LU/LC Change Detection and Forest Degradation Analysis in Dalma Wildlife Sanctuary Using 3S Technology: A Case Study in Jamshedpur-India

Ranjan, Avinash Kumar; Anand, Akash; Vallisree, S; Singh, Rahul Kumar

doi:10.3934/geosci.2016.4.273

Cited by 35 publications

(7 citation statements)

References 4 publications

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“…Figure 4 Deep learning approach on GIS [15] Spatial patterns and trends analysis finds applications across a wide range of domains, including urban planning, environmental management, public health, and natural resource conservation. In urban planning, spatial patterns analysis is used to identify urban growth trends, assess land use changes, and plan for sustainable development.…”

Section: Spatial Patterns and Trendsmentioning

confidence: 99%

“…It involves analyzing how changes in spatial inputs, such as elevation, land cover, or climate variables, affect model predictions and spatial patterns. Spatial sensitivity analysis techniques, such as spatial variance-based sensitivity analysis and geographically weighted regression, account for the spatial autocorrelation and dependency inherent in spatial data and provide localized measures of sensitivity across geographic space [15]. Sensitivity analysis finds applications across various domains of GIS, including environmental modeling, land use planning, hydrological modeling, and natural resource management.…”

Section: Sensitivity Analysismentioning

confidence: 99%

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Machine learning empowered geographic information systems: Advancing Spatial analysis and decision making

Partha Protim Roy,

Md. Shahriar Abdullah,

Iqtiar Md. Siddique

2024

World J. Adv. Res. Rev.

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In this era of technological innovation, the integration of machine learning techniques with Geographic Information Systems (GIS) has emerged as a transformative approach to spatial analysis and decision-making. This abstract explores the synergy between machine learning and GIS, highlighting their combined potential to unlock new insights from spatial data, automate analytical processes, and enhance predictive modeling capabilities. By leveraging machine learning algorithms such as neural networks, random forests, and support vector machines, GIS practitioners can address complex spatial challenges more effectively, ranging from land cover classification and urban growth modeling to environmental monitoring and disaster response. Through case studies and examples, this abstract demonstrates the practical applications of machine learning in GIS, illustrating its role in advancing our understanding of spatial phenomena and informing evidence-based decision-making across diverse domains. As the field continues to evolve, embracing the fusion of machine learning and GIS holds immense promise for unlocking the full potential of spatial analysis and shaping a more sustainable and resilient future. Illustrative case studies and examples showcase the practical applications of machine learning in GIS across diverse domains. From land cover classification and urban growth modeling to environmental monitoring and disaster response, machine learning algorithms offer versatile solutions to address a wide spectrum of spatial challenges. Through the fusion of machine learning and GIS, researchers and practitioners gain unprecedented insights into complex spatial phenomena, enabling them to make data-driven decisions that are both informed and actionable. Looking ahead, the fusion of machine learning and GIS holds immense promise for advancing our understanding of spatial dynamics and shaping a more sustainable and resilient future. As the field continues to evolve, embracing this interdisciplinary approach is essential for unlocking the full potential of spatial analysis, fostering innovation, and addressing pressing societal challenges at local, regional, and global scales. By leveraging the synergies between machine learning and GIS, we can chart a path towards a more data-driven, informed, and equitable world.

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Section: Spatial Patterns and Trendsmentioning

confidence: 99%

Section: Sensitivity Analysismentioning

confidence: 99%

Machine learning empowered geographic information systems: Advancing Spatial analysis and decision making

Partha Protim Roy,

Md. Shahriar Abdullah,

Iqtiar Md. Siddique

2024

World J. Adv. Res. Rev.

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“…While pixel-based directly focus to one single image objects. In recent research, many studies utilized these technologies such as OBIA in change detection at different scales, with both low and high resolution satellites sensor such as landsat data (Dutta, Reddy, Sharma, & Jha, 2016;Waiyasusri et al, 2016;Balaji, Geetha, & Soman, 2016;Ranjan et al, 2016) and data like ALOS (AVNIR2) was used for land use changes (Munthali & Murayama, 2011) and the OBIA approach the same of work by Zhang et al (2017) maintained OBIA approach for change analysis in florida everlades water conservation area using landsat data. The object-based image analysis showed the expansion/reduction of land use types when applied the classified image in change detection algorithms as done by Son et al (2015).…”

