Modification of Temperature Vegetation Dryness Index (TVDI) Method for Detecting Drought with Multi-Scale Image

Nugraha, A Sediyo Adi; Gunawan, Totok; Kamal, Muhammad

doi:10.1088/1755-1315/1039/1/012048

Cited by 6 publications

(5 citation statements)

References 36 publications

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“…(SWA). Potential SWA to known LST is higher compared to Landsat 7 ETM+ and SWA majority used for detection drought and Urban Heat Island (UHI) [26][27][28][29]. The SWA method used emphasizes the Fraction Vegetation Cover (FVC) [30] as an emissivity value to obtain a surface temperature value.…”

Section: Study Areamentioning

confidence: 99%

Thermal index effect in forest canopy density (FCD) methods based on remote sensing imagery

Adi Nugraha,

Windu Kurniawan

2024

Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet

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The objective of this study is to identify variations in the level of accuracy of the Forest Canopy Density (FCD) method's utilization of thermal index (TI). This is significant because the FCD approach can be used to determine vegetation cover without relying on the TI indicator. The four primary indicators used in the initial development of the FCD approach by Rikimaru et al. were the vegetation index, shadow index, soil index, and thermal index. The Split Windows Algorithm (SWA), which is the most effective for Landsat 8 OLI/TIRS imagery with a combination of bands 10 and 11, is utilized as the thermal index calculation method. SWA is obtained by concentrating on variations in the vegetation index value used to calculate surface emissivity. Hence, two types of FCD-SWA FCD and non-SWA FCD-are developed. The results showed that accuracy is obtained using the error matrix: the non-SWA FCD is 42% and the SWA FCD is 53%. In addition, the 1 x 1 test plot further show that SWA FCD tends to overestimate, while non-SWA FCD tends to underestimate. The overall accuracy of the analysis conditions may be impacted by the availability of additional samples and the occurrence of the COVID-19 incident. Based on this, that FCD with four indicators may be more accurate than FCD without TI. Despite the need to deep attention to the high-FCD class of analysis, which has a propensity to overestimate

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Section: Study Areamentioning

confidence: 99%

Thermal index effect in forest canopy density (FCD) methods based on remote sensing imagery

Adi Nugraha,

Windu Kurniawan

2024

Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet

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“…The TVDI measures a combined signal of both soil moisture under trees and the leaves water status [15]. The Landsat 8 and 9 satellite platform combines multispectral and thermal band information and comes with relatively high spatial resolutions, consistency and the free availability of the data by the U.S. Geological Survey (USGS), which made these platforms especially useful for calculating TVDI information [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Drought Monitoring and Prediction in Agriculture: Employing Earth Observation Data, Climate Scenarios and Data Driven Methods; a Case Study: Mango Orchard in Tamale, Ghana

Hobart,

Schirrmann,

Abubakari

et al. 2024

Remote Sensing

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The study focused on the prediction of the Temperature Vegetation Dryness Index (TVDI), an agricultural drought index, for a Mango orchard in Tamale, Ghana. It investigated the temporal relationship between the meteorological drought index, Standardized Precipitation Index (SPI), and TVDI. The SPI was calculated based on utilizing precipitation data from the World Meteorological Organization (WMO) database (2010–2022) and CMIP6 projected precipitation data (2023–2050) from 35 climate models representing various Shared Socioeconomic Pathway (SSP) climate change scenarios. Concurrently, TVDI was derived from Landsat 8/9 satellite imagery, validated using thermal data obtained from unmanned aerial vehicle (UAV) surveys. A comprehensive cross-correlation analysis between TVDI and SPI was conducted to identify lag times between these indices. Building on this temporal relationship, the TVDI was modeled as a function of SPI, with varying lag times as inputs to the Wavelet-Adaptive Neuro-Fuzzy Inference System (Wavelet-ANFIS). This innovative approach facilitated robust predictions of TVDI as an agricultural drought index, specifically relying on SPI as a predictor of meteorological drought occurrences for the years 2023–2050. The research outcome provides practical insights into the dynamic nature of drought conditions in the Tamale mango orchard region. The results indicate significant water stress projected for different time frames: 186 months for SSP126, 183 months for SSP245, and 179 months for both SSP370 and SSP585. This corresponds to a range of 55–57% of the projected months. These insights are crucial for formulating proactive and sustainable strategies for agricultural practices. For instance, implementing supplemental irrigation systems or crop adaptations can be effective measures. The anticipated outcomes contribute to a nuanced understanding of drought impacts, facilitating informed decision-making for agricultural planning and resource allocation.

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“…The single index technique keeps track of and assesses certain ecosystem components using data from remote sensing. For example, the vegetation index [21], the leaf area index [22], and net primary productivity [23] are used to monitor the vegetation coverage in the research area; urban heat island impact is assessed using land surface temperature [24]; and land surface moisture [25] and various drought indices [26] are used to assess drought conditions. However, the composition of ecosystems is diverse and complex, and the various components interact with each other.…”

Section: Introductionmentioning

confidence: 99%

Ecological Environment Quality Assessment of Arid Areas Based on Improved Remote Sensing Ecological Index—A Case Study of the Loess Plateau

Shi,

Lin,

Jing

et al. 2023

Sustainability

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Ecosystems in arid and semi-arid areas are delicate and prone to different erosive effects. Monitoring and evaluating the environmental ecological condition in such areas contribute to the governance and restoration of the ecosystem. Remote sensing ecological indices (RSEIs) are widely used as a method for environmental monitoring and have been extensively applied in various regions. This study selects the arid and semi-arid Loess Plateau as the research area, in response to existing research on ecological monitoring that predominantly uses vegetation indices as monitoring indicators for greenness factors. A fluorescence remote sensing ecological index (SRSEI) is constructed by using monthly synthesized sun-induced chlorophyll fluorescence data during the vegetation growth period as a new component for greenness and combining it with MODIS product data. The study generates the RSEI and SRSEI for the research area spanning from 2001 to 2021. The study compares and analyzes the differences between the two indices and explores the evolution patterns of the ecosystem quality in the Loess Plateau over a 21-year period. The results indicate consistent and positively correlated linear fitting trend changes in the RSEI and SRSEI for the research area between 2001 and 2021. The newly constructed ecological index exhibits a higher correlation with rainfall data, and it shows a more significant decrease in magnitude during drought occurrences, indicating a faster and stronger response of the new index to drought in the research area. The largest proportions are found in the research area’s regions with both substantial and minor improvements, pointing to an upward tendency in the Loess Plateau’s ecosystem development. The newly constructed environmental index can effectively evaluate the quality of the ecosystem in the research area.

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Modification of Temperature Vegetation Dryness Index (TVDI) Method for Detecting Drought with Multi-Scale Image

Cited by 6 publications

References 36 publications

Thermal index effect in forest canopy density (FCD) methods based on remote sensing imagery

Thermal index effect in forest canopy density (FCD) methods based on remote sensing imagery

Drought Monitoring and Prediction in Agriculture: Employing Earth Observation Data, Climate Scenarios and Data Driven Methods; a Case Study: Mango Orchard in Tamale, Ghana

Ecological Environment Quality Assessment of Arid Areas Based on Improved Remote Sensing Ecological Index—A Case Study of the Loess Plateau

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