scholarly journals Evaluation of Optical Remote Sensing Data in Burned Areas Mapping of Thasos Island, Greece

2020 ◽  
Vol 4 (4) ◽  
pp. 813-826
Author(s):  
Mohamed Elhag ◽  
Nese Yimaz ◽  
Jarbou Bahrawi ◽  
Silvena Boteva
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Mediterranean Forests ◽  
Burned Areas ◽  
Multi Temporal

AbstractForest fires are a common feature in the Mediterranean forests through the years, as a wide tract of forest fortune is lost because of the incendiary fires in the forests. The enormous damages caused by forest fires enhanced the efforts of scientists towards the attenuation of the negative effects of forest fire and consequently the minimization of biodiversity losses by searching more for the adequate distribution of attempts on forest fire prevention and, suppression. The multi-temporal Principal Components Analysis is applied to a pair of images of consecutive years obtained from Landsat-8 satellite to unconventional map and assess the spatial extent of the burned areas on the island of Thasos, Greece. First, the PCA was applied on the before fire image, and then a multi-temporal image is created from the 3rd, 4th, and 5th band of before and after images including Normalized Difference Vegetation Index to enhance the results. The results from the different steps of this analysis robustly mapped the burned areas by 82.28 ha confirmed by almost 85%. Are compared with data provided by the local forest service in order to assess their accuracy. The multi-temporal PCA outputs including NDVI (PC 4, PC %, and PC 6) give better accuracy due to its ability to distinguish the burned areas of older years and to the Normalized Difference Vegetation Index that gives better variance to the image.

scholarly journals A Statistical and Spatial Analysis of Portuguese Forest Fires in Summer 2016 Considering Landsat 8 and Sentinel 2A Data

Environments ◽  
2019 ◽  
Vol 6 (3) ◽  
pp. 36 ◽  
Author(s):  
Ana Teodoro ◽  
Ana Amaral
Keyword(s):  
Forest Fires ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Burn Severity ◽  
Landsat 8 ◽  
Fire Event ◽  
Burned Areas ◽  
Area Burned ◽  
Sentinel 2A ◽  
Normalized Burn Ratio

Forest areas in Portugal are often affected by fires. The objective of this work was to analyze the most fire-affected areas in Portugal in the summer of 2016 for two municipalities considering data from Landsat 8 OLI and Sentinel 2A MSI (prefire and postfire data). Different remote sensed data-derived indices, such as Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR), could be used to identify burnt areas and estimate the burn severity. In this work, NDVI was used to evaluate the area burned, and NBR was used to estimate the burn severity. The results showed that the NDVI decreased considerably after the fire event (2017 images), indicating a substantial decrease in the photosynthesis activity in these areas. The results also indicate that the NDVI differences (dNDVI) assumes the highest values in the burned areas. The results achieved for both sensors regarding the area burned presented differences from the field data no higher than 13.3% (for Sentinel 2A, less than 7.8%). We conclude that the area burned estimated using the Sentinel 2A data is more accurate, which can be justified by the higher spatial resolution of this data.

scholarly journals Relationship between Soil Burn Severity in Forest Fires Measured In Situ and through Spectral Indices of Remote Detection

Forests ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 457 ◽  
Author(s):  
Jose Sobrino ◽  
Rafael Llorens ◽  
Cristina Fernández ◽  
José Fernández-Alonso ◽  
José Vega
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Forest Fires ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Spectral Indices ◽  
Soil Burn Severity ◽  
Sentinel 2

