scholarly journals Detecting Forest Fire Area using Landsat Images: A Case Study of Manang District, Nepal

2021 ◽  
Author(s):  
Pawan Thapa
Keyword(s):  
Forest Fires ◽  
Near Infrared ◽  
Human Life ◽  
Vegetation Indices ◽  
Google Earth ◽  
Burn Severity ◽  
Landsat 8 ◽  
Landsat Images ◽  
Severity Rating ◽  
Forest Regions

Abstract Background: Wildfires are on the rise for various reasons, including hunting, the growth of new plants, and the encroachment of forest regions, particularly in developing countries. As a result, it will lose its environment, property, wildlife, and human life. Methods: It generates a burn severity map that can estimate the extent of wildfire damage. The nine bands and vegetation indices are derived using Google Earth Engine (GEE) and the Quantum Geographic Information System (QGIS) platform from Landsat 8 satellite imagery. The Manang district employs wavelengths near-infrared (NIR) and shortwave-infrared (SWIR) to determine burnt patches and burn severity. Results: According to the evaluation, 26 percent of forest fires have moderate, low, high, and higher severity; however, 30 percent of unburned and low-severity fires receive a severity rating of 37 percent. Thus, it shows a considerable rise in wildfires in the Manang area. Conclusion: In general, it has been a novel technique for recognizing wildfire hotspots and mapping their intensity in higher elevations that takes fewer resources and time. Such necessary data assists vital stakeholders, communities, and decision-makers in making well-informed decisions.

Improvement of mapping vegetation cover for arid and semiarid areas using a local nonlinear modelling method and landsat images

2020 ◽  
Vol 42 (3) ◽  
pp. 161
Author(s):  
H. Sun ◽  
Q. Wang ◽  
G. X. Wang ◽  
P. Luo ◽  
F. G. Jiang
Keyword(s):  
Vegetation Cover ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Landsat 8 ◽  
Landsat Images ◽  
Modelling Method ◽  
Semiarid Areas ◽  
Arid And Semiarid Areas ◽  
Land Degradation And Desertification ◽  
Arid And Semiarid

Accurately estimating and mapping vegetation cover for monitoring land degradation and desertification of arid and semiarid areas using remotely sensed images is promising but challenging in remote, sparsely vegetated and large areas. In this study, a novel method – geographically weighted logistic regression (GWLR – integrating geographically weighted regression (GWR) and a logistic model) was proposed to improve vegetation cover mapping of Kangbao County, Hebei of China using Landsat 8 image and field data. Additionally, a new method to determine the bandwidth of GWLR is presented. Using cross-validation, GWLR was compared with a globally linear stepwise regression (LSR), a local linear modelling method GWR and a nonparametric method, k-nearest neighbours (kNN) with varying numbers of nearest plots. Results demonstrated (1) the red and near infrared relevant band ratios and vegetation indices significantly improved mapping; (2) the GWLR, GWR and kNN methods led to more accurate predictions than LSR; (3) GWLR reduced overestimations and underestimations compared with LSR, kNN and GWR, and also eliminated negative and very large estimates caused by GWR and LSR; and (4) The maximum distance of spatial autocorrelation could be used to determine the bandwidth for GWLR. Overall, GWLR proved more promising for mapping vegetation cover of arid and semiarid areas.

scholarly journals Canopy Near-infrared Observation Project

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Garrett Michael Shuldes
Keyword(s):  
Primary Production ◽  
Vegetation Index ◽  
Near Infrared ◽  
Net Primary Production ◽  
Gross Primary Production ◽  
Vegetation Indices ◽  
Landsat 8 ◽  
Infrared Observation ◽  
Old Growth Forest ◽  
Forest Regions

