Spatiotemporal narratives of peri-urban land use dynamics and its political drivers: A geo-spatial mixed methods approach

Climate change and land use land cover (LULC) changes are recognised as two of the most significant causes of environmental change. Climate change and LULC changes are related to one another. Land use change may drive climate change, and a changing climate may result in land cover changes. Climate change and LULC changes are believed to influence soil erosion. This chapter analyses the impacts of climate and LULC changes on soil erosion. The causes and effects of climate change on precipitation, temperature, solar radiation, atmospheric CO2 concentrations, and radiative forcing are discussed. The chapter includes the impacts of climate change on soil characteristics, vegetation cover, runoff, floods, and droughts and extends the impacts of these changes on water and wind erosion. The chapter explores the human alterations of LULC changes in terms of changes in the forest cover, alterations in agricultural lands, increase in urban areas, and decrease in wetland areas. The influence of the LULC changes on soil erosion and sediment production processes is discussed. Also, the combined impact of climate and LULC changes on soil erosion is explored, and mitigation strategies like sustainable land management practices and appropriate policy incentives to conserve soil are discussed.

Public participation in public decision-making is of great significance for speeding up the transformation of government functions and embodying people's ownership.Therefore, in the process of local decision-making, expanding public participation and attaching importance to the important value of public participation are of far-reaching significance to the formulation of local decision-making in China.For various reasons, the role of public participation in local government decision-making has not been well reflected.To achieve effective public participation in local government decision-making, we need to improve the quality of public political participation and cultivate good political participants; we need to improve the public participation mechanism in government public decision-making, expand the channels for public participation in decision-making; strengthen the guidance and supervision of the media, and purify the media environment for public participation in government decision-making.

In rapidly growing areas, land use land cover (LULC) change is one of the most pre-eminent features of environmental changes produced by human-induced activities. LULC changes are critical issues and challenges for environmentally friendly and sustainable development. Understanding land-use and land-cover (LULC) changing patterns is critical for sustainable environmental management, particularly effective water management. This study was focused on the assessment of LULC and sinuosity of the Seti River sub-Basin over 28 years. Satellite imagery of Landsat series (MS, TM, and OLI) were classified using maximum likelihood classifier to create LULC maps for 1991, 2004 and 2019. The LULC change was assessed using change detection analysis and verified the result by confuse matrix. The results showed that forest cover is regaining its original status with the increasing rate of 1.31%. In the meantime, built-up areas are expanding with the rate of 2.62% while agricultural land has decreased with the rate of -1.89% per year and are more converted to built-up area. Trendofsinuosityindexfoundincreasing and varying in different sections of the river path indicated the complex response of changing characteristics of river flow, river mining and geomorphology of landscape. Based on research findings and descriptions from earlier works, river morphology is affected by both natural (topography, climate, precipitation), and anthropogenic (rapid urbanization, foreign labor migration, abandonment of cultivable land, community forest programs, development activities) factors.

Spatio-temporal variations of the LUCC of the Djoudj National Bird Sanctuary in the past 40 years and its sustainable development

With the recent advances in earth observation technologies, the increasing availability of data from more and more different satellite sensors as well as progress in semi-automated and automated classification techniques enable the (semi-) automated remote monitoring and analysis of large areas. Online platforms such as Google Earth Engine (GEE) bring data-driven techniques to the desktops of researchers while changing workflows and making excessive data downloads redundant. We present a study that utilizes machine learning algorithms on the GEE cloud computing platform for land use/land cover (LULC) mapping and change detection analysis using a Landsat satellite image time series. We applied different machine learning techniques to data from an environmentally sensitive area in Northern Iran. We tested their efficiency for LULC mapping and change detection analysis using the support vector machine (SVM), random forest (RF) and classification and regression tree (CART). We obtained LULC maps for the years 2000, 2005, 2010, 2015 and 2020. Training data was collected from field operations and historical datasets, and the respective LULC maps were validated using ground control points. In addition, we validated the reliability of the results through a spatial uncertainty analysis using Dempster-Shafer Theory (DST). The resulting accuracies of the classification outcomes varied significantly. SVM performed best with accuracies of 90.25%, 91.84%, 89.02%, 93.35% and 95.65% for 2000, 2005, 2010, 2015 and 2020, respectively. The spatial uncertainty analysis also validated the efficiency of SVM compared to RF and CART. The results confirm the potential of machine learning techniques for time series LULC mapping on the GEE platform while lowering the barriers to analyzing large amounts of satellite data. The results are also critical for decision-makers and authorities for analyzing the LULC changes and developing the respective environmental protection and polices in Northern Iran.