Forest fires in Galicia have become a serious environmental problem over the years. This is especially the case in the Pontevedra region, where in October 2017 large fires (>500 hectares) burned more than 15,000 Ha. In addition to the area burned being of relevance, it is also very important to know quickly and accurately the different severity degrees that soil has suffered in order to carry out an optimal restoration campaign. In this sense, the use of remote sensing with the Sentinel-2 and Landsat-8 satellites becomes a very useful resource due to the variations that appear in soil after a forest fire (changes in soil cover are noticeably appreciated with spectral information). To calculate these variations, the spectral indices NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) are used, both before and after the fire and their differences (dNBR and dNDVI, respectively). In addition, as a reference for a correct discrimination between severity degrees, severity data measured in situ after the fire are used to classified at 5 levels of severity and 6 levels of severity. Therefore, this study aims to establish a methodology, which relates remote-sensing data (spectral indices) and severity degrees measured in situ. The R2 statistic and the pixel classification accuracy results show the existing synergy of the Sentinel-2 dNBR index with the 5 severity degrees classification (R2 = 0.74 and 81% of global accuracy) and, for this case, the good applicability of remote sensing in the forest fire field.

scholarly journals Consideration of NDVI thematic changes in density analysis and floristic composition of Wadi Yalamlam, Saudi Arabia

2018 ◽  
Vol 7 (4) ◽  
pp. 297-306 ◽  
Author(s):  
Amal Y. Aldhebiani ◽  
Mohamed Elhag ◽  
Ahmad K. Hegazy ◽  
Hanaa K. Galal ◽  
Norah S. Mufareh
Keyword(s):  
Saudi Arabia ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Floristic Composition ◽  
Water Source ◽  
Shannon Index ◽  
Landsat 8 ◽  
Main Stream

Abstract. Wadi Yalamlam is known as one of the significant wadis in the west of Saudi Arabia. It is a very important water source for the western region of the country. Thus, it supplies the holy places in Mecca and the surrounding areas with drinking water. The floristic composition of Wadi Yalamlam has not been comprehensively studied. For that reason, this work aimed to assess the wadi vegetation cover, life-form presence, chorotype, diversity, and community structure using temporal remote sensing data. Temporal datasets spanning 4 years were acquired from the Landsat 8 sensor in 2013 as an early acquisition and in 2017 as a late acquisition to estimate normalized difference vegetation index (NDVI) changes. The wadi was divided into seven stands. Stands 7, 1, and 3 were the richest with the highest Shannon index values of 2.98, 2.69, and 2.64, respectively. On the other hand, stand 6 has the least plant biodiversity with a Shannon index of 1.8. The study also revealed the presence of 48 different plant species belonging to 24 families. Fabaceae (17 %) and Poaceae (13 %) were the main families that form most of the vegetation in the study area, while many families were represented by only 2 % of the vegetation of the wadi. NDVI analysis showed that the wadi suffers from various types of degradation of the vegetation cover along with the wadi main stream.

scholarly journals Estimation of different data compositions for early-season crop type classification

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4834 ◽  
Author(s):  
Pengyu Hao ◽  
Mingquan Wu ◽  
Zheng Niu ◽  
Li Wang ◽  
Yulin Zhan
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Classification Performance ◽  
Distribution Maps ◽  
Production Forecasting ◽  
Crop Type ◽  
Ndvi Time Series ◽  
Multi Temporal

Timely and accurate crop type distribution maps are an important inputs for crop yield estimation and production forecasting as multi-temporal images can observe phenological differences among crops. Therefore, time series remote sensing data are essential for crop type mapping, and image composition has commonly been used to improve the quality of the image time series. However, the optimal composition period is unclear as long composition periods (such as compositions lasting half a year) are less informative and short composition periods lead to information redundancy and missing pixels. In this study, we initially acquired daily 30 m Normalized Difference Vegetation Index (NDVI) time series by fusing MODIS, Landsat, Gaofen and Huanjing (HJ) NDVI, and then composited the NDVI time series using four strategies (daily, 8-day, 16-day, and 32-day). We used Random Forest to identify crop types and evaluated the classification performances of the NDVI time series generated from four composition strategies in two studies regions from Xinjiang, China. Results indicated that crop classification performance improved as crop separabilities and classification accuracies increased, and classification uncertainties dropped in the green-up stage of the crops. When using daily NDVI time series, overall accuracies saturated at 113-day and 116-day in Bole and Luntai, and the saturated overall accuracies (OAs) were 86.13% and 91.89%, respectively. Cotton could be identified 40∼60 days and 35∼45 days earlier than the harvest in Bole and Luntai when using daily, 8-day and 16-day composition NDVI time series since both producer’s accuracies (PAs) and user’s accuracies (UAs) were higher than 85%. Among the four compositions, the daily NDVI time series generated the highest classification accuracies. Although the 8-day, 16-day and 32-day compositions had similar saturated overall accuracies (around 85% in Bole and 83% in Luntai), the 8-day and 16-day compositions achieved these accuracies around 155-day in Bole and 133-day in Luntai, which were earlier than the 32-day composition (170-day in both Bole and Luntai). Therefore, when the daily NDVI time series cannot be acquired, the 16-day composition is recommended in this study.

