450 research outputs found
Land Use and Land Cover Mapping in a Changing World
It is increasingly being recognized that land use and land cover changes driven by anthropogenic pressures are impacting terrestrial and aquatic ecosystems and their services, human society, and human livelihoods and well-being. This Special Issue contains 12 original papers covering various issues related to land use and land use changes in various parts of the world (see references), with the purpose of providing a forum to exchange ideas and progress in related areas. Research topics include land use targets, dynamic modelling and mapping using satellite images, pressures from energy production, deforestation, impacts on ecosystem services, aboveground biomass evaluation, and investigations on libraries of legends and classification systems
Land Surface Monitoring Based on Satellite Imagery
This book focuses attention on significant novel approaches developed to monitor land surface by exploiting satellite data in the infrared and visible ranges. Unlike in situ measurements, satellite data provide global coverage and higher temporal resolution, with very accurate retrievals of land parameters. This is fundamental in the study of climate change and global warming. The authors offer an overview of different methodologies to retrieve land surface parameters— evapotranspiration, emissivity contrast and water deficit indices, land subsidence, leaf area index, vegetation height, and crop coefficient—all of which play a significant role in the study of land cover, land use, monitoring of vegetation and soil water stress, as well as early warning and detection of forest fires and drought
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Decarbonization, Irrigation, and Energy System Planning: Analyses in New York State and Ethiopia
This dissertation contains two collections of analyses, both broadly focused on energy system planning, but motivated by different research objectives in distinct geographic settings.
Part I – Chapters I-III – evaluates decarbonization strategies in New York. These studies are characteristic of the primary energy-related challenge faced by the Global North: How can states cost-effectively meet time-bound emissions reduction targets? A series of linear programs are developed to answer this question, culminating in the System Electrification and Capacity TRansition (SECTR) model, a high-fidelity representation of the New York State energy system that characterizes statewide emissions and allows for comparative study of various decarbonization pathways. SECTR simulations indicate that prioritizing heating and vehicle electrification alongside an expansion of instate wind and solar generation capacity allows New York to meet recently legislated climate goals more affordably than through approaches that mandate substantial low-carbon electricity targets. Additional work also explores the optimal distribution of energy infrastructure within New York to meet specified decarbonization targets, along with the value of supply-side, demand-side, and bidirectional methods of system flexibility.
Part II of this dissertation – Chapters IV-VII – is concerned with the energy system challenges faced by the lowest income countries. Set in the Ethiopian Highlands, this work first aims to locate smallholder irrigated areas, as irrigation has attendant energy requirements that are larger and more likely to generate supplementary sources of revenue compared to residential demands. Here, a novel classification methodology is developed to collect labeled data, train a machine learning-based irrigation detection model, and understand the spatial extent of model applicability. Across isolated plots of land as small as 30m by 30m, the resulting model achieves >95% prediction accuracy. Further studies then explore the system planning implications of simulated electricity demands associated with these irrigated areas
Undergraduate Bulletin, 2023-2024
https://red.mnstate.edu/bulletins/1107/thumbnail.jp
AVHRR NDVI Compositing Method Comparison and Generation of Multi-decadal Time Series —A TIMELINE Thematic Processor
Remote sensing image composites are crucial for a wide range of remote sensing applications, such as multi-decadal time series analysis. The Advanced Very High Resolution Radiometer (AVHRR) instrument provides daily data since the early 1980s at a spatial resolution of 1 km, allowing analyses of climate change related environmental processes. For monitoring vegetation condition, the Normalized Difference Vegetation Index (NDVI) is the most widely used metric. However, to actually enable such analyses, a consistent NDVI time series over the AVHRR time-span needs to be created. In this context, the aim of this study is to thoroughly assess the effect of different compositing procedures on AVHRR NDVI composites, as there is no standard procedure established. 13 different compositing methods have been implemented, daily, decadal and monthly composites over Europe and Northern Africa have been calculated for the year 2007, and the resulting data sets have been thoroughly evaluated according to six criteria. The median approach was selected as the best performing compositing algorithm considering all investigated aspects. However, also the combination of NDVI value and viewing and illumination angles as criteria for best-pixel selection proved to be a promising approach. The generated NDVI time series, currently ranging from 1981 - 2018, shows a consistent behavior and a close agreement to the standard MODIS NDVI product. The conducted analyses demonstrate the strong influence of compositing procedures on the resulting AVHRR NDVI composites