The Canopy Near-infrared Observing Project (CaNOP) will utilize a multispectral pushbroom imager in a 3U CubeSat to carry out spectrally resolved imaging of global forest regions with spectral resolution sufficient to reproduce the LandSat 8 mission and calculate vegetation indices. The project provides an educational experience for undergraduate students to design, build, test, and operate a 3U CubeSat. Using concepts from fields such as physics, engineering, and computer science, students were to link the electrical, mechanical, and software components of the satellite together to create a flawless flow of information and power across the entirety of the satellite and its corresponding ground stations to which the images of the global forest regions will be transferred and analyzed. Two vegetation indices; the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) will be used in combination with the Net Primary Production (NPP) of any forest to produce the Gross Primary Production (GPP), which will then provide the information required to answer the argument that the CaNOP team’s hypothesis proposes: An old-growth forest will absorb more carbon than a newly grown secondary forest.

scholarly journals Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform

2021 ◽  
Vol 13 (22) ◽  
pp. 4683
Author(s):  
Masoumeh Aghababaei ◽  
Ataollah Ebrahimi ◽  
Ali Asghar Naghipour ◽  
Esmaeil Asadi ◽  
Jochem Verrelst
Keyword(s):  
Vegetation Index ◽  
Google Earth ◽  
Optimal Time ◽  
Quantitative Detection ◽  
Landsat 8 ◽  
Vegetation Types ◽  
Landsat Images ◽  
Multi Temporal ◽  
Google Earth Engine ◽  
Land Covers

Vegetation Types (VTs) are important managerial units, and their identification serves as essential tools for the conservation of land covers. Despite a long history of Earth observation applications to assess and monitor land covers, the quantitative detection of sparse VTs remains problematic, especially in arid and semiarid areas. This research aimed to identify appropriate multi-temporal datasets to improve the accuracy of VTs classification in a heterogeneous landscape in Central Zagros, Iran. To do so, first the Normalized Difference Vegetation Index (NDVI) temporal profile of each VT was identified in the study area for the period of 2018, 2019, and 2020. This data revealed strong seasonal phenological patterns and key periods of VTs separation. It led us to select the optimal time series images to be used in the VTs classification. We then compared single-date and multi-temporal datasets of Landsat 8 images within the Google Earth Engine (GEE) platform as the input to the Random Forest classifier for VTs detection. The single-date classification gave a median Overall Kappa (OK) and Overall Accuracy (OA) of 51% and 64%, respectively. Instead, using multi-temporal images led to an overall kappa accuracy of 74% and an overall accuracy of 81%. Thus, the exploitation of multi-temporal datasets favored accurate VTs classification. In addition, the presented results underline that available open access cloud-computing platforms such as the GEE facilitates identifying optimal periods and multitemporal imagery for VTs classification.

Assessment of the State of the Territories of Closed Waste Disposal Facilities by Vegetation Indices

2021 ◽  
Vol 25 (9) ◽  
pp. 30-37
Author(s):  
N.N. Sliusar ◽  
A.P. Belousova ◽  
G.M. Batrakova ◽  
R.D. Garifzyanov ◽  
M. Huber-Humer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Waste Disposal ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Cost Effective ◽  
Google Earth ◽  
The State ◽  
Landsat 8 ◽  
The Earth

The possibilities of using remote sensing of the Earth data to assess the formation of phytocenoses at reclaimed dumps and landfills are presented. The objects of study are landfills and dumps in the Perm Territory, which differed from each other in the types and timing of reclamation work. The state of the vegetation cover on the reclaimed and self-overgrowing objects was compared with the reference plots with naturally formed herbage of zonal meadow vegetation. The process of reclamation of the territory of closed landfills was assessed by the presence and homogeneity of the vegetation layer and by the values of the vegetation index NDVI. To identify the dynamics of changes in the vegetation cover, we used multi-temporal satellite images from the open resources of Google Earth and images in the visible and infrared ranges of the Landsat-5/TM and Landsat-8/OLI satellites. It is shown that the data of remote sensing of the Earth, in particular the analysis of vegetation indices, can be used to assess the dynamics of overgrowing of territories of reclaimed waste disposal facilities, as well as an additional and cost-effective method for monitoring the restoration of previously disturbed territories.

scholarly journals Optimizing kNN for Mapping Vegetation Cover of Arid and Semi-Arid Areas Using Landsat images

