Recovery of Forest Vegetation in a Burnt Area in the Republic of Korea: A Perspective Based on Sentinel-2 Data

Forest fires are severe disasters that cause significant damage in the Republic of Korea and the entire world, and an effort is being made to prevent forest fires internationally. The Republic of Korea budgets 3.38 million USD every year to prevent forest fires. However, an average of 430 wildfires occur nationwide annually. Thirty-eight percent of the forest fire budget is used for forest restoration. Restoring afforestation in the affected areas is a top priority. This study aimed to estimate the degree of vegetative regeneration using the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil-Adjustment Vegetation Index (SAVI), and Normalized Burn Ratio (NBR). Although many studies have used NBR with NDVI to extract plant regeneration regions, they suffer from atmospheric effects and soil brightness. Thus, this study utilizes NBR with NDVI, EVI, and SAVI to accurately select areas for targeted forest restoration. Furthermore, this study applies clustering analysis to extract the spatial boundary of vegetative regenerative regions. The proposed method suggests a pixel range of vegetation indices. These ranges can be used as an indicator, such as the NBR’s Fire Severity Level, which reflects the mountain’s local characteristics, meaning that it can be useful after forest fires. Using the three vegetation indices can extract more accurate vegetation areas than using NBR with NDVI and can help determine a forest restoration target area.

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Can We Detect the Brownness or Greenness of the Congo Rainforest Using Satellite-Derived Surface Albedo? A Study on the Role of Aerosol Uncertainties

Sustainability ◽

10.3390/su11051410 ◽

2019 ◽

Vol 11 (5) ◽

pp. 1410 ◽

Cited By ~ 3

Author(s):

Suman Moparthy ◽

Dominique Carrer ◽

Xavier Ceamanos

Keyword(s):

Remote Sensing ◽

Surface Albedo ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Satellite Observations ◽

Atmospheric Effects ◽

Forest Regions ◽

Aerosol Emissions

The ability of spatial remote sensing in the visible domain to properly detect the slow transitions in the Earth’s vegetation is often a subject of debate. The reason behind this is that the satellite products often used to calculate vegetation indices such as surface albedo or reflectance, are not always correctly decontaminated from atmospheric effects. In view of the observed decline in vegetation over the Congo during the last decade, this study investigates how effectively satellite-derived variables can contribute to the answering of this question. In this study, we use two satellite-derived surface albedo products, three satellite-derived aerosol optical depth (AOD) products, two model-derived AOD products, and synthetic observations from radiative transfer simulations. The study discusses the important discrepancies (of up to 70%) found between these satellite surface albedo products in the visible domain over this region. We conclude therefore that the analysis of trends in vegetation properties based on satellite observations in the visible domain such as NDVI (normalized difference vegetation index), calculated from reflectance or albedo variables, is still quite questionable over tropical forest regions such as the Congo. Moreover, this study demonstrates that there is a significant increase (of up to 14%) in total aerosols within the last decade over the Congo. We note that if these changes in aerosol loads are not correctly taken into account in the retrieval of surface albedo, a greenness change of the surface properties (decrease of visible albedo) of around 8% could be artificially detected. Finally, the study also shows that neglecting strong aerosol emissions due to volcano eruptions could lead to an artificial increase of greenness over the Congo of more than 25% in the year of the eruptions and up to 16% during the 2–3 years that follow.

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FIRE SEVERITY ASSESSMENT USING NDVI DERIVED FROM LANDSAT TM/ETM IMAGES AND TERRAIN DATA

10.32006/eeep.2017.1.2937 ◽

2017 ◽

pp. 29-37 ◽

Cited By ~ 1

Author(s):

Ibrahim Molla ◽

Emiliya Velizarova ◽

Mariana Zaharinova

Keyword(s):