Public participation is the tool of the government to gather citizens or customers’ information in order to increase performance and respond their needs and expectations. Public participation in decision-making processes enhances citizen’s income, security, and self-esteem. This paper identifies the concepts and levels of public participation in decision-making processes. In addition, this paper presents participation tools used by government to facilitate citizen involvement. Thus, increasing public participation in government decision-making has become a large component for government administration especially in early stage before major decisions are made.

Land use land cover change (LULC) has become part of the global science agenda and the understanding of LULC change is vital for planning sustainable management of natural resources. The study has employed multi- temporal satellite imagery to examine the LULC change in the Abbottabad District from 1989 to 2019. Images from Landsat-5, Landsat-7, and Landsat-8 Thematic Mapper (TM) for the same season were acquired from the USGS for the years of 1989, 1999, 2009 and 2019. The images were pre-processed by atmospheric correction, extraction of the study area and band composite. The supervised image classification using Maximum Likelihood Classifier and accuracy assessment were applied to prepare LULC maps of the Abbottabad District. In the last three decades, the study area witnessed number of changes in the pattern of LULC due to population growth, rapid urbanization and increased development of infrastructure, which cumulatively led to the emergence of new patterns being employed for land use. Results of the analysis involving the classified maps show that agricultural land and bare land have decreased, respectively 15.73% and 3.81%, whereas water resources have decreased significantly by 0.58%. This study reveals that GIS can be used as an informative tool to detect LULC changes. However, for planning and management, as well as to gain better insight into the human dynamics of environmental variations on the regional scale, it is crucial to have information about temporal LULC transformation patterns in the study area.

Background/Objectives: Land Use Land Cover (LULC) is an important factor in monitoring the land use area of a region. This study aims to examine LULC change in Karbi Anglong District of Assam. Method: The study is conducted using data from remote sensing, geographic context, and field research findings. The study used Google Earth Pro Satellite Images using Arc GIS 10.5 software to map the LULC change detection for the years 2013–2023. Six primary categories of land use and land cover classification are used to categorize the research area: built-up areas, agricultural land, ginger cultivated area, and forest, wasteland, and water bodies. Findings: The results reveal a 30.31 sq. km increase in built-up areas, a 26.69 sq. km increase in agricultural land, and a significant 51.69 sq. km rise in ginger-cultivated area. Conversely, forest cover declined by 108.56 sq. km and water bodies by 4.03 sq. km. The increase in the wasteland, agricultural, ginger-cultivated land and built-up areas are evident in the outcome. Conversely, there is a decline in water and forest areas. Novelty: This study offers a detailed micro-level temporal analysis of LULC using field-verified remote sensing data and provides a baseline for future environmental planning in Karbi Anglong District of Assam. LULC is crucial because inappropriate land use can have negative impact on the environment as well as in the society and economy. Keywords: Remote sensing, GIS, LULC, Google Earth, Ginger Cultivation

Land use / Land cover change is one of the most sensitive factors that show the interactions between human activities and the ecological environment. This research study demonstrated the importance of geographical information system and remote sensing technologies in spatial temporal data analysis and also this paper shows a GIS and remote sensing approach for modeling of spatial - temporal pattern of land use and land cover change (LULC) in a fastest growing towns / industrial region of Karur town. QGIS 3.10 version and Arc GIS 10.2 software platforms were utilized in the study for Image processing, LULC mapping and change detection analysis. USGS Earth explorer Landsat series satellite imageries were acquired and LULC maps were prepared for the years 1991, 2000, 2010 and 2020. Supervised classification with maximum likelihood algorithm is adopted for LULC classification. The LULC classes are Built upland, Agricultural land, Barren land and Water body based on NRSA Level – I supervised classification. The Built-up area has drastically increased from 1991 to 2020. It has increased more than double. It was 17 percent in 1991 and increased to 40 percent in 2020. This clearly shows Karur town is the becoming more and more urbanized.

Evaluation of ecosystem services vis-à-vis perceptions and attitudes of local communities towards Wetland conservation in Kashmir Himalaya

Quantitative assessment of Land use/land cover changes in a developing region using machine learning algorithms: A case study in the Kurdistan Region, Iraq

Dynamicity of the channel is the main characteristic of the Kaljani River in the Himalayan foothill. The present work intends to document the historical changes in the land use and land cover (LULC) pattern driven by channel migration during 1987–2020 at the Kaljani River adjacent village area. In this study, the sinuosity index, the radius of curvature, meander wavelength, amplitude, meander width, channel width, arc angle, direction angle, rate of channel migration, and direction of migration have been calculated for the years of 1987, 2004, and 2020. The historical positions of both bankline and dynamic channel width and meander width indicate that a large portion of the floodplain area depict an erosion-accretion sequence with time. This work also investigated LULC changes in the Kaljani River adjacent village area using supervised image classification with an overall accuracy ranging between 85 and 89%. This research has demonstrated the application and capability of RS and GIS technology and generated a detailed evaluation of temporal and spatial changes in river channel processes and adjustment of LULC types. The LULC results revealed that the water bodies and dense forest are decreased and sandy area and built-up areas are increased. The LULC changes by the direct effect of bankline migration have a bad impact on the dwellers of the floodplain adjacent village area of the Kaljani River. The results of this study can represent an important indicator of the vulnerability of the Kaljani River adjacent village area and also provide information about geomorphological instabilities of the study area.KeywordsAlluvial channelChannel migrationChannel widthSinuosityRadius of curvatureLULC changes