2018 ◽  
Vol 10 (8) ◽  
pp. 1248 ◽  
Author(s):  
Hua Sun ◽  
Qing Wang ◽  
Guangxing Wang ◽  
Hui Lin ◽  
Peng Luo ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Near Infrared ◽  
Good Alternative ◽  
Landsat 8 ◽  
Arid Areas ◽  
Landsat Images ◽  
Infrared Band ◽  
K Value ◽  
Mean Square Errors ◽  
Semi Arid

Land degradation and desertification in arid and semi-arid areas is of great concern. Accurately mapping percentage vegetation cover (PVC) of the areas is critical but challenging because the areas are often remote, sparsely vegetated, and rarely populated, and it is difficult to collect field observations of PVC. Traditional methods such as regression modeling cannot provide accurate predictions of PVC in the areas. Nonparametric constant k-nearest neighbors (Cons_kNN) has been widely used in estimation of forest parameters and is a good alternative because of its flexibility. However, using a globally constant k value in Cons_kNN limits its ability of increasing prediction accuracy because the spatial variability of PVC in the areas leads to spatially variable k values. In this study, a novel method that spatially optimizes determining the spatially variable k values of Cons_kNN, denoted with Opt_kNN, was proposed to map the PVC in both Duolun and Kangbao County located in Inner Mongolia and Hebei Province of China, respectively, using Landsat 8 images and sample plot data. The Opt_kNN was compared with Cons_kNN, a linear stepwise regression (LSR), a geographically weighted regression (GWR), and random forests (RF) to improve the mapping for the study areas. The results showed that (1) most of the red and near infrared band relevant vegetation indices derived from the Landsat 8 images had significant contributions to improving the mapping accuracy; (2) compared with LSR, GWR, RF and Cons-kNN, Opt_kNN resulted in consistently higher prediction accuracies of PVC and decreased relative root mean square errors by 5%, 11%, 5%, and 3%, respectively, for Duolun, and 12%, 1%, 23%, and 9%, respectively, for Kangbao. The Opt_kNN also led to spatially variable and locally optimal k values, which made it possible to automatically and locally optimize k values; and (3) the RF that has become very popular in recent years did not perform the predictions better than the Opt_kNN for the both areas. Thus, the proposed method is very promising to improve mapping the PVC in the arid and semi-arid areas.

scholarly journals Spectral characterization of forest plantations with Landsat 8/OLI images for forest planning and management

2017 ◽  
Vol 52 (11) ◽  
pp. 1072-1079 ◽  
Author(s):  
Elisiane Alba ◽  
Eliziane Pivotto Mello ◽  
Juliana Marchesan ◽  
Emanuel Araújo Silva ◽  
Juliana Tramontina ◽  
...  
Keyword(s):  
Near Infrared ◽  
Vegetation Indices ◽  
Pinus Elliottii ◽  
Growth Stages ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Infrared Band ◽  
Near Infrared Reflectance ◽  
Planning And Management ◽  
Forest Planning And Management

Abstract: The objective of this work was to evaluate the use of Landsat 8/OLI images to differentiate the age and estimate the total volume of Pinus elliottii, in order to determine the applicability of these data in the planning and management of forest activity. Fifty-three sampling units were installed, and dendrometric variables of 9-and-10-year-old P. elliottii commercial stands were measured. The digital numbers of the image were converted into surface reflectance and, subsequently, vegetation indices were determined. Red and near-infrared reflectance values were used to differentiate the ages of the stands. Regression analysis of the spectral variables was used to estimate the total volume. Increase in age caused an addition in reflectance in the near-infrared band and a decrease in the red band. The general equation for estimating the total volume for P.elliottii had an R2adj of 0.67 with a Syx of 31.46 m3 ha-1. Therefore, the spectral data with medium spatial resolution from the Landsat 8/OLI satellite can be used to distinguish the growth stages of the stands and can, thus, be used in the planning and proper management of forest activity on a spatial and temporal scale.