scholarly journals Fractional Vegetation Cover Change Detection In Megacities Using Landsat Time-Series Images: A Case Study Of Hanoi City (Vietnam) During 1986-2019

2019 ◽  
Vol 12 (4) ◽  
pp. 175-187
Author(s):  
Thanh Tien Nguyen
Keyword(s):  
Rural Areas ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Classification Method ◽  
Landsat 8 ◽  
Landsat Images ◽  
Fractional Vegetation Cover ◽  
Multi Temporal

The objective of the study is to assess changes of fractional vegetation cover (FVC) in Hanoi megacity in period of 33 years from 1986 to 2016 based on a two endmember spectral mixture analysis (SMA) model using multi-spectral and multi-temporal Landsat-5 TM and -8 OLI images. Landsat TM/OLI images were first radiometrically corrected. FVC was then estimated by means of a combination of Normalized Difference Vegetation Index (NDVI) and classification method. The estimated FVC results were validated using the field survey data. The assessment of FVC changes was finally carried out using spatial analysis in GIS. A case study from Hanoi city shows that: (i) the proposed approach performed well in estimating the FVC retrieved from the Landsat-8 OLI data and had good consistency with in situ measurements with the statistically achieved root mean square error (RMSE) of 0.02 (R 2 =0.935); (ii) total FVC area of 321.6 km 2 (accounting for 9.61% of the total area) was slightly reduced in the center of the city, whereas, FVC increased markedly with an area of 1163.6 km 2 (accounting for 34.78% of the total area) in suburban and rural areas. The results from this study demonstrate the combination of NDVI and classification method using Landsat images are promising for assessing FVC change in megacities.

scholarly journals DETECTION OF ACID SLUDGE CONTAMINATED AREA BASED ON NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI) VALUE

2017 ◽  
Vol 11 (1) ◽  
pp. 21
Author(s):  
Nanik Suryo Haryani ◽  
Sayidah Sulma ◽  
Junita Monika Pasaribu
Keyword(s):  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Dry Season ◽  
Wet Season ◽  
Multi Temporal ◽  
Acid Sludge ◽  
Landsat Satellite ◽  
Solid Form

The solid form of oil heavy metal waste is  known as acid sludge. The aim of this research is to exercise the correlation between acid sludge concentration in soil and NDVI value, and further studying the Normalized Difference Vegetation Index (NDVI) anomaly by multi-temporal Landsat satellite images. The implemented method is NDVI.  In this research, NDVI is analyzed using the  remote sensing data  on dry season and wet season.  Between 1997 to 2012, NDVI value in dry season  is around – 0.007 (July 2001) to 0.386 (May 1997), meanwhile in wet season  NDVI value is around – 0.005 (November 2006) to 0.381 (December 1995).  The high NDVI value shows the leaf health or  thickness, where the low NDVI indicates the vegetation stress and rareness which can be concluded as the evidence of contamination. The rehabilitation has been executed in the acid sludge contaminated location, where the high value of NDVI indicates the successfull land rehabilitation effort.

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.