Forest Fires ◽

Vegetation Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Fire Severity ◽

Landsat 8 ◽

Fire Occurrence ◽

One Year ◽

In Fire

The forest fires influence on the plants and soil depends on the fire severity and time of exposure. Fire severity integrates physical, chemical and biological changes occurring in ecosystems on the area as a consequence of fire influence. The purpose of the current investigation was to examine the role of the forest fire severity on the vegetation cover of the area of Svilengrad Municipality, using NDVI (Normalized Difference Vegetation Index) before fire and after fire, derived from LANDSAT 8 TM/ETM images. The comparison of the data from NDVI and that observed on the terrain data was also targeted. The results show that NDVI are changed significantly in fire affected area depending on vegetation cover and type of fire. This index also is very sensitive to changes during time after fire occurrence. One year after fire occurrence the NDVI values increased to +0.305 (0.048) for whole studied area. Through dNDVI could be distinguish the recovery rates of the fire affected areas with different tree species.

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Sensitivity of Seven MODIS Vegetation Indices to BRDF Effects during the Amazonian Dry Season

Remote Sensing ◽

10.3390/rs11141650 ◽

2019 ◽

Vol 11 (14) ◽

pp. 1650 ◽

Cited By ~ 4

Author(s):

Caio Arlanche Petri ◽

Lênio Soares Galvão

Keyword(s):

Zenith Angle ◽

Effect Size ◽

Vegetation Index ◽

Near Infrared ◽

Vegetation Indices ◽

Atmospheric Correction ◽

Dry Season ◽

Atmospheric Effects ◽

Modis Data ◽

Corrected Data

We used Moderate Resolution Imaging Spectroradiometer (MODIS) data, processed by the multi–angle implementation of atmospheric correction (MAIAC) algorithm, to investigate the sensitivity of seven vegetation indices (VIs) to bidirectional reflectance distribution function (BRDF) effects in the dry season (June–September) of the Brazilian Amazon. The analysis was first performed over three sites, located from north to south of the Amazon, and then extended into the entire region. We inspected for differences in viewing–illumination parameters and pixel quality retrievals during MODIS data acquisition over the region. By comparing and correlating corrected and non–corrected data for bidirectional effects, we evaluated monthly changes in reflectance and VIs (2000–2014). Finally, we computed the effect size of the BRDF correction using non–parametric Mann–Whitney tests and Cohen’s r metrics. The results showed that the most anisotropic VIs were the enhanced vegetation index (EVI), photochemical reflectance index (PRI), and shortwave infrared normalized difference (SWND). These VIs presented the largest relative changes and the lowest correlation coefficients, between corrected and non–corrected data, because of the large effect size of the BRDF. The least anisotropic VI was the normalized difference water index (NDWI). The anisotropy of these VIs was stronger in the northern Amazon. It increased from the beginning to the end of the dry season, following changes in the relative azimuth angle (RAA) toward the BRDF hotspot in September. The modifications in the relative proportions of backscattering observations used in composite products caused a reflectance increase in all MODIS bands at the end of the dry season, especially in the near infrared (NIR). The reflectance decreased after BRDF correction. Because of the atmospheric effects, the view zenith angle (VZA) of the pixels selected in composite products decreased toward the south of the Amazon. In the southern Amazon, the seasonal amplitude in the solar zenith angle (SZA) reached values close to 18°. For the most anisotropic index, the BRDF correction removed, on average, 30% of the EVI signal in June, and 60% of the EVI signal in September, reducing dry season variations over time. The results reinforce the need for bidirectional correction of MODIS data before the seasonal and inter–annual analyses of the most anisotropic VIs.

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Monitoring of Afforestation Activities Using Landsat-8 Temporal Images, Billion Trees Afforestation Project, Pakistan

Nova mehanizacija šumarstva ◽

10.5552/nms.2021.3 ◽

2021 ◽

Vol 42 ◽

pp. 27-40

Author(s):

Muhammad Daud Khan ◽

Saba Ali Arooj ◽

Waqar Ahmed ◽

Zia-ur Rehman ◽

Arif Iqbal ◽

...

