Soil moisture index estimation from landsat 8 images for prediction and monitoring landslide occurrences in Ulu Kelang, Selangor, Malaysia

Tajudin, Noraisyah; Ya’acob, Norsuzila; Ali, Darmawaty Mohd; Adnan, Nor Aizam

doi:10.11591/ijece.v11i3.pp2101-2108

Cited by 9 publications

(6 citation statements)

References 24 publications

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“…With the MNDWI, water bodies (such as rivers or lakes) have positive values, and vegetation and soil are generally linked to zero or negative values (McFeeters, 1996). The SMI variable ratio is between 0 and 1 and indicates the relative soil moisture within the site with 0 being the lowest soil moisture and 1 being the highest on a specific day (Tajudin et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Prediction of pine mistletoe infection using remote sensing imaging: A comparison of the artificial neural network model and logistic regression model

Usta

Yılmaz

2022

Forest Pathology

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In this study, the prediction of pine mistletoe distribution in Scots pine ecosystems was explored using remote sensing variables to compare the multilayer perceptron (MLP) artificial neural network (ANN) and logistic regression (LR) model performances. For this purpose, 109 sample plots were distinguished in pure Scots pine forests (natural) in the Eastern Black Sea Region of Turkey. Distinguishing mistletoe-infected stands (69) and uninfected stands (40) was performed with field observations. The variables acquired from Landsat 8 (Level 1) images were used as independent variables for independent-sample t-test, MLP ANN and LR models. Remote sensing variables indicated that mistletoe-infected stands were in drier areas with a lower vegetation-leaf area index. Based on the performance results of both models, the sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and accuracy of the MLP ANN model were superior to those of the LR model. The prediction percentages (SEN, SPE, PPV and NPV) of mistletoe-infected stands were better than the prediction percentages of uninfected stands. The prediction accuracies of LR and MLP ANN models were 74.3% and 89.6%, respectively. However, all remote sensing variables were included in the prediction equation of the MLP ANN model, while the thermal infrared 1 (TIRS1) variable was included in the LR model. In the MLP ANN model, the TIRS1 variable also had the highest normalized importance (100%).The area under the curve (AUC) value for identifying the mistletoe-infected stands of Scots pine forests used by the MLP ANN model (0.892 ± 0.034) was higher than in the LR model (0.838 ± 0.039), explaining the more accurate predictions obtained from the MLP ANN model. The MLP ANN model showed much better performance than the LR model. The results of this study are expected to make important contributions to the identification of potential mistletoe-infected areas.

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Section: Discussionmentioning

confidence: 99%

Prediction of pine mistletoe infection using remote sensing imaging: A comparison of the artificial neural network model and logistic regression model

Usta

Yılmaz

2022

Forest Pathology

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“…Soil moisture index (SMI) data was based on the relationship between land surface temperature (LST) and the normalized vegetation index (NDVI). The number of equations in the analysis refers to Potic et al (2017) cited by Tajudin et al (2021), as follows:…”

Section: Discussionmentioning

confidence: 99%

Growth response of Dendrocalamus asper on elevational variation and intra-clump spacing management

2021

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Abstract. Growth response of Dendrocalamus asper on elevational variation and intra-clump spacing management. 2021. Title. Biodiversitas 22: 3801-3810. Dendrocalamus asper (Schult. Schult. F.) Backer Ex. K. Heyne is a well-known commodity classified as a non-timber forest product (NTFP) to substitute wood-based products in the future. While bamboo is widely distributed in various habitats, and it could impact growth performance and quality. Nevertheless, the development of bamboo research on the upstream level is quite rare, specifically for clumping bamboo species. Therefore, our study aimed to reveal the performance in elevational variation and to discover the intra-clump spacing and diameter relationship. The elevational variation was divided into three levels, which were lower, middle, and higher levels. Each elevation was established in 9 plots with parameters observed were culm diameter at breast height (DBH), culm height (H), and culm volume (V) of D. asper. The intra-clump spacing was used to assess the relationship between the clump density and diameter growth. The research is complemented with in-depth interviews to explore the traditional silvicultural practices of Dendrocalamus asper and spatial analysis to generate land surface temperature and soil moisture index. The result showed that there is no effect (P > 0.05) of elevational variation to D. asper growth and development, while the availability of intra-clump spacing showed a significant result (P < 0.05) on the culm DBH of D, asper. Furthermore, our result suggests a wider intra-clump spacing (0.4-0.6 m2) is more recommended than a narrow intra-clump spacing for optimal culm diameter growth. Additionally, bamboo plantation was still less managed and utilized. Therefore, improving the productivity of D. asper by maintaining plantation, i.e., fertilizing, managing spacing among clump bamboo, and harvesting to achieve sustainable development of the bamboo plantation is useful.

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“…Landslide is closely related to the physical and anthropologic condition [15]. The state represents factors that affected landslides are land surface temperature (LST) and soil moisture index (SMI) [36,37]. Both parameters are quickly drawn from satellite image analysis spatially and temporally, depending on the image availability of various providers.…”

Section: Introductionmentioning

confidence: 99%

Land surface temperature (LST) and soil moisture index (SMI) to identify slope stability

Putro

Arif

Sarastika

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Scientists widely use satellite images for scientific purposes, including investigation on earth science and environmental issues. Developing of many environmental models is due to replicating the natural process. Landslide is a known natural process controlled by slope stability which incorporates many parameters such as soil water content, morphology, and meteorological factor. Both LST and SMI were derived from satellite images, while SMI was the derivation of LST, meanwhile the use of both parameters in determining slope stability was rarely done. This research explores the use of LST and SMI in slope stability modeling. The LST analysis was calculated based on SEBAL (Surface Energy Balance Algorithms) using Landsat 8 imagery. The LST was then used to construct the SMI. Slope stability (FS) was calculated using the Selby model. All those variables were then cross-plotted in a regression to find the R2 value. The result shows a weak connection between FS-LST and FS-SMI with the R2 value of 9,09% and 8,16%. A stronger connection is only demonstrated in FS-Slope regression with a value of 70,98%. The weak R2 indicates that the model is not fit to calculate the FS of the Selby model. The LST and SMI were derived from satellite images and did not directly correspond to the soil characteristic as SMI was derived from LST and vegetation indices. Further empirical data collection needs to be used to build a better model on FS.

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Soil moisture index estimation from landsat 8 images for prediction and monitoring landslide occurrences in Ulu Kelang, Selangor, Malaysia

Cited by 9 publications

References 24 publications

Prediction of pine mistletoe infection using remote sensing imaging: A comparison of the artificial neural network model and logistic regression model

Prediction of pine mistletoe infection using remote sensing imaging: A comparison of the artificial neural network model and logistic regression model

Growth response of Dendrocalamus asper on elevational variation and intra-clump spacing management

Land surface temperature (LST) and soil moisture index (SMI) to identify slope stability

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