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Bioenergy and land use change
\"Although bioenergy is a renewable energy source, it is not without impact on the environment. Both the cultivation of crops specifically for use as biofuels and the use of agricultural byproducts to generate energy changes the landscape, affects ecosystems, and impacts the climate. Bioenergy and Land Use Change focuses on regional and global assessments of land use change related to bioenergy and the environmental impacts. This interdisciplinary volume provides both high level reviews and in-depth analyses on specific topics. Volume highlights include: land use change concepts, economics, and modeling; relationships between bioenergy and land use change; impacts on soil carbon, soil health, water quality, and the hydrologic cycle; impacts on natural capital and ecosystem services; effects of bioenergy on direct and indirect greenhouse gas emissions; biogeochemical and biogeophysical climate regulation; and uncertainties and challenges associated with land use change quantification and environmental impact assessments. Bioenergy and Land Use Change is a valuable resource for professionals, researchers, and graduate students from a wide variety of fields including energy, economics, ecology, geography, agricultural science, geoscience, and environmental science\" -- Provided by publisher.
Book
Using Landsat satellite data for assessing the land use and land cover change in Kashmir valley
Land use and land cover (LULC) change has been one of the most immense and perceptible transformations of the earth’s surface. Evaluating LULC change at varied spatial scales is imperative in wide range of perspectives such as environmental conservation, resource management, land use planning, and sustainable development. This work aims to examine the land use and land cover changes in the Kashmir valley between the time periods from 1992–2001–2015 using a set of compatible moderate resolution Landsat satellite imageries. Supervised approach with maximum likelihood classifier was adopted for the classification and generation of LULC maps for the selected time periods. Results reveal that there have been substantial changes in the land use and cover during the chosen time periods. In general, three land use and land cover change patterns were observed in the study area: (1) consistent increase of the area under marshy, built-up, barren, plantation, and shrubs; (2) continuous decrease in agriculture and water; (3) decrease (1992–2001) and increase (2001–2015) in forest and pasture classes. In terms of the area under each LULC category, most significant changes have been observed in agriculture (−), plantation (+), built-up (+), and water (−); however, with reference to percent change within each class, the maximum variability was recorded in built-up (198.45%), plantation (87.98%), pasture (− 71%), water (− 48%) and agriculture (− 28.85%). The massive land transformation is largely driven by anthropogenic actions and has been mostly adverse in nature, giving rise to multiple environmental issues in the ecologically sensitive Kashmir valley.
Journal Article
Disrupted landscapes
The fall of the Soviet Union was a transformative event for the national political economies of Eastern Europe, leading not only to new regimes of ownership and development but to dramatic changes in the natural world itself. This painstakingly researched volume focuses on the emblematic case of postsocialist Romania, in which the transition from collectivization to privatization profoundly reshaped the nation's forests, farmlands, and rivers. From bureaucrats abetting illegal deforestation to peasants opposing government agricultural policies, it reveals the social and political mechanisms by which neoliberalism was introduced into the Romanian landscape.
eBook
The great urban transformation : politics of land and property in China
This book emphasizes the centrality of cities in China's ongoing transformation. Based on fieldwork in twenty-four Chinese cities between 1996 and 2007, the author forwards an analysis of the relations between the city, the state, and society through two novel concepts: urbanization of the local state and civic territoriality. Urbanization of the local state is a process of state power restructuring entailing an accumulation regime based on the commodification of state-owned land, the consolidation of territorial authority through construction projects, and a policy discourse dominated by notions of urban modernity. Civic territoriality encompasses the politics of distribution engendered by urban expansionism, and social actors' territorial strategies toward self-protection. Findings are based on observations in three types of places. In the inner city of major metropolitan centers, municipal governments battle high-ranking state agencies to secure land rents from redevelopment projects, while residents mobilize to assert property and residential rights. At the urban edge, as metropolitan governments seek to extend control over their rural hinterland through massive-scale development projects, villagers strategize to profit from the encroaching property market. At the rural fringe, township leaders become brokers of power and property between the state bureaucracy and villages, while large numbers of peasants are dispossessed, dispersed, and deterritorialized; their mobilizational capacity is consequently undermined.
eBook
Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review
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.
Journal Article
Analysis of peri-urban land use/land cover change and its drivers using geospatial techniques and geographically weighted regression
The rate of transformation of natural land use land cover (LULC) to the built-up areas is very high in the peri-urban areas of Indian metropolitan cities. Delhi National Capital Region (Delhi NCR) is an inter-state planning region, located in the central part of India. The region has attracted a larger chunk of population by providing better economic opportunities during last few decades. This has resulted in large-scale transformation of the LULC pattern in the region. Thus, this study is intended to analyze and quantify the LULC change and its drivers in the peri-urban areas of Delhi NCR using Landsat datasets. Based on an extensive literature survey, several potential drivers of the LULC change have been analyzed using ordinary least squares (OLS) and geographical weighted regression (GWR) for the Delhi NCR. The results from LULC classification showed that the built-up area has increased from 1.67 to 7.12% of the total area of Delhi NCR during 1990–2018 while other LULC types have declined significantly. The OLS results showed that migration and employment in the tertiary sector are the most important drivers of built-up expansion in the study area. The standard residuals and local
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results from GWR showed spatial heterogeneity among the coefficients of the explanatory variables throughout the study area. This study can be helpful for the urban policy makers and planners for making better master plan of Delhi NCR and other cities of developing countries.
Journal Article