A Scheme for the Long-Term Monitoring of Impervious−Relevant Land Disturbances Using High Frequency Landsat Archives and the Google Earth Engine

Xu, Hualan; Wei, Yuchun; Liu, Chong; Fang, Hong

doi:10.3390/rs11161891

Cited by 31 publications

(23 citation statements)

References 65 publications

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“…The second step was to remove the effects of atmospheric influence on the thermal infrared band of the Landsat data. The surface-leaving radiance LT is calculated via Equation (1) [39]:…”

Section: Retrieval Of Lstmentioning

confidence: 99%

Influence of Impervious Surface Area and Fractional Vegetation Cover on Seasonal Urban Surface Heating/Cooling Rates

2021

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The urban thermal environment is impacted by changes in urban landscape patterns resulting from urban expansion and seasonal variation. In order to cope effectively with urban heat island (UHI) effects and improve the urban living environment and microclimate, an analysis of the heating effect of impervious surface areas (ISA) and the cooling effects of vegetation is needed. In this study, Landsat 8 data in four seasons were used to derive the percent ISA and fractional vegetation cover (FVC) by spectral unmixing and to retrieve the land surface temperature (LST) from the radiative transfer equation (RTE). The percent ISA and FVC were divided into four different categories based on ranges 0–25%, 25–50%, 50–75%, and 75–100%. The LST with percent ISA and FVC were used to calculate the surface heating rate (SHR) and surface cooling rate (SCR). Finally, in order to analyze the heating effect of ISA and the cooling effect of vegetation, the variations of LST with SHR and SCR were compared between different percent ISA and FVC categories in the four seasons. The results showed the following: (1) In summer, SHR decreases as percent ISA increases and SCR increases as FVC increases in the study area. (2) Unlike the dependence of LST on percent ISA and FVC, the trends of SHR/SCR as a function of percent ISA/FVC are more complex for different value ranges, especially in spring and autumn. (3) The SHR (heating capacity) decreases with increasing percent ISA in autumn. However, the SCR (cooling capacity) decreases with increasing FVC, except in summer. This study shows that our methodology to analyze the variation and change trends of SHR, SCR, and LST based on different ISA and FVC categories in different seasons can be used to interpret urban ISA and vegetation cover, as well as their heating and cooling effects on the urban thermal environment. This analytical method provides an important insight into analyzing the urban landscape patterns and thermal environment. It is also helpful for urban planning and mitigating UHI.

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“…The second step was to remove the effects of atmospheric influence on the thermal infrared band of the Landsat data. The surface-leaving radiance LT is calculated via Equation (1) [39]:…”

Section: Retrieval Of Lstmentioning

confidence: 99%

Influence of Impervious Surface Area and Fractional Vegetation Cover on Seasonal Urban Surface Heating/Cooling Rates

2021

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“…Furthermore, trends in mining-induced land cover change based on LandTrendr-GEE were successfully tracked respectively in Richards Bay Minerals Site, South Africa [54], and in central east Queensland, Australia [55]. The combination of LandTrendr-based indices and Landsat time-series data allowed analysis of cropland conversion patterns in a 10-year time span around Dongting Lake, China [56], and the detection of impervious surfaces in two urban areas of Jiangsu Province, China, including Nanjing [57] and Xinbei District [58]. LandTrendr-GEE's application in analyzing urban land cover dynamics and change trajectories is very scanty, thus this needs to be tested.…”

Section: Figurementioning

confidence: 99%

Continuous Monitoring of Urban Land Cover Change Trajectories with Landsat Time Series and LandTrendr-Google Earth Engine Cloud Computing