Impacts of irrigation tank restoration on water bodies and croplands in Telangana State of India using Landsat time series data and machine learning algorithms
In 2014, the State of Telangana in southern India began repairing and restoring more than 46,000 irrigation water tanks (artificial reservoirs) under the Mission Kakatiya project with an investment in excess of USD 2 billion. In this study, we attempted to map the temporal changes that have occurred in cropland areas and water bodies as a result of the project, using remote sensing imagery and applying land use/land cover (LULC) mapping algorithms. We used 16-day time series data from Landsat 8 to study the spatial
distribution of changes in water bodies and cropland areas over the 2013–18 period. Ground survey information was used to assess the pixel-based accuracy of the Landsat-derived data. The areas served by these tanks were identified on the basis of training data and Random Forest algorithms using Google Earth Engine. Our spatial analysis revealed a substantial increase in cropped area under irrigation and expansion of water bodies over the study period. We observed a 20% increase in total tank area in 2017–18 and total cropland and irrigated area expansion of the order of 0.6M ha and 0.2M ha, respectively. A comparison of ground survey data and four LULC classes derived from Landsat temporal imagery showed an overall accuracy of 87%, significantly
correlated with national agriculture statistics. Periodic monitoring based on remote sensing has proved to be an effective method of capturing LULC changes resulting from the Mission Kakatiya interventions. Higher-resolution satellite data can further improve the accuracy of estimates
Land Use and Land Cover Mapping in a Changing World
It is increasingly being recognized that land use and land cover changes driven by anthropogenic pressures are impacting terrestrial and aquatic ecosystems and their services, human society, and human livelihoods and well-being. This Special Issue contains 12 original papers covering various issues related to land use and land use changes in various parts of the world (see references), with the purpose of providing a forum to exchange ideas and progress in related areas. Research topics include land use targets, dynamic modelling and mapping using satellite images, pressures from energy production, deforestation, impacts on ecosystem services, aboveground biomass evaluation, and investigations on libraries of legends and classification systems
Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate
This book focuses on some significant progress in vegetation dynamics and their response to climate change revealed by remote sensing data. The development of satellite remote sensing and its derived products offer fantastic opportunities to investigate vegetation changes and their feedback to regional and global climate systems. Special attention is given in the book to vegetation changes and their drivers, the effects of extreme climate events on vegetation, land surface albedo associated with vegetation changes, plant fingerprints, and vegetation dynamics in climate modeling
The Spatial and Temporal Characteristics of the Urban Thermal Environment in East Africa: Implications for Sustainable Urban Development
Targeting cities in East Africa, where urbanisation and climate change are posing unprecedented threats to livelihoods and ecosystems, this thesis focuses on the combined effects of rapid urbanisation and climate change on Land Surface Temperature (LST), Surface Urban Heat Island (SUHI) effects and the role of Blue Green infrastructure (BGI) and vegetation dynamics. The aim of this thesis is to advance understanding of the urban thermal environment and the role of factors such as climate, vegetation and urbanisation patterns that add to its complexity.
Through the use of satellite and remote sensing data (e.g., Google Earth Engine), spatial and statistical analyses, conducted in ArcGIS, Geoda and R, this thesis provides analyses of temporal trends between 2003 and 2017, and spatial differences in LST and SUHI in five East African cities (Khartoum, Addis Ababa, Kampala, Nairobi, Dar es Salaam). It advances understanding of how the configuration of urban areas affects the urban thermal environment, the amount of vegetation and surface water, and demonstrates the influence of urban density on the changes in SUHI intensity in both space and time.
By linking the findings from the three results chapters and placing this in the context of the broader literature, corresponding policy implications and solutions are presented. The urgent need to provide a more detailed understanding of urban thermal environments, including macroclimate differences, seasonal variation and urban morphological characteristics, is highlighted. Recommendations emphasise the use of cloud-based analysis methods to overcome data scarcity, while the results point towards the utility of nature-based solutions for urban sustainable development. The methods and lessons emerging from this study can also be applied in other rapidly urbanising cities, where climate change is posing an unprecedented threat to livelihoods and ecosystems, and where resources are limited
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