Impervious surface mapping with Quickbird imagery

Lu, Dengsheng; Hetrick, Scott; Moran, Emílio F.

doi:10.1080/01431161003698393

Cited by 99 publications

(68 citation statements)

References 54 publications

(86 reference statements)

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“…These limitations are no longer related to the coarse spatial resolution of the sensors, because for the last ten years imagery from several space-borne sensor systems with sub-meter spatial resolution have been available [2]. Obtaining better results is most frequently hampered by the fact that these sensors provide images with only four spectral bands (generally named blue, green, red and infra-red), which makes the distinction of urban land cover classes of similar coloration a difficult task [3,4].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Comparison between WorldView-2 and QuickBird-2-Simulated Imagery Regarding Object-Based Urban Land Cover Classification

et al. 2011

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Abstract:The objective of this study is to compare WorldView-2 (WV-2) and QuickBird-2-simulated (QB-2) imagery regarding their potential for object-based urban land cover classification. Optimal segmentation parameters were automatically found for each data set and the obtained results were quantitatively compared and discussed. Four different feature selection algorithms were used in order to verify to which data set the most relevant object-based features belong to. Object-based classifications were performed with four different supervised algorithms applied to each data set and the obtained accuracies and model performances indexes were compared. Segmentation experiments carried out involving bands exclusively available in the WV-2 sensor generated segments slightly more similar to our reference segments (only about 0.23 discrepancy). Fifty seven percent of the different selected features and 53% of all the 80 selections refer to features that can only be calculated with the additional bands of the WV-2 sensor. On the other hand, 57% of the most relevant features and 63% of the second most relevant features can also be calculated considering only the QB-2 bands. In 10 out of 16 classifications, higher Kappa values were achieved when features related to the additional bands of the WV-2 sensor were also considered. In most cases, classifications carried out with the 8-band-related features generated less complex and more efficient models than those generated only with OPEN ACCESSRemote Sens. 2011, 3 2264 QB-2 band-related features. Our results lead to the conclusion that spectrally similar classes like ceramic tile roofs and bare soil, as well as asphalt and dark asbestos roofs can be better distinguished when the additional bands of the WV-2 sensor are used throughout the object-based classification process.

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Section: Introductionmentioning

confidence: 99%

“…For most urban remote sensing applications and for most of those ones based on very high spatial resolution data, the object-based image analysis approach is advantageous [4,[6][7][8][9]. Object-based image classification involves three main steps: (1) determine appropriate segmentation parameters;…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Comparison between WorldView-2 and QuickBird-2-Simulated Imagery Regarding Object-Based Urban Land Cover Classification

et al. 2011

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“…Conventional satellites are mainly used for high resolution images. One example is the Quick-Bird satellite, which high spatial resolution, i.e., panchromatic is 61 cm GSD at Nadir and Multispectral is 2.4 meter GSD at Nadir [3]. As set out in Table 1, these large satellites are really costly, heavy and consume large amount of power.…”

Section: Introductionmentioning

confidence: 99%

“…The applications of remote sensing include land use, agriculture, environment monitoring, disaster assessment, research, and national security. An example of mini satellite is FORMOSAT-2, which is a Taiwanese imagery satellite with a mass of 693 kg (without original propellant) and operate in the Low Earth Orbit (LEO) at altitude of 891 km [3]. Another example is SPOT 5, which is a French remote sensing mini satellite.…”

Section: Introductionmentioning

confidence: 99%

The design requirements for Libyan imaging mini-satellite (LibyaSat-1)

Tubbal

Elarabi

Etabeb

et al. 2015

2015 1st International Conference on Wireless and Telematics (ICWT)

