Urban land use intensity assessment: The potential of spatio-temporal spectral traits with remote sensing

Wellmann, Thilo; Haase, Dagmar; Knapp, Sonja; Salbach, Christoph; Selsam, Peter; Lausch, Angela

doi:10.1016/j.ecolind.2017.10.029

Cited by 71 publications

(32 citation statements)

References 46 publications

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“…RS image segmentation is indispensable to measure urban metrics [66,67], to monitor landscape changes [68] and to model the pattern and extent of urban sprawl [69]. It is also important to define urban typologies [5], to classify land use [4], to manage urban environment [2] and to support sustainable urban development [6,7]. While for accurate decision making, diverse techniques should be involved [70][71][72][73][74], such as LiDAR and aerial imagery.…”

Section: Discussionmentioning

confidence: 99%

“…Buildings segmentation using remote-sensing (RS) images plays an important role in monitoring and modeling the process of urban landscape changes. Quantification of these changes can deliver useful products to individual users and public administrations and most importantly, it can be used to support the sustainable urban development and to balance both the economic and environmental benefits [1][2][3][4][5]. Via analyzing the data source of Landsat TM/ETM+ in 1990s, 2000s and 2010s, a study estimated China's urban expansion from the urban built-up area, the area of croplands converted into urban as well as the speed of urbanization in different cities [6].…”

Section: Introductionmentioning

confidence: 99%

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One View Per City for Buildings Segmentation in Remote-Sensing Images via Fully Convolutional Networks: A Proof-of-Concept Study

Wen

Jin

et al. 2019

Sensors

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The segmentation of buildings in remote-sensing (RS) images plays an important role in monitoring landscape changes. Quantification of these changes can be used to balance economic and environmental benefits and most importantly, to support the sustainable urban development. Deep learning has been upgrading the techniques for RS image analysis. However, it requires a large-scale data set for hyper-parameter optimization. To address this issue, the concept of “one view per city” is proposed and it explores the use of one RS image for parameter settings with the purpose of handling the rest images of the same city by the trained model. The proposal of this concept comes from the observation that buildings of a same city in single-source RS images demonstrate similar intensity distributions. To verify the feasibility, a proof-of-concept study is conducted and five fully convolutional networks are evaluated on five cities in the Inria Aerial Image Labeling database. Experimental results suggest that the concept can be explored to decrease the number of images for model training and it enables us to achieve competitive performance in buildings segmentation with decreased time consumption. Based on model optimization and universal image representation, it is full of potential to improve the segmentation performance, to enhance the generalization capacity, and to extend the application of the concept in RS image analysis.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

One View Per City for Buildings Segmentation in Remote-Sensing Images via Fully Convolutional Networks: A Proof-of-Concept Study

Wen

Jin

et al. 2019

Sensors

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“…In addition to recording 3D buildings, TIR, multispectral, or hyperspectral RS technologies can be used to detect other traits such as the characteristics of buildings, the degree of sealed surfaces and other aspects. Comparative reviews and papers on the acquisition and discrimination of plant species [137], the monitoring of vegetation diversity [54,138], forest health [139,140], as well as anthropogenic structures and traits [141,142] are all important in this context.…”

Section: Acquiring Surface Data On Vegetation and Urban Structuresmentioning

confidence: 99%

Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces

Lausch

Schaepman

Skidmore³

et al. 2020

Remote Sensing

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The status, changes, and disturbances in geomorphological regimes can be regarded as controlling and regulating factors for biodiversity. Therefore, monitoring geomorphology at local, regional, and global scales is not only necessary to conserve geodiversity, but also to preserve biodiversity, as well as to improve biodiversity conservation and ecosystem management. Numerous remote sensing (RS) approaches and platforms have been used in the past to enable a cost-effective, increasingly freely available, comprehensive, repetitive, standardized, and objective monitoring of geomorphological characteristics and their traits. This contribution provides a state-of-the-art review for the RS-based monitoring of these characteristics and traits, by presenting examples of aeolian, fluvial, and coastal landforms. Different examples for monitoring geomorphology as a crucial discipline of geodiversity using RS are provided, discussing the implementation of RS technologies such as LiDAR, RADAR, as well as multi-spectral and hyperspectral sensor technologies. Furthermore, data products and RS technologies that could be used in the future for monitoring geomorphology are introduced. The use of spectral traits (ST) and spectral trait variation (STV) approaches with RS enable the status, changes, and disturbances of geomorphic diversity to be monitored. We focus on the requirements for future geomorphology monitoring specifically aimed at overcoming some key limitations of ecological modeling, namely: the implementation and linking of in-situ, close-range, air- and spaceborne RS technologies, geomorphic traits, and data science approaches as crucial components for a better understanding of the geomorphic impacts on complex ecosystems. This paper aims to impart multidimensional geomorphic information obtained by RS for improved utilization in biodiversity monitoring.

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“…Figure 3d-g shows a decline in vegetation coverage at Ningbo from 1975 to 2015. The built-up density reflects the intensity of human-disturbance and probably population density [49], and a higher built-up density suggests a greater human alteration of the natural environments [50]. Figure 3h-k shows the NDBI maps that measure the built-up density as an important indicator of human impacts, reflecting the status of human-made surfaces.…”

Section: Ndvi Normalized Difference Vegetation Indexmentioning

confidence: 99%

Long-Term Regional Environmental Risk Assessment and Future Scenario Projection at Ningbo, China Coupling the Impact of Sea Level Rise

et al. 2019

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Regional environmental risk (RER) denotes potential threats to the natural environment, human health and socioeconomic development caused by specific risks. It is valuable to assess long-term RER in coastal areas with the increasing effects of global change. We proposed a new approach to assess coastal RER considering spatial factors using principal component analysis (PCA) and used a future land use simulation (FLUS) model to project future RER scenarios considering the impact of sea level rise (SLR). In our study, the RER status was classified in five levels as highest, high, medium, low and lowest. We evaluated the 30 m × 30 m gridded spatial pattern of the long-term RER at Ningbo of China by assessing its 1975-2015 history and projecting this to 2020-2050. Our results show that RER at Ningbo has increased substantially over the past 40 years and will slowly increase over the next 35 years. Ningbo's city center and district centers are exposed to medium-to-highest RER, while the suburban areas are exposed to lowest-to-medium lower RER. Storm surges will lead to strong RER increases along the Ningbo coast, with the low-lying northern coast being more affected than the mountainous southern coast. RER at Ningbo is affected principally by the combined effects of increased human activity, rapid population growth, rapid industrialization, and unprecedented urbanization. This study provides early warnings to support practical regulation for disaster mitigation and environmental protection.

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Urban land use intensity assessment: The potential of spatio-temporal spectral traits with remote sensing

Cited by 71 publications

References 46 publications

One View Per City for Buildings Segmentation in Remote-Sensing Images via Fully Convolutional Networks: A Proof-of-Concept Study

One View Per City for Buildings Segmentation in Remote-Sensing Images via Fully Convolutional Networks: A Proof-of-Concept Study

Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces

Long-Term Regional Environmental Risk Assessment and Future Scenario Projection at Ningbo, China Coupling the Impact of Sea Level Rise

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