Keyword(s):

Survival Rate ◽

Growth Performance ◽

Forest Restoration ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Robinia Pseudoacacia ◽

Vegetation Indices ◽

Eucalyptus Camaldulensis ◽

Landsat 8 ◽

Pinus Roxburghii

In context of Bonn Challenge commitment, Pakistan (Khyber Pukhtunkhwa) has implemented forest restoration and afforestation on 0.35 million hectares between 2015–2017. Billion Tree Afforestation Project (BTAP) is an initiative of mass afforestation and forest restoration to meet the Bonn Challenge commitment. The current study is a pilot study to evaluate the success of plantation activities by assessment of regeneration, growth performance and survival rate of plantations raised under BTAP in Malakand Forest Division. Further, four vegetation indices were computed from Landsat-8 image, which include Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Modified Soil Adjusted Vegetation Index (MSAVI) and Difference Vegetation Index (DVI). A total of 72 sample plots of 0.1 ha were laid out in 11 plantation sites extended over an area of 647 ha in Timargara, Chakdara and Jandool. According to the results, all the selected plantation sites showed good performance in terms of survival rate, mostly above 90%. In terms of species composition, Eucalyptus camaldulensis has the highest share (81%) followed by Robinia pseduacacia with 17% and Pinus roxburghii with 2% share in the plantation. Growth performance was good in all species; Pinus roxburghii attained an average girth of 14.3 cm and height of 3.21 feet, whereas Eucalyptus camaldulensis and Robinia pseudoacacia attained a mean girth of 10.3 and 12.1 cm with the height of 8.6 and 8.2 feet in 27 months, respectively. Further, a good correlation was observed between the volume (m3) and Landsat-8 spectral values. The highest performance (R2=0.63) was recorded by NDVI and SAVI. The temporal changes in spectral values of Landsat-8 images from 2013 to 2018 showed that the plantation was successful at these sites. The study concluded that FLR activities across the Khyber Pukhtunkwa province will rehabilitate and improve the existing forest ecosystems and support local livelihood for climate change mitigation.

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Sensitivity Analysis and Coverage Review of the Parameters for Inundation Simulations of Coastal Areas Using XP-SWMM

Korean Society of Hazard Mitigation ◽

10.9798/kosham.2021.21.5.59 ◽

2021 ◽

Vol 21 (5) ◽

pp. 59-68

Author(s):

Dongkyun Sun ◽

Taeuk Kang ◽

Sangho Lee

Keyword(s):

Sensitivity Analysis ◽

Urban Areas ◽

Input Data ◽

Coastal Areas ◽

Republic Of Korea ◽

Target Area ◽

Urban Flooding ◽

Two Dimensional ◽

Simulation Results ◽

The Republic

In the Republic of Korea, XP-SWMM is mainly used to analyze the causes of urban flooding, which occurs continuously, and to establish countermeasures. However, it is difficult for a model to be calibrated because most urban areas are ungauged. Therefore, many engineers use the default values provided in the user manual when using XP-SWMM. In this study, a sensitivity analysis of the four main parameters for simulating two-dimensional inundation with XP-SWMM was conducted. In addition, the proper ranges were reviewed for the parameters by comparing the archived map of the target area (Marine City in Busan) and the simulation results that were derived from parameter changes. The results of this study can be applied in the estimation of input data for various urban inundation analyses.

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FOREST RECOVERY IN THE TERRITORY OF THE REPUBLIC OF BASHKORTOSTAN FOR 2019

RUSSIAN ELECTRONIC SCIENTIFIC JOURNAL ◽

10.31563/2308-9644-2020-35-1-177-185 ◽

2020 ◽

Vol 35 (1) ◽

pp. 177-185

Author(s):

U.I. Ismagilova ◽

◽

I.F. Safin ◽

R.V. Mazur ◽

◽

...

Keyword(s):

Forest Fires ◽

Forest Restoration ◽

Positive Impact ◽

Forest Recovery ◽

Republic Of Bashkortostan ◽

The Republic

The article deals with the main provisions related to the process of forest restoration in 2019 on the territory of the Republic of Bashkortostan. As a result of the work, the author revealed that in 2019, the area that was subjected to artificial reforestation became significantly larger compared to 2018. in 2019, appropriate equipment was purchased, and the level of survival of forest crops increased to 93%. In 2019, the budget for reforestation in the Republic amounted to more than 2 billion rubles. The level of forest fires in the Republic in extnyjv year was not high, which also had a positive impact on reforestation and their effectiveness. In General, we can say that a clear regulation of the reforestation process has a positive impact on its effectiveness.

