Impact of land use and land cover on urban ecosystem service value in Chandigarh, India: a GIS-based analysis

Sharma, Sheenu; Hussain, Sabir; Singh, Anita

doi:10.1093/jue/juac030

Cited by 15 publications

(6 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Carbon sequestration modelling faces limitations, including oversimpli cation of the carbon cycle and disregard for key biological factors (Sharma et al, 2023). Model validation against real data is essential to evaluate performance across diverse landscapes (Sharp et al, 2018).…”

Section: Strengths and Limitations Of The Studymentioning

confidence: 99%

Assessing Tradeoffs and Synergies between Land Use Land Cover Change and Ecosystem Services in River Ecosystem Using InVEST Model

Majumdar,

Avishek

2024

Preprint

View full text Add to dashboard Cite

Riverine ecosystems supply humans with a variety of ecosystem services (ESs), but anthropogenic activities endanger their availability worldwide. Understanding the spatiotemporal characteristics of riverine ESs and identifying the primary driving forces behind various ESs are crucial for preserving regional ecological security and achieving ecosystem sustainability. The study examines the spatio-temporal changes from 2000 to 2022 in the Danro River Basin in Jharkhand in four essential Ecosystem Services (ES): Sediment Delivery Ratio (SDR), Nutrient Delivery Ratio (NDR), Habitat Quality Monitoring (HQM) and Carbon Storage (CS), using InVEST model, Land Use Dynamics Index and Correspondence analysis. Danro River is a tributary of the Ganges River basin affected by sand mining. Key results were: (1) A rise in soil erosion was observed due to the transformation of agricultural land into urban areas; (2) The phosphorous and nitrogen retention was higher in agricultural land as compared to forest areas; (3) The habitat quality of the Danro River body showed degradation during 2000 to 2020; (4) The study area can sequester 2128304.92 Mg of Carbon; (5) The land use dynamic index (K) indicated that bare ground experienced the greatest impact, with a value of -0.021. The study uncovered complex relationships between ecosystem services and land use changes, emphasizing tradeoffs and synergies and laying stress on the holistic management strategies to balance tradeoffs and leverage synergies. The findings provide valuable insights for decision-making in socio-environmental processes. Other regions missing meteorological, hydrological, and geological data may also benefit from applying the InVEST model with localized parameters.

show abstract

Section: Strengths and Limitations Of The Studymentioning

confidence: 99%

Assessing Tradeoffs and Synergies between Land Use Land Cover Change and Ecosystem Services in River Ecosystem Using InVEST Model

Majumdar,

Avishek

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The geographical information systems technology was used to estimate and map the distribution, while the remote sensing helped to generate LULC categories identified in the study area, and then they were used as proxies for measurements. Important web-based technologies for spatial mapping are gradually being used to value ecological services [36,22]. Thus, this study employed the ESV coefficients of Costanza et al [3] to obtain the ESV for the LULC categories.…”

Section: Valuations Of Ecosystem Servicesmentioning

confidence: 99%

Impact of Land Use and Land Cover Changes on Ecosystem Services Value in Mwanza City, Tanzania

Kaganga,

Ngogo

2024

AJEE

View full text Add to dashboard Cite

Ecosystem services are vital services that support life and are the basis for human socio-economic progress. However, changes in land use and land cover (LULC) brought about by urban expansion degrade them. Thus, analysing the impact of land use and land cover (LULC) change on ecosystem service values (ESVs) is crucial for understanding and informing resource policy decisions. This study aims to analyse the impact of land use and land cover changes on ecosystem service values in Mwanza City, Tanzania. To achieve that, the benefits transfer approach was employed to analyse the changes in ESV in response to LULC. We estimated and analysed changes in ESV using satellite image datasets from 1999, 2009, and 2019. The LULC classes that were identified are vegetated land, agricultural land, waterbodies, built-up area, and bareland. The results exhibit that Mwanza City experienced significant LULC changes. While vegetated land, agricultural land, and bareland decreased by 49%, 15%, and 36%, respectively, the built-up area and water bodies increased by 568% and 48%, respectively, during the two decades. The total ESV decreased from 31.35 million US dollars to 26.3 million US dollars between 1999 and 2009 and to 23.96 million US dollars between 2009 and 2019. The waterbodies increased due to the increased volume of water in streams that expanded the floodplains, which resulted from surface runoff attributed to increased paved surfaces as more land was converted into a built-up environment upstream. The built-up area and bareland contributed nothing to ESV. However, the built-up area was the driving force behind the reduction of ESV in other LULC classes, as it was encroaching on them. The study concludes that the decrease in ESV reflects the degradation of ecosystem services due to the change in LULC. Hence, it is recommended that sustainable management of ecosystems be adhered for the proper functioning of the earth’s life-support system.

show abstract

“…In this study, six LULC classes were selected, i.e., built-up, cropland, fallow land, vegetation, open land, and waterbodies. These LULC classes are directly related to ecosystem services, planning, and management of the study area to support urban sustainability [63]. The selected LULC classes establish a baseline for the change analysis, the distribution and pattern of LULC classes in a city region [64].…”

Section: Selection Of Lulcmentioning

confidence: 99%

Transfer-Ensemble Learning: A Novel Approach for Mapping Urban Land Use/Cover of the Indian Metropolitans

Barman,

Mustak,

Kuffer

et al. 2023

Sustainability

View full text Add to dashboard Cite

Land use and land cover (LULC) classification plays a significant role in the analysis of climate change, evidence-based policies, and urban and regional planning. For example, updated and detailed information on land use in urban areas is highly needed to monitor and evaluate urban development plans. Machine learning (ML) algorithms, and particularly ensemble ML models support transferability and efficiency in mapping land uses. Generalization, model consistency, and efficiency are essential requirements for implementing such algorithms. The transfer-ensemble learning approach is increasingly used due to its efficiency. However, it is rarely investigated for mapping complex urban LULC in Global South cities, such as India. The main objective of this study is to assess the performance of machine and ensemble-transfer learning algorithms to map the LULC of two metropolitan cities of India using Landsat 5 TM, 2011, and DMSP-OLS nightlight, 2013. This study used classical ML algorithms, such as Support Vector Machine-Radial Basis Function (SVM-RBF), SVM-Linear, and Random Forest (RF). A total of 480 samples were collected to classify six LULC types. The samples were split into training and validation sets with a 65:35 ratio for the training, parameter tuning, and validation of the ML algorithms. The result shows that RF has the highest accuracy (94.43%) of individual models, as compared to SVM-RBF (85.07%) and SVM-Linear (91.99%). Overall, the ensemble model-4 produces the highest accuracy (94.84%) compared to other ensemble models for the Kolkata metropolitan area. In transfer learning, the pre-trained ensemble model-4 achieved the highest accuracy (80.75%) compared to other pre-trained ensemble models for Delhi. This study provides innovative guidelines for selecting a robust ML algorithm to map urban LULC at the metropolitan scale to support urban sustainability.

show abstract

Impact of land use and land cover on urban ecosystem service value in Chandigarh, India: a GIS-based analysis

Cited by 15 publications

References 60 publications

Assessing Tradeoffs and Synergies between Land Use Land Cover Change and Ecosystem Services in River Ecosystem Using InVEST Model

Assessing Tradeoffs and Synergies between Land Use Land Cover Change and Ecosystem Services in River Ecosystem Using InVEST Model

Impact of Land Use and Land Cover Changes on Ecosystem Services Value in Mwanza City, Tanzania

Transfer-Ensemble Learning: A Novel Approach for Mapping Urban Land Use/Cover of the Indian Metropolitans

Contact Info

Product

Resources

About