2020

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Producing accurate land cover maps is time-consuming and estimating land cover changes between two generated maps is affected by error propagation. The increased availability of analysis-ready Earth Observation (EO) data and the access to big data analytics capabilities on Google Earth Engine (GEE) have opened the opportunities for continuous monitoring of environment changing patterns. This research proposed a framework for analyzing urban land cover change trajectories based on Landsat time series and LandTrendr, a well-known spectral-temporal segmentation algorithm for land-based disturbance and recovery detection. The framework involved the use of baseline land cover maps generated at the beginning and at the end of the considered time interval and proposed a new approach to merge the LandTrendr results using multiple indices for reconstructing dense annual land cover maps within the considered period. A supervised support vector machine (SVM) classification was first performed on the two Landsat scenes, respectively, acquired in 1987 and 2019 over Kigali, Rwanda. The resulting land cover maps were then imported in the GEE platform and used to label the interannual LandTrendr-derived changes. The changes in duration, year, and magnitude of land cover disturbance were derived from six different indices/bands using the LandTrendr algorithm. The interannual change LandTrendr results were then combined using a robust estimation procedure based on principal component analysis (PCA) for reconstructing the annual land cover change maps. The produced yearly land cover maps were assessed using validation data and the GEE-based Area Estimation and Accuracy Assessment (Area2) application. The results were used to study the Kigali’s urbanization in the last three decades since 1987. The results illustrated that from 1987 to 1998, the urbanization was characterized by slow development, with less than a 2% annual growth rate. The post-conflict period was characterized by accelerated urbanization, with a 4.5% annual growth rate, particularly from 2004 onwards due to migration flows and investment promotion in the construction industry. The five-year interval analysis from 1990 to 2019 revealed that impervious surfaces increased from 4233.5 to 12116 hectares, with a 3.7% average annual growth rate. The proposed scheme was found to be cost-effective and useful for continuously monitoring the complex urban land cover dynamics, especially in environments with EO data affordability issues, and in data-sparse regions.

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“…In this study, we refer to the parameter scheme used by Kennedy et al [52] in relevant experiments in the United States. For the definition of the algorithm parameters and the parameters used in the experiment [52,53], see Supplementary Material Table S1.…”

Section: Random Forest Classificationmentioning

confidence: 99%

Detecting Forest Disturbance and Recovery in Primorsky Krai, Russia, Using Annual Landsat Time Series and Multi–Source Land Cover Products

Yang

2020

Remote Sensing

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The spatial distribution and dynamic changes of the forests in Primorsky Krai, Russia, are of great significance for regional ecological security and sustainable economic and societal development. With the support of the Google Earth Engine cloud computing platform, we first synthesized yearly Landsat surface reflectance images of the best quality of the research area and then used the random forest method to calculate the forest classification probability of the study area year by year from 1998 to 2015. Furthermore, we used a time–series segmentation algorithm to perform temporal trajectory segmentation for forest classification probability estimation, and determined the spatial and temporal distribution characteristics and change laws of the forest. We extended the existing algorithms and parameters of forest classification probability trajectory analysis, achieving a high overall accuracy (86.2%) in forest change detection in the study area. The extended method can accurately capture the time node information of the changes. In the present research we observed: (1) that from 1998 to 2015, the forest area of the whole district showed a net loss state, with a loss area of 0.56 × 106 ha, of which the cumulative forest disturbance area reached 1.12 × 106 ha, and the cumulative forest recovery area reached 0.55 × 106 ha; and (2) that more than 90% of the forest change occurred in areas with a slope of less than 18°, at a distance of less than 20 km from settlements, and at a distance of less than 10 km from roads. The forest disturbance monitoring results are consistent with the changes in official statistical results over time, but there was a 20% overestimation. The technical method we extended in this study can be used as a reference for large–scale and high–precision dynamic monitoring of the forests in Russia’s Far East and other regions of the world; it also provides a basis for estimating illegal timber harvesting and determining the appropriate amount of forest harvested.

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A Scheme for the Long-Term Monitoring of Impervious−Relevant Land Disturbances Using High Frequency Landsat Archives and the Google Earth Engine

Cited by 31 publications

References 65 publications

Influence of Impervious Surface Area and Fractional Vegetation Cover on Seasonal Urban Surface Heating/Cooling Rates

Influence of Impervious Surface Area and Fractional Vegetation Cover on Seasonal Urban Surface Heating/Cooling Rates

Continuous Monitoring of Urban Land Cover Change Trajectories with Landsat Time Series and LandTrendr-Google Earth Engine Cloud Computing

Detecting Forest Disturbance and Recovery in Primorsky Krai, Russia, Using Annual Landsat Time Series and Multi–Source Land Cover Products

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