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Generating the Baseline in the Early Detection of Bud Rot and Red Ring Disease in Oil Palms by Geospatial Technologies

Remote Sensing ◽

10.3390/rs12193229 ◽

2020 ◽

Vol 12 (19) ◽

pp. 3229

Author(s):

Mauricio Viera-Torres ◽

Izar Sinde-González ◽

Mariluz Gil-Docampo ◽

Vladimir Bravo-Yandún ◽

Theofilos Toulkeridis

Keyword(s):

Early Detection ◽

Gross Domestic Product ◽

Oil Palm ◽

Vegetation Index ◽

Agricultural Sector ◽

Vegetation Indices ◽

Spatial Behavior ◽

Domestic Product ◽

The Republic ◽

Aircraft System

Oil palm cultivation in Ecuador is important for the agricultural sector. As a result of it, the country generates sources of employment in some of the most vulnerable zones; it contributes 0.89% of the gross domestic product and 4.35% of the agricultural gross domestic product. In 2017, a value of USD $252 million was generated by exports, and palm contributed 4.53% of the agricultural gross domestic product (GDP). It is estimated that 125,000 hectares of palm were lost in the Republic of Ecuador due to Red Ring Disease (RRD) and specifically Bud Rot (BR). The current study aimed to generate an early detection of BR and RRD in oil palm. Image acquisition has been performed using Remotely Piloted Aircraft System (RPAS) with Red, Green, and Blue (RGB) cannons, and multispectral cameras, in study areas with and without the presence of the given disease. Hereby, we proposed two phases. In phase A, a drone flight has been conducted for processing and georeferencing. This allowed to obtain an orthomosaic that serves as input for obtaining several vegetation indices of the healthy crop. The data and products obtained from this phase served as a baseline to perform comparisons with plantations affected by BR and RRD and to differentiate the palm varieties that are used by palm growers. In phase B, the same process has been applied three times with an interval of 15 days in an affected plot, in order to identify the symptoms and the progress of them. A validation for the diseases detection has been performed in the field, by taking Global Positioning System (GPS) points of the palms that presented symptoms of BR and RRD, through direct observation by field experts. The inputs obtained in each monitoring allowed to analyze the spatial behavior of the diseases. The values of the vegetation indices obtained from Phase A and B aimed to establish the differences between healthy and diseased palms, with the purpose of generating the baseline of early responses of BR and RRD conditions. However, the best vegetation index to detect the BR was the Visible Atmospherically Resistant Index (VARI).

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Minimización de los efectos atmosféricos en el índice espectral de la vegetación IVIS

REVISTA TERRA LATINOAMERICANA ◽

10.28940/terra.v36i1.230 ◽

2018 ◽

Vol 36 (1) ◽

pp. 31

Author(s):

Fernando Paz Pellat

Keyword(s):

Time Series ◽

Vegetation Index ◽

Satellite Image ◽

Vegetation Indices ◽

Atmospheric Correction ◽

Image Data ◽

Atmospheric Effects ◽

Atmospheric Conditions ◽

Atmospheric Effect ◽

Remote Sensors

It is essential to minimize atmospheric effects on spectral information of remote sensors from space platforms to avoid under estimation of biophysical variables associated with satellite image data. In this paper, a generic algorithm was developed, based on sound theoretical arguments, to analyze time series ISVI spectral vegetation index (vegetation index based on iso-soil curves), thus avoiding the problems associated with the classic design of vegetation indices, where the spectral signal saturates quickly. The results, when applying the algorithm in pixel time series of AVHRR satellite images, showed that reduction and standardization of atmospheric effects in the ISVI was achieved. Using ISVI maximum values in time series (temporal window), a reasonable approximation to atmospheric conditions with minimum or standardized effects was obtained. In conclusion, although the scheme developed failed to eliminate the atmospheric effect on ISVI entirely, it was reduced to a minimum. The algorithm developed was simple enough for operational use, with regard to atmospheric correction methods using radiative model inversions.

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