Dynamics of ecosystem services (ESs) in response to land use land cover (LU/LC) changes in the lower Gangetic plain of India

Talukdar, Swapan; Singha, Pankaj; Shahfahad, .; Mahato, Susanta; Praveen, Bushra; Rahman, Atiqur

doi:10.1016/j.ecolind.2020.106121

Cited by 159 publications

(53 citation statements)

References 68 publications

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“…Knowledge of land-use/land-cover (LULC) change is essential in a number of fields based on the use of Earth observations, such as urban and regional planning [1,2], environmental vulnerability and impact assessment [3][4][5][6][7], natural disasters and hazards monitoring [8][9][10][11][12][13] and estimation of soil erosion and salinity, etc. [14][15][16][17]. Quantitative assessment and prediction of LULC dynamics are the most efficient means to manage and understand the landscape transformation [18].…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, assessment in LULC change is inevitably required for a variety of purposes for the welfare of human beings in the context of rapid and uncontrolled population growth along with economic and industrial development, especially in developing countries with intensified LULC changes [20][21][22][23]. These changes have a series of impacts on both human society and environment in many ways like increasing flood and drought vulnerability, environmental degradation, loss of ecosystem services, groundwater depletion, landslide hazards, soil erosion and others [14,15,[24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

“…Over the last decade, more advanced methods, such as artificial neural networks (ANN), SVM, RF, decision tree, and other models, have gained exceptional attention in remote sensing-based applications, such as LULC classification. Thus, numerous studies on the LULC modelling have been carried out using different machine-learning algorithms [14,[48][49][50][51] as well as comparing the machine-learning algorithms [52][53][54]. Furthermore, a few studies have been carried out to identify the best suited and accurate algorithm among used machine-learning classifiers for LULC mapping [52][53][54][55].…”

Section: Introductionmentioning

confidence: 99%

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Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review

et al. 2020

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Rapid and uncontrolled population growth along with economic and industrial development, especially in developing countries during the late twentieth and early twenty-first centuries, have increased the rate of land-use/land-cover (LULC) change many times. Since quantitative assessment of changes in LULC is one of the most efficient means to understand and manage the land transformation, there is a need to examine the accuracy of different algorithms for LULC mapping in order to identify the best classifier for further applications of earth observations. In this article, six machine-learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), fuzzy adaptive resonance theory-supervised predictive mapping (Fuzzy ARTMAP), spectral angle mapper (SAM) and Mahalanobis distance (MD) were examined. Accuracy assessment was performed by using Kappa coefficient, receiver operational curve (RoC), index-based validation and root mean square error (RMSE). Results of Kappa coefficient show that all the classifiers have a similar accuracy level with minor variation, but the RF algorithm has the highest accuracy of 0.89 and the MD algorithm (parametric classifier) has the least accuracy of 0.82. In addition, the index-based LULC and visual cross-validation show that the RF algorithm (correlations between RF and normalised differentiation water index, normalised differentiation vegetation index and normalised differentiation built-up index are 0.96, 0.99 and 1, respectively, at 0.05 level of significance) has the highest accuracy level in comparison to the other classifiers adopted. Findings from the literature also proved that ANN and RF algorithms are the best LULC classifiers, although a non-parametric classifier like SAM (Kappa coefficient 0.84; area under curve (AUC) 0.85) has a better and consistent accuracy level than the other machine-learning algorithms. Finally, this review concludes that the RF algorithm is the best machine-learning LULC classifier, among the six examined algorithms although it is necessary to further test the RF algorithm in different morphoclimatic conditions in the future.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review

et al. 2020

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“…Therefore, the rate of changes in the ESVs over time serves as an important indicator to assess the possible degradation of natural cover 27 . In this regard, the study of Talukdar et al 28 in this region successfully established the positive relationship between the loss of wetland area and loss of ESVs. Further, the perception survey study on the Chatra Wetland by Das and Basu 24 also showed that the loss of ecosystem services suggests the degradation of the wetland.…”

Section: Introductionmentioning

confidence: 86%

“…Compact natural cover with no significant loss in natural cover will deliver steady ESVs over time. Previous studies showed that the conversion of the natural cover into anthropogenic land uses led to negative changes in the overall ecosystem service values (ESVs) 28 , 93 .…”

Section: Materials and Methodologymentioning

confidence: 99%

Development of an integrated peri-urban wetland degradation assessment approach for the Chatra Wetland in eastern India

Basu

Pham

Al‐Ansari

et al. 2021

Sci Rep

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The loss of peri-urban wetlands is a major side effect of urbanization in India in recent days. Timely and proper assessment of wetland area change is essential for the conservation of wetlands. This study follows the integrated way of the peri-urban wetland degradation assessment in the case of medium and small-size urban agglomerations with a special focus on Chatra Wetland. Analysis of land-use and land cover (LULC) maps of the past 28 years shows a decrease of 60% area of the wetland including marshy land. This has reduced the ecosystem services value by about 71.90% over the period 1991–2018. From this end, The Land Change Modeler of IDRISI TerrSet using the combination of MLPNN and Markov Chain has been used to predict the LULC map of this region. The scenario-based modeling following the LULC conversion and nine explanatory variables suggests the complete loss of this wetland by 2045. However, the authors have also tried to present a future LULC pattern of this region based on an environmental perspective. This proposed map suggests possible areas for built-up expansion on the western side of the city without significantly affecting the environment.

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Impacts of ecological programs on land use and ecosystem services since the 1980s: A case‐study of a typical catchment on the Loess Plateau, China

Fang

et al. 2022

Land Degrad Dev

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China' Loess Plateau, which was once dominated by sloping fields with poor farming conditions and serious soil erosion, has experienced a series of ecological programs. This article sought to explore the impacts of ecological programs on land use and ecosystem services in a typical catchment of China's Loess Plateau from 1984 to 2017, and thus understand what needs to be improved for future ecological programs. The findings showed that: (a) Due to the implementation of various ecological programs, the land use structure was optimized, and vegetation coverage was significantly increased, especially with the implement of the Grain for Green (GFG) Program. (b) The change of the total ecosystem service value (ESV) presented a Ushaped curve. The total ESV decreased under the Comprehensive Management of Small Catchments Program, and increased significantly since the implementation of the GFG. The rapid increase stage can be traced to the effects of the Gully Land Consolidation Program, reflecting the time lag effect of ecological programs on the improvement of ecosystem services. (c) The various ecosystem services were improved significantly, except for the food provisioning value (FPV). The abandonment of croplands without consolidation determined the decline of the FPV, implying the necessity for a land-use efficiency boost. Our research proposed to build up an actor network to integrate local governments, rural cadres, villagers, enterprises, and natural resources, especially land resources, into ecological programs to ensure ecological restoration and activate the factors of production, thus securing the sustainable development of China' Loess Plateau.

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Dynamics of ecosystem services (ESs) in response to land use land cover (LU/LC) changes in the lower Gangetic plain of India

Cited by 159 publications

References 68 publications

Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review

Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review

Development of an integrated peri-urban wetland degradation assessment approach for the Chatra Wetland in eastern India

Impacts of ecological programs on land use and ecosystem services since the 1980s: A case‐study of a typical catchment on the Loess Plateau, China

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