scholarly journals COMPARISON OF MODEL ACCURACY IN TREE CANOPY DENSITY ESTIMATION USING SINGLE BAND, VEGETATION INDICES AND FOREST CANOPY DENSITY (FCD) BASED ON LANDSAT-8 IMAGERY (CASE STUDY: PEAT SWAMP FOREST IN RIAU PROVINCE)

2018 ◽  
Vol 15 (1) ◽  
pp. 81 ◽  
Author(s):  
Faisal Ashaari ◽  
Muhammad Kamal ◽  
Dede Dirgahayu
Keyword(s):  
Remote Sensing ◽  
Forest Canopy ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Single Band ◽  
Tree Canopy ◽  
Landsat 8 ◽  
Swamp Forest ◽  
Canopy Density ◽  
Forest Canopy Density

Identification of a tree canopy density information may use remote sensing data such as Landsat-8 imagery. Remote sensing technology such as digital image processing methods could be used to estimate the tree canopy density. The purpose of this research was to compare the results of accuracy of each method for estimating the tree canopy density and determine the best method for mapping the tree canopy density at the site of research. The methods used in the estimation of the tree canopy density are Single band (green, red, and near-infrared band), vegetation indices (NDVI, SAVI, and MSARVI), and Forest Canopy Density (FCD) model. The test results showed that the accuracy of each method: green 73.66%, red 75.63%, near-infrared 75.26%, NDVI 79.42%, SAVI 82.01%, MSARVI 82.65%, and FCD model 81.27%. Comparison of the accuracy results from the seventh methods indicated that MSARVI is the best method to estimate tree canopy density based on Landsat-8 at the site of research. Estimation tree canopy density with MSARVI method showed that the canopy density at the site of research predominantly 60-70% which spread evenly.

scholarly journals Forest Fire Area Detection by Using Landsat-8 and Sentinel-2 Satellite Images: A Case Study in Mugla, Turkey

2020 ◽  
Author(s):  
Bahadir Kurnaz ◽  
Caglar Bayik ◽  
Saygin Abdikan
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Satellite Images ◽  
Near Infrared ◽  
Kappa Statistics ◽  
Landsat 8 ◽  
Important Place ◽  
Burn Scar ◽  
Object Based ◽  
Sentinel 2

Abstract Background: Forests have an extremely important place in the ecosystem in terms of ensuring social and environmental balance. The biggest danger for forests that have this importance is forest fires due to various reasons. It is extremely important to estimate the formation and behavior characteristics of fires in terms of combating forest fires. Using the satellite images obtained with the developing technology for this purpose provides great convenience in the detection of the fire areas and the severity of the fire affected. In this study, forest fire that occurred in the Zeytinköy region of Muğla province was investigated using remotely sensed images. According to the reference data provided by the General Directorate of Forestry (GDF), 425 hectares of area was destroyed by fire. In this study, it is aimed to extract burn scar by applying seven vegetation indexes on Sentinel-2 and Landsat-8 satellite images. Additionally, forest fire areas have been determined with the object-based classification technique. Results: As a result of the study, when the obtained results are compared with the values obtained from GDF, it is determined that object based analysis of Sentinel-2 provided the highest accuracy with 98.36% overall accuracy and 0.976 kappa statistics. Comparing the results of spectral indices of Sentinel-2 and Landsat-8, Sentinel-2 resulted better results in all indices. Among the indices RdNBR and dNDVI obtained better results than other indices with Sentinel-2 and Landsat-8, respectively. Conclusions: In general, it has been determined that Sentinel-2 data is more suitable than Landsat-8 satellite images for determining Turkish red pine forest fired areas. Red and near infrared based images can be used for rapid mapping of fired areas. The results also indicated that the indices provided by multi-temporal Sentinel-2 data can assist forest management for rapid monitoring of fire scars and also for evolution of reforestation after fire.

scholarly journals Monitoring Wildfires in the Northeastern Peruvian Amazon Using Landsat-8 and Sentinel-2 Imagery in the GEE Platform