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In this paper we present the conceptual design of Libyan remote sensing satellite (LibyaSat-1) and its subsystems requirements. LibyaSat-1 is a 300 kg mini satellite, which will be used to support high resolution multi-spectral earth imaging camera to fulfill the civilian needs. This satellite will operate at LEO of 775 km and will provide a resolution of 2.5 m for the panchromatic band and 10 m for the VIS/NIR bands with 30 km swath. We have presented the mission overview, mission operation concept and mission requirements. Moreover, the System Tool Kit (STK) simulation is used to show the ground trucks of LibyaSat-1 for three days and to find the contact numbers between LibyaSat-1 and both Murezeq and Tripoli stations. We have also presented the design of telemetry and command subsystem, code and data handling subsystem, electrical power subsystem, altitude orbit control subsystem, and structure subsystem. Abstract-In this paper we present the conceptual design of Libyan remote sensing satellite (LibyaSat-1) and its sub-systems requirements. LibyaSat-1 is a 300 kg mini satellite, which will be used to support high resolution multi-spectral earth imaging camera to fulfill the civilian needs. This satellite will operate at LEO of 775 km and will provide a resolution of 2.5 m for the panchromatic band and 10 m for the VIS/NIR bands with 30 km swath. We have presented the mission overview, mission operation concept and mission requirements. Moreover, the System Tool Kit (STK) simulation is used to show the ground trucks of LibyaSat-1 for three days and to find the contact numbers between LibyaSat-1 and both Murezeq and Tripoli stations. We have also presented the design of telemetry and command subsystem, code and data handling subsystem, electrical power subsystem, altitude orbit control subsystem, and structure subsystem.

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“…Classification is widely used to derive thematic information from (RS) [25]. Remote sensing has been widely used for mapping land cover at a variety of spatio-temporal scales [26].…”

Section: Introductionmentioning

confidence: 99%

Post-War Land Cover Changes and Fragmentation in Halgurd Sakran National Park (HSNP), Kurdistan Region of Iraq

Hamad

Kolo

Balzter

2018

Land

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Context:The fundamental driving force of land use and land cover (LULC) change is related to spatial and temporal processes caused by human activities such as agricultural expansion and demographic change. Landscape metrics were used to analyze post-war changes in a rural mountain landscape, the protected area of Halgurd-Sakran National Park (HSNP) in north-east Iraq. Therefore, the present work attempts to identify the temporal trends of the most fragmented land cover types between two parts of the national park. Objectives: The objectives of this study are to compare two land cover classification algorithms, maximum likelihood classification (MLC) and random forest (RF) in the upper and lower parts of HSCZ, and to examine whether landscape configuration in the park has changed over time by comparing the fragmentation, connectivity and diversity of LULC classes. Methods: Two Landsat images were used to analyze LULC fragmentation and loss of habitat connectivity (before and after the Fall of Baghdad in 2003). Seven landscape pattern metrics, percentage of land (PLAND), number of patch (NP), largest patch index (LPI), mean patch size (MPS), euclidian nearest neighborhood distance (ENN_AM), interspersion and juxtaposition (IJI) and cohesion at class level were selected to assess landscape composition and configuration. Results: A significant change in LULC classes was noticed in the lower part of the park, especially for pasture, cultivated and forest-lands. The fragmentation trends and their changes were observed in both parts of the park, however, more were observed in the lower part. The inherent causes of these changes are the socio-economic factors created by the 1991-2003 UN post-war economic sanctions. The changes increased during sanctions and decreased afterwards. The fall of Baghdad in 2003, followed by rapid economic boom, marked the greatest cause in land use change, especially in changes-susceptible cultivated areas. Conclusions: Shrinkage of forest patches in the lower part of the park increases the distance between them, which contributes to a decline in biological diversity from decreasing habitat area. Lastly, the results confirm the applicability of the combined method of remote sensing and landscape metrics.

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Impervious surface mapping with Quickbird imagery

Cited by 99 publications

References 54 publications

Machine Learning Comparison between WorldView-2 and QuickBird-2-Simulated Imagery Regarding Object-Based Urban Land Cover Classification

Machine Learning Comparison between WorldView-2 and QuickBird-2-Simulated Imagery Regarding Object-Based Urban Land Cover Classification

The design requirements for Libyan imaging mini-satellite (LibyaSat-1)

Post-War Land Cover Changes and Fragmentation in Halgurd Sakran National Park (HSNP), Kurdistan Region of Iraq

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