Mapping 2000–2010 Impervious Surface Change in India Using Global Land Survey Landsat Data

Wang, Panshi; Huang, Chengquan; Colstoun, E. C. Brown de

doi:10.3390/rs9040366

Cited by 21 publications

(13 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We estimated the surface water fraction for the Mediterranean region from MODIS data, and improved on previous efforts to estimate surface water fraction from mediumresolution imagery (MODIS or similar). The prediction accuracy of our model (R 2 = 0.91, RMSE = 11.41 %, and MAE = 6.39 %) is higher than the R 2 of 0.625 reported by Weiss and Crabtree (2011), who used a linear regression model, and the R 2 of 0.7 reported by Guerschmann et al (2011), using a logistic regression model. This research successfully expanded our previous work by upscaling it from a relatively small region to the whole Mediterranean while retaining a similar high accuracy (both achieved an R 2 of 0.91).…”

Section: Discussioncontrasting

confidence: 72%

A new dense 18-year time series of surface water fraction estimates from MODIS for the Mediterranean region

Skidmore

Vrieling

2019

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Detailed knowledge on surface water distribution and its changes is of high importance for water management and biodiversity conservation. Landsat-based assessments of surface water, such as the Global Surface Water (GSW) dataset developed by the European Commission Joint Research Centre (JRC), may not capture important changes in surface water during months with considerable cloud cover. This results in large temporal gaps in the Landsat record that prevent the accurate assessment of surface water dynamics. Here we show that the frequent global acquisitions by the Moderate Resolution Imaging Spectrometer (MODIS) sensors can compensate for this shortcoming, and in addition allow for the examination of surface water changes at fine temporal resolution. To account for water bodies smaller than a MODIS cell, we developed a global rule-based regression model for estimating the surface water fraction from a 500 m nadir reflectance product from MODIS (MCD43A4). The model was trained and evaluated with the GSW monthly water history dataset. A high estimation accuracy (R2=0.91, RMSE =11.41 %, and MAE =6.39 %) was achieved. We then applied the algorithm to 18 years of MODIS data (2000–2017) to generate a time series of surface water fraction maps at an 8 d interval for the Mediterranean. From these maps we derived metrics including the mean annual maximum, the standard deviation, and the seasonality of surface water. The dynamic surface water extent estimates from MODIS were compared with the results from GSW and water level data measured in situ or by satellite altimetry, yielding similar temporal patterns. Our dataset complements surface water products at a fine spatial resolution by adding more temporal detail, which permits the effective monitoring and assessment of the seasonal, inter-annual, and long-term variability of water resources, inclusive of small water bodies.

show abstract

Section: Discussioncontrasting

confidence: 72%

A new dense 18-year time series of surface water fraction estimates from MODIS for the Mediterranean region

Skidmore

Vrieling

2019

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…However, different seasons of multispectral imagery can produce different fractional results [44], resulting in the difficulty in detection of urban land cover change. Therefore, change detection based on fractional results has not been commonly used in reality due to different biases caused by seasonal variation [45,46]. In order to avoid this difficulty, this research used a hybrid approach consisting of spectral mixture analysis, vegetation indices, and cluster analysis to extract ISA data for examining spatial patterns and rates of urban expansion in the Asian metropoles.…”

Section: Analysis Of Spatial Patterns and Expansion Ratesmentioning

confidence: 99%

Examining Spatial Patterns of Urban Distribution and Impacts of Physical Conditions on Urbanization in Coastal and Inland Metropoles