2020 ◽  
Vol 9 (10) ◽  
pp. 564 ◽  
Author(s):  
Elgar Barboza Castillo ◽  
Efrain Y. Turpo Cayo ◽  
Cláudia Maria de Almeida ◽  
Rolando Salas López ◽  
Nilton B. Rojas Briceño ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Near Infrared ◽  
Google Earth ◽  
Landsat 8 ◽  
Peruvian Amazon ◽  
Great Loss ◽  
Mid Infrared ◽  
Normalized Burn Ratio ◽  
Burnt Areas ◽  
Sentinel 2

During the latest decades, the Amazon has experienced a great loss of vegetation cover, in many cases as a direct consequence of wildfires, which became a problem at local, national, and global scales, leading to economic, social, and environmental impacts. Hence, this study is committed to developing a routine for monitoring fires in the vegetation cover relying on recent multitemporal data (2017–2019) of Landsat-8 and Sentinel-2 imagery using the cloud-based Google Earth Engine (GEE) platform. In order to assess the burnt areas (BA), spectral indices were employed, such as the Normalized Burn Ratio (NBR), Normalized Burn Ratio 2 (NBR2), and Mid-Infrared Burn Index (MIRBI). All these indices were applied for BA assessment according to appropriate thresholds. Additionally, to reduce confusion between burnt areas and other land cover classes, further indices were used, like those considering the temporal differences between pre and post-fire conditions: differential Mid-Infrared Burn Index (dMIRBI), differential Normalized Burn Ratio (dNBR), differential Normalized Burn Ratio 2 (dNBR2), and differential Near-Infrared (dNIR). The calculated BA by Sentinel-2 was larger during the three-year investigation span (16.55, 78.50, and 67.19 km2) and of greater detail (detected small areas) than the BA extracted by Landsat-8 (16.39, 6.24, and 32.93 km2). The routine for monitoring wildfires presented in this work is based on a sequence of decision rules. This enables the detection and monitoring of burnt vegetation cover and has been originally applied to an experiment in the northeastern Peruvian Amazon. The results obtained by the two satellites imagery are compared in terms of accuracy metrics and level of detail (size of BA patches). The accuracy for Landsat-8 and Sentinel-2 in 2017, 2018, and 2019 varied from 82.7–91.4% to 94.5–98.5%, respectively.

scholarly journals Application of an improved vegetation index from the visible spectrum in the diagnosis of degraded pastures: Implications for development

2021 ◽  
Author(s):  
Thiago Quinaia ◽  
Renato Valle Junior ◽  
Victor Coelho ◽  
Rafael Cunha ◽  
Carlos Valera ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Visible Spectrum ◽  
Google Earth ◽  
Detection Accuracy ◽  
Landsat 8 ◽  
Pasture Management ◽  
Efficient Detection ◽  
Pasture Degradation ◽  
Degraded Pastures

Inadequate pasture management causes land degradation and negative impacts on the socio-economic development of agricultural regions. Given the importance for Brazil and the World of pasture-based livestock production, the recognition of pasture degradation is essential. The use of remote sensing satellite systems to detect degraded pastures increased in the recent past, because of their capability to survey large portions of Earth’s surface. A struggle nowadays is to improve detection accuracy and to implement high-resolution surveys at farmland scale using unmanned aerial vehicles (UAVs). The satellite sensors capture reflectance from the visible spectrum and near infrared bands, which allows estimating plant’s vigor vegetation indices. The NDVI is a widely accepted index, but to generate an NDVI map using a UAV a relatively high-cost multispectral sensor is required, while most UAVs are equipped with low-cost RGB cameras. In the present study, a script developed on the Google Earth Engine image-processing platform manipulated images from the Landsat 8 satellite, and compared the performances of NDVI and an improved color index that we coined “Total Brightness Quotient” of red (TBQR), green (TBQG) and blue (TBQB) bands. An efficient detection of pasture degradation using the TBQs would be a good prognosis for the surveys at farm scale where environmental authorities are progressively using UAVs and forcing landowners towards pasture restoration. When compared to NDVI, the TBQG showed a correlation of 0.965 and an accuracy of 88.63%. Thus, the TBQG proved as efficient as the NDVI in the diagnosis of degraded pastures.

Sign in / Sign up

Export Citation Format

Share Document