This paper is a critical review, which synthesizes the theory-application linkage of peri-urban land use and land cover changes (LULCC) using the Bosomtwe District in the Ashanti Region of Ghana as the case. From abstractive thinking to empirical possibility, we conjecture human decisions within agent-based modeling (ABM) perspective. The key question the paper has tried to answer is: what are the probable future land use conversion and modification potentials in the district? LULCC in peri-urban areas respond to social and biophysical dynamics. These control spatial distribution of populations, infrastructure, and the space economy. Under systemic laxity of controls, peri-urban land uses deviate from effective land use plans.

Forest plays a vital role in carbon sequestration and climate regulation. A crucial tool for managing forest, particularly in protected regions, is keeping track of how the land cover changes in natural places. Using geospatial approaches, such as remote sensing and geographic information system (GIS), the present study has revealed spatio-temporal changes in land use categories and forest cover in the Chandaka National Park of Odisha, India, throughout the period of 1980-2020. The Landsat, LISS III and Sentinel satellite images of the year 1980, 2000 and 2020 were utilized respectively to map five land use land cover categories i.e. deciduous broadleaf forest, crop land, mixed forest, scrub land and water bodies in this preserved area. The satellite images were classified using a Supervised Classification method using Maximum Likelihood algorithm and ground control points (GCPs) were used for the spatial statistical analyses. The overall accuracies of the classification method in land cover categories in year 1980, 2000 and 2020 were 90.45%, 92.76% and 94.68%, respectively. Elsewhere, in order to study land use land cover (LULC) change and loss in forest of the Chandaka National Park, LULC classification, per-pixel scales post classification and self-knowledge on the LULC change process were used. The LULC change detection results showed that deciduous broadleaf forest decreased from 179.01 sq. km (76.66%) in 1980 to 132.75 sq. km (56.85%) in 2020, while mixed forest cover increased by 8.17 sq. km (3.50%) in 1980 to 38.84 sq. km (16.63%) in 2020. Crop land, Scrub land and Water bodies were also increased by 3.34%, 3.27% and 0.07%, respectively. Two significant change processes in the area are the logging activities in several places for timber and the conversion of natural forests with plantation. Agriculture expansion in the forest’s periphery is linked to the dramatic decline in forest cover change. The decline in forest cover is also a result of the production of charcoal and lumber exploitation. Overall, our findings indicated that more public awareness and participatory forest management are necessary to preserve Chandaka National Park. This study highlights the use of geospatial technologies in understanding the changes in LULC in the Chandaka National Park.

The changes that occur in various forms of land classes are known as land-use and land-cover changes (LULC). Such changes have a substantial impact on the contours of an urban basin and consequently have an effect on the surface runoff of the rainfall that occurs in the area. The runoff features obviously deteriorate because of the decrease in initial abstraction, and an increase in imperviousness causes enhanced runoff. The particular challenges in the urban basin are specificity calculation and quantification of surface runoff. Geospatial techniques are adopted along with the Soil Conservation Service-curve number (SCS-CN) technique to reliably predict and accurately measure runoff. The GIS is used to prepare the transformed layers of land classes from remotely sensed data. In this explorative study, LULC change and its impact on the urban Koraiyar basin, Tiruchirappalli city, South India, is studied by using SCS-CN with the aid of GIS techniques. As the basin that passes is located in a developing city, rapid changes in LULC are observed in and around the periphery of the basin. The LULC changes and their impact on surface runoff are analyzed using GIS with multi-dated Landsat satellite images for the years 1986–2016 at intervals of every 10 years. The supervised classification algorithm is used to develop LULC maps. From the study, it is observed that there is a continuous increase in settlement area of 1.04% from 1986 to 2016, especially in the northern part of the basin. A Markov model analysis is done from the historically developed LULC maps to predict the anticipated future LULC changes for the years 2026 and 2036 along with an estimation of the surface runoff. The predicted maps show that there is likely to be an increase in settlement area of 1.04% and decrease in 12.29% of agricultural land. The various thematic layers like slope, soil and curve number (CN) maps are also prepared using GIS. The composite CNs are generated for various land classes in the basin from 1986 to 2036. The increase in CN from 72 to 74.76 and their influence on runoff are studied. Finally, the study attempts to reliably estimate the present and future LULC status and its effect on surface runoff in the Koraiyar river basin.