Li²,

et al. 2018

Remote Sensing

View full text Add to dashboard Cite

Urban expansion has long been a research hotspot and is often based on individual cities, but rarely has research conducted a comprehensive comparison between coastal and inland metropoles for understanding different spatial patterns of urban expansions and driving forces. We selected coastal metropoles (Shanghai and Shenzhen in China, and Ho Chi Minh City in Vietnam) and inland metropoles (Ulaanbaatar in Mongolia, Lanzhou in China, and Vientiane in Laos) with various developing stages and physical conditions for examining the spatiotemporal patterns of urban expansions in the past 25 years . Multitemporal Landsat images with 30 m spatial resolution were used to develop urban impervious surface area (ISA) distributions and examine their dynamic changes. The impacts of elevation, slope, and rivers on spatial patterns of urban expansion were examined. This research indicates that ISA is an important variable for examining urban expansion. Coastal metropoles had much faster urbanization rates than inland metropoles. The spatial patterns of urban ISA distribution and expansion are greatly influenced by physical conditions; that is, ISA is mainly distributed in the areas with slopes of less than 10 degrees. Rivers are important geographical factors constraining urban expansion, especially in developing stages, while bridges across the rivers promote urban expansion patterns and rates. The relationships of spatial patterns of urban ISA distribution and dynamics with physical conditions provide scientific data for urban planning, management, and sustainability.

show abstract

“…However, because the urban landscape is very complex and highly heterogeneous, the spectral signals of remote sensing pixels usually mingle the effects of impervious and non-impervious surfaces, even at 30 m resolution. Therefore, numerous studies have focused on an estimation of the continuous impervious surface percentage (ISP) within remote sensing pixels [3,17,18]. Various methods, including spectral mixture analysis (SMA) [19], regression model [17], and spectral index-based method [20], have been employed to extract ISP from the available remote sensing images at the sub-pixel level.…”

Section: Introductionmentioning

confidence: 99%

Estimating 2009–2017 Impervious Surface Change in Gwadar, Pakistan Using the HJ-1A/B Constellation, GF-1/2 Data, and the Random Forest Algorithm

Bian

Zuo

et al. 2019

IJGI

View full text Add to dashboard Cite

The China–Pakistan Economic Corridor (CPEC) is the flagship project of the Belt and Road Initiative. At the end of the CPEC, the Gwadar port on the Arabian Sea is being built quickly, providing an important economical route for the flow of Central Asia’s natural resources to the world. Gwadar city is in a rapid urbanization process and will be developed as a modern, world-class port city in the near future. Therefore, monitoring the urbanization process of Gwadar at both high spatial and temporal resolution is vital for its urban planning, city ecosystem management, and the sustainable development of CPEC. The impervious surface percentage (ISP) is an essential quantitative indicator for the assessment of urban development. Through the integration of remote sensing images and ISP estimation models, ISP can be routinely and periodically estimated. However, due to clouds’ influence and spatial–temporal resolution trade-offs in sensor design, it is difficult to estimate the ISP with both high spatial resolution and dense temporal frequency from only one satellite sensor. In recent years, China has launched a series of Earth resource satellites, such as the HJ (Huangjing, which means environment in Chinese)-1A/B constellation, showing great application potential for rapid Earth surface mapping. This study employs the Random Forest (RF) method for a long-term and fine-scale ISP estimation and analysis of the city of Gwadar, based on the density in temporal and multi-source Chinese satellite images. In the method, high spatial resolution ISP reference data partially covering Gwadar city was first extracted from the 1–2 meter (m) GF (GaoFen, which means high spatial resolution in Chinese)-1/2 fused images. An RF retrieval model was then built based on the training samples extracted from ISP reference data and multi-temporal 30-m HJ-1A/B satellite images. Lastly, the model was used to generate the 30-m time series ISP from 2009 to 2017 for the whole city area based on the HJ-1A/B images. Results showed that the mean absolute error of the estimated ISP was 6.1–8.1% and that the root mean square error (RMSE) of the estimation results was 12.82–15.03%, indicating the consistently high performance of the model. This study highlights the feasibility and potential of using multi-source Chinese satellite images and an RF model to generate long-term ISP estimations for monitoring the urbanization process of the key node city in the CPEC.

show abstract

Mapping 2000–2010 Impervious Surface Change in India Using Global Land Survey Landsat Data

Cited by 21 publications

References 37 publications

A new dense 18-year time series of surface water fraction estimates from MODIS for the Mediterranean region

A new dense 18-year time series of surface water fraction estimates from MODIS for the Mediterranean region

Examining Spatial Patterns of Urban Distribution and Impacts of Physical Conditions on Urbanization in Coastal and Inland Metropoles

Estimating 2009–2017 Impervious Surface Change in Gwadar, Pakistan Using the HJ-1A/B Constellation, GF-1/2 Data, and the Random Forest Algorithm

Contact Info

Product

Resources

About