Impact of highway construction on land surface energy balance and local climate derived from LANDSAT satellite data

Nedbal, Václav; Brom, Jakub

doi:10.1016/j.scitotenv.2018.03.220

Cited by 25 publications

(14 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Inappropriate land use is another triggering factor, which is regularly utilized in LSM [34]. Similarity, it reflects the influence of human activities on natural environment as surface coverage and the integrity of rocks [35].…”

Section: Conditioning Factorsmentioning

confidence: 99%

A Hybrid Model Consisting of Supervised and Unsupervised Learning for Landslide Susceptibility Mapping

et al. 2021

View full text Add to dashboard Cite

Landslides cause huge damage to social economy and human beings every year. Landslide susceptibility mapping (LSM) occupies an important position in land use and risk management. This study is to investigate a hybrid model which makes full use of the advantage of supervised learning model (SLM) and unsupervised learning model (ULM). Firstly, ten continuous variables were used to develop a ULM which consisted of factor analysis (FA) and k-means cluster for a preliminary landslide susceptibility map. Secondly, 351 landslides with “1” label were collected and the same number of non-landslide samples with “0” label were selected from the very low susceptibility area in the preliminary map, constituting a new priori condition for a SLM, and thirteen factors were used for the modeling of gradient boosting decision tree (GBDT) which represented for SLM. Finally, the performance of different models was verified using related indexes. The results showed that the performance of the pretreated GBDT model was improved with sensitivity, specificity, accuracy and the area under the curve (AUC) values of 88.60%, 92.59%, 90.60% and 0.976, respectively. It can be concluded that a pretreated model with strong robustness can be constructed by increasing the purity of samples.

show abstract

Section: Conditioning Factorsmentioning

confidence: 99%

A Hybrid Model Consisting of Supervised and Unsupervised Learning for Landslide Susceptibility Mapping

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The normalized vegetation index derived from satellites has been frequently used to assess vegetation responses to environmental change in many previous studies [34][35][36]. It is one of the most commonly used vegetation indices for monitoring ecosystem dynamics and processes [37,38].…”

Section: Normalized Difference Vegetation Indexmentioning

confidence: 99%

“…Canopy water content is an important indicator of ecosystem stress and health [36]. The normalized difference moisture index is a satellite-derived index that can be calculated from the near-infrared (NIR) and short-wave infrared (SWIR) channels of Landsat [40] as follows:…”

Section: Normalized Difference Moisture Indexmentioning

confidence: 99%

Quantification of the Environmental Impacts of Highway Construction Using Remote Sensing Approach

et al. 2021

View full text Add to dashboard Cite

Highways provide key social and economic functions but generate a wide range of environmental consequences that are poorly quantified and understood. Here, we developed a before–during–after control-impact remote sensing (BDACI-RS) approach to quantify the spatial and temporal changes of environmental impacts during and after the construction of the Wujing Highway in China using three buffer zones (0–100 m, 100–500 m, and 500–1000 m). Results showed that land cover composition experienced large changes in the 0–100 m and 100–500 m buffers while that in the 500–1000 m buffer was relatively stable. Vegetation and moisture conditions, indicated by the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI), respectively, demonstrated obvious degradation–recovery trends in the 0–100 m and 100–500 m buffers, while land surface temperature (LST) experienced a progressive increase. The maximal relative changes as annual means of NDVI, NDMI, and LST were about −40%, −60%, and 12%, respectively, in the 0–100m buffer. Although the mean values of NDVI, NDMI, and LST in the 500–1000 m buffer remained relatively stable during the study period, their spatial variabilities increased significantly after highway construction. An integrated environment quality index (EQI) showed that the environmental impact of the highway manifested the most in its close proximity and faded away with distance. Our results showed that the effect distance of the highway was at least 1000 m, demonstrated from the spatial changes of the indicators (both mean and spatial variability). The approach proposed in this study can be readily applied to other regions to quantify the spatial and temporal changes of disturbances of highway systems and subsequent recovery.

show abstract

“…The process of vegetation cooling has been described for open landscapes [10,11] and urban environments [12][13][14][15], and has been found to occur at mesoclimatic [16] and continental scales [17,18]. Conversely, vegetation removal brings about a reduction of evaporation and increased surface temperature [19,20]. The vegetation cooling effect in polar regions has received little attention until present, even though the ground surface of the Arctic tundra has been monitored using satellite technologies when it comes to surface temperature [21,22] and studied by ground measurement methods for energy fluxes [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Mitigation of Arctic Tundra Surface Warming by Plant Evapotranspiration: Complete Energy Balance Component Estimation Using LANDSAT Satellite Data

2020

Self Cite

View full text Add to dashboard Cite

Global climate change is expected to cause a strong temperature increase in the polar regions, accompanied by a reduction in snow cover. Due to a lower albedo, bare ground absorbs more solar energy and its temperature can increase more. Here, we show that vegetation growth in such bare ground areas can efficiently mitigate surface warming in the Arctic, thanks to plant evapotranspiration. In order to establish a comprehensive energy balance for the Arctic land surface, we used an ensemble of methods of ground-based measurements and multispectral satellite image analysis. Our estimate is that the low vegetation of polar tundra transforms 26% more solar energy into evapotranspiration than bare ground in clear sky weather. Due to its isolation properties, vegetation further reduces ground heat flux under the surface by ~4%, compared to bare areas, thus lowering the increase in subsurface temperature. As a result, ~22% less solar energy can be transformed into sensible heat flux at vegetated surfaces as opposed to bare ground, bringing about a decrease in surface temperature of ~7.8 °C.

show abstract

Impact of highway construction on land surface energy balance and local climate derived from LANDSAT satellite data

Cited by 25 publications

References 53 publications

A Hybrid Model Consisting of Supervised and Unsupervised Learning for Landslide Susceptibility Mapping

A Hybrid Model Consisting of Supervised and Unsupervised Learning for Landslide Susceptibility Mapping

Quantification of the Environmental Impacts of Highway Construction Using Remote Sensing Approach

Mitigation of Arctic Tundra Surface Warming by Plant Evapotranspiration: Complete Energy Balance Component Estimation Using LANDSAT Satellite Data

Contact Info

Product

Resources

About