfeh and Jawbat Burghal in 2011 compared to 1984. The results also revealed a signiﬁcant increase trend of Normalized Difference Vegetation Index (NDVI) for natural stands of Cedrus libani in Slenfeh and Jawbat Burghal during 1984-2014, which reflects the good health status of the natural Cedar stands in Syria.

Download Full-text

HEALTHINESS OF OIL PALM PLANTATION TOWARDS SUSTAINABILITY OF ENVIRONMENT

Malaysian Journal of Sustainable Environment ◽

10.24191/myse.v7i1.8905 ◽

2020 ◽

Vol 7 (1) ◽

pp. 21

Author(s):

Faradina Marzukhi ◽

Nur Nadhirah Rusyda Rosnan ◽

Md Azlin Md Said

Keyword(s):

Oil Palm ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Satellite Image ◽

Vegetation Indices ◽

Soil Nutrient ◽

Oil Palm Plantation ◽

Sustainable Environment ◽

Moisture Analysis ◽

The Relationship

The aim of this study is to analyse the relationship between vegetation indices of Normalized Difference Vegetation Index (NDVI) and soil nutrient of oil palm plantation at Felcra Nasaruddin Bota in Perak for future sustainable environment. The satellite image was used and processed in the research. By Using NDVI, the vegetation index was obtained which varies from -1 to +1. Then, the soil sample and soil moisture analysis were carried in order to identify the nutrient values of Nitrogen (N), Phosphorus (P) and Potassium (K). A total of seven soil samples were acquired within the oil palm plantation area. A regression model was then made between physical condition of the oil palms and soil nutrients for determining the strength of the relationship. It is hoped that the risk map of oil palm healthiness can be produced for various applications which are related to agricultural plantation.

Download Full-text