Environmental Engineering
I. Ridwan; S. Kadir; N. Nurlina
Abstract
BACKGROUND AND OBJECTIVES: The condition of the watershed area, particularly the Tabunio Watershed, is one with priority treatment due to the condition of the land where it is located, which qualifies for the “very high recovery” category with a critical land area of 19,109.89 hectare. Moreover, ...
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BACKGROUND AND OBJECTIVES: The condition of the watershed area, particularly the Tabunio Watershed, is one with priority treatment due to the condition of the land where it is located, which qualifies for the “very high recovery” category with a critical land area of 19,109.89 hectare. Moreover, the diminishing water absorption also results in flooding during the rainy season and drought in the dry season. Environmental damage in the Tabunio Watershed is exacerbated by the existence of traditional gold mining and has become a concern for many parties. With this in mind, the perceived increase in natural disasters, such as floods, landslides, and droughts from year to year requires an evaluation of land degradation in the Tabunio Watershed.METHODS: The objective of this study was to monitor and simulate the spatial and temporal aspects of land degradation in the Tabunio Watershed. It was suggested that a complete land degradation index be developed to capture the spatial and temporal aspects of land degradation between the years 2005 and 2020. This index integrates land use land cover, vegetation coverage, soil erosion, and soil moisture content.FINDINGS: The proposed comprehensive land degradation index in this study demonstrated that (a) the land degradation index, which successfully monitored the spatio-temporal aspect of land degradation (kappa coefficient > 0.73 and overall accuracy > 86 percent), is regarded as having high accuracy. (b) In comparison to the individual indices, the land degradation index is capable of revealing land degradation in a more comprehensive manner. (c) land degradation index is readily transferable and applicable to other study areas due to the fact that all of its land degradation indices can be quickly extracted from remotely sensed imagery. (d) land degradation index can be used in a wide variety of contexts, which also accounts for the provision of quantitative predictions with regard to the possibility of land degradation. (e) The rate of land degradation will generally increase from 2005 to 2020, with 2010 being the most extreme year.CONCLUSION: The proposed comprehensive land degradation index method is capable of describing the spatial and temporal aspect of land degradation from 2005 to 2020 in the watershed area. Moreover, the proposed approach shows that the level of land degradation from 2005 to 2020 normally increases, recording the extreme years as the 2010s. In addition, in most years, the amount of land degradation was moderate, only few of which had severe or extreme degradation. As a consequence of this, some land degradation management measures ought to be created in advance, guaranteeing the protection of this vital region, which is a source of freshwater. The study provides a substantial understanding of the effect of land degradation on sustainable environment management and development in the watershed.
Environmental Management
C. Loukrakpam; B. Oinam
Abstract
BACKGROUND AND OBJECTIVE: Soil erosion is considered one of the major indicators of soil degradation in our environment. Extensive soil erosion process leads to erosion of nutrients in the topsoil and decreases in fertility and hence productivity. Moreover, creeping erosion leads to landslides in the ...
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BACKGROUND AND OBJECTIVE: Soil erosion is considered one of the major indicators of soil degradation in our environment. Extensive soil erosion process leads to erosion of nutrients in the topsoil and decreases in fertility and hence productivity. Moreover, creeping erosion leads to landslides in the hilly regions of the study area that affects the socio-economics of the inhabitants. The current study focuses on the estimation of soil erosion rate for the year 2011 to 2019 and projection for the years 2021, 2023 and 2025.METHODS: In this study, the Revised Universal Soil Loss Equation is used for estimation of soil erosion in the study area for the year 2011 to 2019. Using Artificial Neural Network-based Cellular Automata simulation, the Land Use Land Cover is projected for the future years 2021, 2023 and 2025. Using the projected layer as one of the spatial variables and applying the same model, Soil Erosion based on Revised Universal soil loss equation is projected for a corresponding years.FINDINGS: For both cases of projection, simulated layers of 2019 (land use land cover and soil erosion) are correlated with the estimated layer of 2019 using actual variables and validated. The agreement and accuracy of the model used in the case land use are 0.92 and 96.21% for the year 2019. The coefficient of determination of the model for both simulations is also observed to be 0.875 and 0.838. The simulated future soil erosion rate ranges from minimum of 0 t/ha/y to maximum of 524.271 t/ha/y, 1160.212 t/ha/y and 783.135 t/ha/y in the year 2021, 2023 and 2025, respectively.CONCLUSION: The study has emphasized the use of artificial neural network-based Cellular automata model for simulation of land use and land cover and subsequently estimation of soil erosion rate. With the simulation of future soil erosion rate, the study describes the trend in the erosion rate from past to future, passing through present scenario. With the scarcity of data, the methodology is found to be accurate and reliable for the region under study.
