Toward accurate mapping of 30-m time-series global impervious surface area (GISA)

Huang, Xin; Song, Yihong; Yang, Jie; Wang, Wenrui; Ren, Huiqun; Dong, Mengjie; Feng, Yujin; Yin, Haidan; Li, Jiayi

doi:10.1016/j.jag.2022.102787

Cited by 37 publications

(22 citation statements)

References 30 publications

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“…Although there is a 6-year gap between GlobeLand30 and the other X. Huang et al: Mapping 10 m global impervious surface area datasets (i.e., GISA and FROM_GLC10), we adopted the commonly used assumption that the transition from ISA to non-impervious surface area (NISA) rarely happens (P. Huang et al, 2021bHuang et al, , 2022, so that the GlobeLand30 for 2010 could be used for the GISA-10m mapping. The following spatial and spectral rules were used to remove the possible errors.…”

Section: Sample Collectionmentioning

confidence: 99%

Mapping 10 m global impervious surface area (GISA-10m) using multi-source geospatial data

Huang

Yang

Wang

et al. 2022

Earth Syst. Sci. Data

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Abstract. Artificial impervious surface area (ISA) documents the human footprint. Accurate, timely, and detailed ISA datasets are therefore essential for global climate change studies and urban planning. However, due to the lack of sufficient training samples and operational mapping methods, global ISA datasets at a 10 m resolution are still lacking. To this end, we proposed a global ISA mapping method leveraging multi-source geospatial data. Based on the existing satellite-derived ISA maps and crowdsourced OpenStreetMap (OSM) data, 58 million training samples were extracted via a series of temporal, spatial, spectral, and geometric rules. We then produced a 10 m resolution global ISA dataset (GISA-10m) from over 2.7 million Sentinel optical and radar images on the Google Earth Engine platform. Based on test samples that are independent of the training set, GISA-10m achieves an overall accuracy of greater than 86 %. In addition, the GISA-10m dataset was comprehensively compared with the existing global ISA datasets, and the superiority of GISA-10m was confirmed. The global road area was further investigated, courtesy of this 10 m dataset. It was found that China and the US have the largest areas of ISA and road. The global rural ISA was found to be 2.2 times that of urban while the rural road area was found to be 1.5 times larger than that of the urban regions. The global road area accounts for 14.2 % of the global ISA, 57.9 % of which is located in the top 10 countries. Generally speaking, the produced GISA-10m dataset and the proposed sampling and mapping method are able to achieve rapid and efficient global mapping, and have the potential for detecting other land covers. It is also shown that global ISA mapping can be improved by incorporating OSM data. The GISA-10m dataset could be used as a fundamental parameter for Earth system science, and will provide valuable support for urban planning and water cycle study. The GISA-10m can be freely downloaded from https://doi.org/10.5281/zenodo.5791855 (Huang et al., 2021a).

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Section: Sample Collectionmentioning

confidence: 99%

Mapping 10 m global impervious surface area (GISA-10m) using multi-source geospatial data

Huang

Yang

Wang

et al. 2022

Earth Syst. Sci. Data

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“…For example, Li and Xu [58] proposed a rapid method to extract training samples from multi-source land-cover products, which effectively improved the reliability and accuracy of the samples; and Li and Xu [59] used a robust marginal distance detection method to automatically update 35 annual training samples for dynamic surface water mapping. More recently, Huang et al [38] combined training samples from visual interpretation and automatic extraction to generate a new 30 m global ISA dataset (GISA 2.0), and the results indicated that this method further improves global ISA accuracy. Hence, these automatic and semi-automatic training sample collection methods are important references for high-resolution global ISA mapping.…”

Section: B Training Sample Collectionmentioning

confidence: 99%

“…Meanwhile, other features play a complementary role in global ISA mapping, and they contribute to further improving the accuracy of the datase Topographic features describe the elevation, slope, aspect of the land surface. Owing to the unique characteristics of topographic features in mountainous and shaded areas, many studies (e.g., GISA 2.0, GISA, GHSL, FROM-GLC, and GLC_FCS30) have generated ISA products using topographic features at a global scale [30], [32], [37], [38], [40]. For instance, the GLC_FCS30 product considers elevation, slope, and aspect, calculated from the Shuttle Radar Topography Mission (SRTM) Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEM data, to help identify ISA.…”

Section: Features Of Global High-resolution Isa Mappingmentioning

confidence: 99%

“…Focusing on the long time-series products (e.g., GISA 2.0, GISA, GAIA, GAUD, and GHSL), GISA 2.0 has the highest test accuracy, and its overall accuracy (OA) is 97.38% and 88.56% for the MI and ZY3 samples, respectively, followed by GISA (96.92%, 86.72%), GAUD (95.54%, 88.06%), GAIA (89.19%, 86.03%), and GHSL (94.37%, 83.72 %). GISA 2.0 obtains the highest accuracy due to its concentration on the inconsistent regions of the existing global ISA products, and the mapping results in these regions were enhanced by adding manually interpreted samples [38]. It should be noted that GHSL provides only a few discontinuous time-series results.…”