Section: Dealing With Cloud and Image Patchingmentioning

confidence: 99%

Monitoring and Predicting Land Use-Land Cover (Lulc) Changes Within and Around Krau Wildlife Reserve (Kwr) Protected Area in Malaysia Using Multi-Temporal Landsat Data

Gambo

Shafri

Shaharum

et al. 2018

Geoplanning: Journal of Geomatics and Planning

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Natural and anthropogenic activities surrounding a Protected Area (PA) may cause its natural area to change in terms of Land Use-Land Cover (LULC). Thus, there is need of environmental change monitoring within and around PA because of its significant values to ecosystem at conservation scales. Effects and influences of local community within and around PA turn into the major problems for natural resource and conservations management as well as environmental impact assessment. Ascertaining the complex interface in relations to changes and its driving factors over period of time within and around PA is significant in order to predict future LULC changes, build alternative scenarios and serve as tools for decision making. The main objective of this work was to evaluate temporal change detection and prediction of LULC as well as the trends of changes from 1989 to 2016 within and around Krau Wildlife Reserve (KWR). The cloud issues were mitigated by producing cloud free image and object-based image analysis (OBIA) was adopted after a comparison with pixel-based analysis for overall accuracy and kappa statistics. The comparison of classified maps had produced a satisfactory results of overall accuracies of 91%, 86% and 90% for 1989, 2004 and 2016 respectively. The natural/dense forest between periods of 1989-2016 was decreased whereas built-up and agricultural/sparse forest were increased. The simulation model of Land Change Modeler (LCM) was utilized with digital elevation model (DEM) and past LULC maps to project future LULC pattern using Markov chain. The predicted map trend showed an increase of dense forest converted to agricultural/sparse forest in the north-western, and urban/built-up in east-southern part of KWR. The study is important for the conservation of habitat species and monitoring the current status of the KWR

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“…It has an ability to classify large datasets since it considers dimensionality reduction procedures and also handles missing and outlier values [33]. This classifier's performance is comparable to that of other classifiers like Support Vector Machine and is better than many other classifiers like Maximum Likelihood Classifier [34] and Artificial Neural Network [35,36].…”

Section: Introductionmentioning

confidence: 99%

An Integrated Spatiotemporal Pattern Analysis Model to Assess and Predict the Degradation of Protected Forest Areas

Malhi

Asundi

Srivastava

et al. 2020

IJGI

Self Cite

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Forest degradation is considered to be one of the major threats to forests over the globe, which has considerably increased in recent decades. Forests are gradually getting fragmented and facing biodiversity losses because of climate change and anthropogenic activities. Future prediction of forest degradation spatiotemporal dynamics and fragmentation is imperative for generating a framework that can aid in prioritizing forest conservation and sustainable management practices. In this study, a random forest algorithm was developed and applied to a series of Landsat images of 1998, 2008, and 2018, to delineate spatiotemporal forest cover status in the sanctuary, along with the predictive model viz. the Cellular Automata Markov Chain for simulating a 2028 forest cover scenario in Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, India. The model’s predicting ability was assessed using a series of accuracy indices. Moreover, spatial pattern analysis—with the use of FRAGSTATS 4.2 software—was applied to the generated and predicted forest cover classes, to determine forest fragmentation in SWS. Change detection analysis showed an overall decrease in dense forest and a subsequent increase in the open and degraded forests. Several fragmentation metrics were quantified at patch, class, and landscape level, which showed trends reflecting a decrease in fragmentation in forest areas of SWS for the period 1998 to 2028. The improvement in SWS can be attributed to the enhanced forest management activities led by the government, for the protection and conservation of the sanctuary. To our knowledge, the present study is one of the few focusing on exploring and demonstrating the added value of the synergistic use of the Cellular Automata Markov Chain Model Coupled with Fragmentation Statistics in forest degradation analysis and prediction.

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LU/LC Change Detection and Forest Degradation Analysis in Dalma Wildlife Sanctuary Using 3S Technology: A Case Study in Jamshedpur-India

Cited by 35 publications

References 4 publications

Machine learning empowered geographic information systems: Advancing Spatial analysis and decision making

Machine learning empowered geographic information systems: Advancing Spatial analysis and decision making

Monitoring and Predicting Land Use-Land Cover (Lulc) Changes Within and Around Krau Wildlife Reserve (Kwr) Protected Area in Malaysia Using Multi-Temporal Landsat Data

An Integrated Spatiotemporal Pattern Analysis Model to Assess and Predict the Degradation of Protected Forest Areas

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