Comparison of the Vegetation Indices to Detect the Tropical Rain Forest Changes Using Breaks for Additive Seasonal and Trend (Bfast) Model

Darmawan, Yahya; Sofan, Parwati

doi:10.30536/j.ijreses.2012.v9.a1823

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…Different vegetation types respond differently to groundwater presence in aquifers [33]. NDVI is a conventional vegetation index, but recent studies suggest that the EVI, with a more robust profile in areas with atmospheric disturbance, can be derived from Landsat 8-9 data [34]. Equation ( 2) outlines the general formula for determining the EVI, covering the canopy background with the L value.…”

Section: Groundwater Availability (Ga)mentioning

confidence: 99%

Cost-Effective Groundwater Potential Mapping by Integrating Multiple Remote Sensing Data and the Index–Overlay Method

Nainggolan,

Ni,

Darmawan

et al. 2024

Remote Sensing

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The Choushui River groundwater basin (CRGB) in Yunlin County, Taiwan, is a significant groundwater source for the western part of the region. However, increasing groundwater demand and human activities have triggered a potential crisis due to overexploitation. Therefore, groundwater potential zone (GWPZ) maps are crucial for mapping groundwater resources and water resource management. This study employs the normalized index–overlay method and fuzzy extended analytical hierarchy process (FE-AHP) to map GWPZs cost-effectively. The methodology objectively incorporates weightings from various thematic layers by normalizing and correlating parameters with observed groundwater availability (GA). Site-specific observations, including aquifer thickness, depth to the groundwater level, and porosity, inform GA calculations. Seven comprehensive layers derived from remote sensing (RS) data are processed to obtain weightings and ratings for the groundwater potential index (GWPI) in the CRGB. Selected parameters are categorized into hydrological processes, human interventions, geological, and surface profiles. Hydrological processes include precipitation, modified normalized difference water index (MNDWI), and drainage density. Human interventions consist of the enhanced vegetation index (EVI) and normalized difference building index (NDBI). Surface profiles encompass the terrain ruggedness index (TRI) and slope, enhancing the study’s multi-criteria approach. The observed GA validates the GWPZ accuracy, classifying zones into five categories. According to the GWPI of FE-AHP, about 59.56% of the CRGB area can be categorized as “moderate” to “very good” potential groundwater recharge zones. Pearson’s correlation coefficient between GWPI and GA, based on FE-AHP, outperforms the conventional AHP. This RS-based approach efficiently evaluates GA in aquifers with limited wells, highlighting crucial zones in CRGB’s proximal-fan and southeastern mid-fan for informed groundwater management strategies.

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Section: Groundwater Availability (Ga)mentioning

confidence: 99%

Cost-Effective Groundwater Potential Mapping by Integrating Multiple Remote Sensing Data and the Index–Overlay Method

Nainggolan,

Ni,

Darmawan

et al. 2024

Remote Sensing

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“…On a global scale, the NDFI spectral index has been used to detect changes in the structure of tropical forests and detect degradation patterns, either with Landsat or MODIS images [5,6,7]. In Latin America, it has been used in investigations to monitor forest degradation and deforestation, mainly in the Brazilian Amazon, and to detect selective extraction activities in areas with forest management plans.…”

Section: Introductionmentioning

confidence: 99%

Spatial Analysis of Degradation in the Reserve for San Rafael National Park, Period 2005-2019

Franco¹,

Marín²,

Rodríguez³

et al. 2021

Preprint

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The goal of this study was to analyze the forest degradation in the Reserve for San Rafael National Park, Paraguay, during the period 2005-2019. This Reserve is one of the most important forest remnants of the Upper Paraná Atlantic Forest Ecoregion. A multitemporal analysis of degradation was carried out due to the occurrence of three disturbances: forest fires, a twister and illicit crops, using the Continuous Degradation Detection (CODED) algorithm, for which 3 factors were considered: variations due to pixel in the NDFI index values before, during and after every disturbance registered. In this context, the phenomenon with the greatest impact in terms of magnitude of degradation were the forest fires of 2005, being that year at the same time, the one that reported the highest degradation values. Secondly, there are the illicit crops established until the first semester of 2019, and lastly, the twister that occurred in 2017. Our findings demonstrate that CODED algorithm can detect multi-temporal degradation events in a Subtropical Broadleaf Forest, and the post-disturbance regeneration process after every disturbance tends to occur immediately. The response in terms of degradation-regeneration is highly variable, depending of the nature and severity of each disturbance and the vegetation recovery dynamics.

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“…BFAST decomposes into the time series trend, seasonal, and remainder components. BFAST was widely used to study vegetation changes in different parts of the world for various applications [32][33][34][35][36][37][38][39]. The comparison of BFAST, MRA-WT, and STL by [23] showed that BFAST was the most accurate approach for their dataset.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Analysis of MODIS NDVI in the Semi-Arid Region of Kurdistan (Iran)

et al. 2019

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In this study, the spatiotemporal behavior of vegetation cover in the Kurdistan province of Iran was analyzed for the first time by TIMESAT and Breaks for Additive Season and Trend (BFAST) algorithms. They were applied on Normalized Vegetation Index (NDVI) time series from 2000 to 2016 derived from Moderate Resolution Imaging Spectroradiometer (MODIS) observations. The TIMESAT software package was used to estimate the seasonal parameters of NDVI and their relation to land covers. BFAST was applied for identifying abrupt changes (breakpoints) of NDVI and their magnitudes. The results from TIMESAT and BFAST were first reported separately, and then interpreted together. TMESAT outcomes showed that the lowest and highest amplitudes of NDVI during the whole time period happened in 2008 and 2010. The spatial distribution of the number of breakpoints showed different behaviors in the west and east of the study area, and the breakpoint frequency confirmed the extreme NDVI amplitudes in 2008 and 2010 found by TIMESAT. For the first time in Iran, a correlation analysis between accumulated precipitations and maximum NDVIs (from one to seven months before the NDVI maximum) was conducted. The results showed that precipitation one month before had a higher correlation with the maximum NDVIs in the region. Overall, the results describe the NDVI behavior in terms of greenness, lifetime, abrupt changes for the different land covers, and across the years, suggesting how the northwest and west of the study area can be more susceptible to drought conditions.

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Comparison of the Vegetation Indices to Detect the Tropical Rain Forest Changes Using Breaks for Additive Seasonal and Trend (Bfast) Model

Cited by 4 publications

References 12 publications

Cost-Effective Groundwater Potential Mapping by Integrating Multiple Remote Sensing Data and the Index–Overlay Method

Cost-Effective Groundwater Potential Mapping by Integrating Multiple Remote Sensing Data and the Index–Overlay Method

Spatial Analysis of Degradation in the Reserve for San Rafael National Park, Period 2005-2019

Spatiotemporal Analysis of MODIS NDVI in the Semi-Arid Region of Kurdistan (Iran)

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