Section: ) Accuracy Comparisonmentioning

confidence: 99%

“…Moreover, the emergence of cloud computing platforms (such as Google Earth Engine (GEE), Amazon Web Services (AWS), Microsoft Azure Cloud, and PIE-Engine) has enabled parallel accessing, processing, and computing of huge amounts of remote sensing data [25]. Global ISA datasets with a high resolution (≤ 30 m) (the socalled second-generation products) have been produced in recent years, including the 30 m Global Land Cover (GlobeLand30) [26]- [28], the Finer Resolution Observation and Monitoring of Global Land Cover (FROM-GLC) [29]- [31], the Global Human Settlement Layer (GHSL) [32]- [34], the global artificial impervious areas (GAIA) [35], the global annual urban dynamics (GAUD) [36], the global impervious surface area (GISA) product [37], the updated global impervious surface area (GISA 2.0) [38], and the global landcover product with fine classification system (GLC_FCS30) [39], [40]. The second-generation products were achieved by remote sensing images with a fine spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

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Mapping High-Resolution Global Impervious Surface Area: Status and Trends

Ren

Liu

Chang³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Impervious surface area (ISA) mapping at the global scale has entered a new era. Currently, the number of highresolution global ISA products is gradually increasing; however, a literature review that systematically investigates these ISA products is still lacking, which limits the application of these products. Thus, we provide a comprehensive analysis of the existing high-resolution global ISA products, concentrating on the aspects of the data sources, training samples, features, and methods. Moreover, we evaluate these products at multi-temporal and multi-spatial scales, using a series of independent test samples. The results demonstrate that the multi-temporal accuracy of the ISA products presents an increasing trend, due to the increase of the available sensors. Among the continuous time-series products (e.g., the updated new global impervious surface area (GISA 2.0), the global impervious surface area (GISA), global annual urban dynamics (GAUD), Global Human Settlement Layer (GHSL), and global artificial impervious areas (GAIA)), the accuracy of the GISA 2.0 outperforms the others at global, continental, and regional scales. However, the mapping performance of these products in small towns and arid and rural regions needs to be enhanced. In particular, we focus on the spatio-temporal disagreement of the ISA products. We show that the high disagreement regions are predominantly concentrated in eastern Asia, western Europe, and eastern North America. In addition, the high disagreement regions are characterized by low ISA density, high vegetation coverage, and high albedo bare ground coverage. Additionally, this paper concludes with some remarks about the future directions of global ISA mapping.

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Wildlife resilience in an urban landscape: understanding land-use impacts in Cape Town

Oladimeji,

Woodgate,

O’Riain

2024

Urban Ecosyst

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Urbanisation is rapidly transforming and fragmenting natural habitats, disrupting ecosystems and negatively impacting biodiversity. The City of Cape Town (CoCT) is situated in a global biodiversity hotspot, but sustained anthropogenic activities have resulted in the local extirpation of most medium and large mammals. A recent survey of mammals within urban protected areas of CoCT revealed that a few, mostly medium-sized generalist species, persist. It is uncertain which native mammal species, if any, inhabit the unprotected green belts and parks in suburban and urban areas of the city. A total of 37 camera trap sites were established along four transects for a period of four months between 31 January and 31 May 2022. A total of 12 terrestrial mammal species were detected, nine of which were wild native mammals and three domestic species. Most detections were in natural habitat followed by suburban, with urban areas having the lowest detection rate of wildlife. Single season hierarchical multi-species occupancy models revealed that tree cover had a significant positive effect on both community and individual species occupancy. Contrary to our predictions, neither human population density nor the extent of the impervious surface at sites significantly affected occupancy. Cape grysbok (Raphicerus melanotis) were significantly more likely to occur at sites with a higher proportion of impervious surfaces supporting other recent research, which showed this species together with water mongoose (Atilax paludinosus) and Cape porcupine (Hystrix africaeaustralis) are one of only a few native mammals that appear to persist and may even thrive in human-modified landscapes. Our findings underscore the complexity of urban biodiversity conservation and the species-specific responses to environmental factors, emphasising the importance of tree cover in urban wildlife management.

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Toward accurate mapping of 30-m time-series global impervious surface area (GISA)

Cited by 37 publications

References 30 publications

Mapping 10 m global impervious surface area (GISA-10m) using multi-source geospatial data

Mapping 10 m global impervious surface area (GISA-10m) using multi-source geospatial data

Mapping High-Resolution Global Impervious Surface Area: Status and Trends

Wildlife resilience in an urban landscape: understanding land-use impacts in Cape Town

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