Land use/land cover classification for applied urban planning - the challenge of automation

Thunig, Holger; Wolf, Nils; Naumann, Simone; Siegmund, Alexander; Jürgens, Carsten; Uysal, Cihan; Maktav, D.

doi:10.1109/jurse.2011.5764762

Cited by 12 publications

(4 citation statements)

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“…By application of classification algorithms, one can generate an up-to-date land cover/landuse map of an area of interest. For the extraction of that kind of thematic information out of the potentially ambiguous image data, one has to define a proper nomenclature to describe the land cover/land-use classes for a clear separation among each other (Thunig et al 2011). Normally a hierarchically structured nomenclature serves most needs and allows to merge classes of lower hierarchy to a class of the next level hierarchy if needed in generalisation processes.…”

Section: Use Of Earth Observation Image Datamentioning

confidence: 99%

Data Sources

et al. 2019

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This chapter is devoted to the overview of the data fundamentals as regards data models and sources accompanied by geomatics, remote sensing, and economy. Description of such data sources is complemented with the basics from respective disciplines to provide a thematic context to the reader. The chapter starts with a summary of the most commonly used data models, starting with tabular and attribute formats. It is then followed by the spatial data models, including vector and raster data core principles. Since the geospatial domain is heterogeneous in terms of different data formats, the list of interoperability data sources and services is provided. Emphasis is also given to the international and selected national data sources, both non-spatial and spatial. This part is mainly covering the economic (sociodemographic) topics. At last, a remote sensing perspective on data sources is introduced, pointing out the most important Earth observation data. The whole chapter focuses on the major data models and sources, so it serves as a gateway to further exploration of existing data storages.

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Section: Use Of Earth Observation Image Datamentioning

confidence: 99%

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et al. 2019

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“…Furthermore, the methodologies presented in these studies, such as landscape metrics (e.g., Herold et al, 2002 and2005), structural pattern recognition (e.g., Barr et al, 2004), and object-oriented image processing (e.g., Blaschke et al, 2000;Mathieu et al, 2007), have opened up new venues to utilize high-resolution land cover and other types of satellite images for a variety of research or policy-making purposes. More recently, further methodological advancement has been achieved (see e.g., Rashed and Jurgens, 2010;Thunig et al, 2011), even though this progress has often been made outside of social science.…”

Section: Previous Research -A Brief Reviewmentioning

confidence: 99%

Crossing-over between land cover and land use: Exploring spatially varying relationships in two large US metropolitan areas

Kim

2015

Applied Geography

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Crossing-over between land cover and land use: Exploring spatially varying relationships in two large US metropolitan areas Abstract: Difficulties in identifying actual uses of land space from remote sensing-based land cover products often result in lost opportunities to enhance the capacity of applied research on human settlements. In an attempt to address these difficulties, this study investigates how land cover and land use are interrelated with each other and what determines the relationship patterns by analyzing detailed land use and land cover data for two large US metropolitan areas-the five-county Los Angeles and six-county Chicago regions-where a broad spectrum of human settlements, ranging from urban cores to less-urbanized edges, coexist. The analysis shows that the land cover-land use relationship substantially varies not only across regions but across neighborhoods within each region. Through multivariate regression, it is also found that the intraregional variation is highly associated with the neighborhood's stage of urbanization, median housing age, and other development characteristics, suggesting that the relationship pattern can largely be shaped by the history and evolution of urban design/development.

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“…Furthermore, small islands face environmental issues directly or indirectly related to land use/land cover changes (LULCCs), such as natural hazards, loss of biodiversity, and proliferation of invasive alien species, some of which are caused by direct human exploitation [3]. In Macaronesian oceanic archipelagos like the Azores, the main threat to nature conservation is LULCCs [4][5][6][7]. Nature protection and slowing the rate of biodiversity loss in small oceanic islands have become increasingly essential.…”

Section: Introductionmentioning

confidence: 99%

A Land Cover Change Detection Approach to Assess the Effectiveness of Conservation Projects: A Study Case on the EU-Funded LIFE Projects in São Miguel Island, Azores (2002–2021)

Tiengo,

Merino-De-Miguel,

Uchôa

et al. 2024

Land

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Small oceanic islands, such as São Miguel Island in the Azores (Portugal), face heightened susceptibility to the adverse impacts of climate change, biological invasions, and land cover changes, posing threats to biodiversity and ecosystem functions and services. Over the years, persistent conservation endeavors, notably those supported by the EU LIFE Programme since 2003, have played a pivotal role in alleviating biodiversity decline, particularly in the eastern region of São Miguel Island. This study advocates the application of remote sensing data and techniques to support the management and effective monitoring of LIFE Nature projects with land cover impacts. A land cover change detection approach utilizing Rao’s Q diversity index identified and assessed changes from 2002 to 2021 in intervention areas. The study analyzed the changes in LIFE project areas using ASTER, Landsat 8, and Sentinel 2 data through Google Earth Engine on Google Colab (with Python). This methodological approach identified and assessed land cover changes in project intervention areas within defined timelines. This technological integration enhances the potential of remote sensing for near-real-time monitoring of conservation projects, making it possible to assess their land cover impacts and intervention achievements.

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Land use/land cover classification for applied urban planning - the challenge of automation

Cited by 12 publications

References 1 publication

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Crossing-over between land cover and land use: Exploring spatially varying relationships in two large US metropolitan areas

A Land Cover Change Detection Approach to Assess the Effectiveness of Conservation Projects: A Study Case on the EU-Funded LIFE Projects in São Miguel Island, Azores (2002–2021)

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