Effect of Locust Invasion and Mitigation Using Remote Sensing Techniques: A Case Study of North Sindh Pakistan

2022 ◽  
Vol 88 (1) ◽  
pp. 47-53
Author(s):  
Muhammad Nasar Ahmad ◽  
Zhenfeng Shao ◽  
Orhan Altan
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Landsat 8 ◽  
Abrupt Decrease ◽  
Computing Platform ◽  
Remote Sensing Techniques ◽  
Processing Techniques

This study comprises the identification of the locust outbreak that happened in February 2020. It is not possible to conduct ground-based surveys to monitor such huge disasters in a timely and adequate manner. Therefore, we used a combination of automatic and manual remote sensing data processing techniques to find out the aftereffects of locust attack effectively. We processed MODIS -normalized difference vegetation index (NDVI ) manually on ENVI and Landsat 8 NDVI using the Google Earth Engine (GEE ) cloud computing platform. We found from the results that, (a) NDVI computation on GEE is more effective, prompt, and reliable compared with the results of manual NDVI computations; (b) there is a high effect of locust disasters in the northern part of Sindh, Thul, Ghari Khairo, Garhi Yaseen, Jacobabad, and Ubauro, which are more vulnerable; and (c) NDVI value suddenly decreased to 0.68 from 0.92 in 2020 using Landsat NDVI and from 0.81 to 0.65 using MODIS satellite imagery. Results clearly indicate an abrupt decrease in vegetation in 2020 due to a locust disaster. That is a big threat to crop yield and food production because it provides a major portion of food chain and gross domestic product for Sindh, Pakistan.

scholarly journals Combined Use of Multi-Temporal Landsat-8 and Sentinel-2 Images for Wheat Yield Estimates at the Intra-Plot Spatial Scale

Agronomy ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 327 ◽  
Author(s):  
Remy Fieuzal ◽  
Vincent Bustillo ◽  
David Collado ◽  
Gerard Dedieu
Keyword(s):  
Relative Error ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Wheat Yield ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Optical Images ◽  
Multi Temporal ◽  
Input Variables ◽  
Sentinel 2

The objective of this study is to address the capabilities of multi-temporal optical images to estimate the fine-scale yield variability of wheat, over a study site located in southwestern France. The methodology is based on the Landsat-8 and Sentinel-2 satellite images acquired after the sowing and before the harvest of the crop throughout four successive agricultural seasons, the reflectance constituting the input variables of a statistical algorithm (random forest). The best performances are obtained when the Normalized Difference Vegetation Index (NDVI) is combined with the yield maps collected during the crop rotation, the agricultural season 2014 showing the lower level of performances with a coefficient of determination (R2) of 0.44 and a root mean square error (RMSE) of 8.13 quintals by hectare (q.h−1) (corresponding to a relative error of 12.9%), the three other years being associated with values of R2 close or upper to 0.60 and RMSE lower than 7 q.h−1 (corresponding to a relative error inferior to 11.3%). Moreover, the proposed approach allows estimating the crop yield throughout the agricultural season, by using the successive images acquired from the sowing to the harvest. In such cases, early and accurate yield estimates are obtained three months before the end of the crop cycle. At this phenological stage, only a slight decrease in performance is observed compared to the statistic obtained just before the harvest.

scholarly journals Application of Deep Learning Algorithm to Build an Automated Cloud Segmentation Model Based on Open Data Cube Framework

2020 ◽  
Vol 36 (4) ◽  
Author(s):  
Pham Vu Dong ◽  
Bui Quang Thanh ◽  
Nguyen Quoc Huy ◽  
Vo Hong Anh ◽  
Pham Van Manh
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Rapid Development ◽  
Open Data ◽  
Remote Sensing Data ◽  
Data Cube ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Sensing Data ◽  
Multi Temporal

Cloud detection is a significant task in optical remote sensing to reconstruct the contaminated cloud area from multi-temporal satellite images. Besides, the rapid development of machine learning techniques, especially deep learning algorithms, can detect clouds over a large area in optical remote sensing data. In this study, the method based on the proposed deep-learning method called ODC-Cloud, which was built on convolutional blocks and integrating with the Open Data Cube (ODC) platform. The results showed that our proposed model achieved an overall 90% accuracy in detecting cloud in Landsat 8 OLI imagery and successfully integrated with the ODC to perform multi-scale and multi-temporal analysis. This is a pioneer study in techniques of storing and analyzing big optical remote sensing data.

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