Impacts of land use and land cover changes on regional climate in the Lhasa River basin, Tibetan Plateau

Li, Dan; Tian, Peipei; Luo, Hao; Hu, Tiesong; Dong, Bin; Cui, Yuanlai; Khan, Shahbaz; Luo, Yufeng

doi:10.1016/j.scitotenv.2020.140570

Cited by 74 publications

(28 citation statements)

References 39 publications

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“…This was consistent with Li et al. (2020), who reported that afforestation construction (usually with larger LAI) resulted in an increase of approximately 20 W m −2 of Rn. Previous studies have reported that the Ts changes depend on the balance of the radiative effect (Rn changes) and the nonradiative effect (changes of ET and surface roughness) (Davin and de Noblet Ducoudré, 2010; Lee et al., 2011).…”

Section: Resultssupporting

confidence: 92%

“…The land use/land cover (LULC) is one key component of environmental conditions and land surface characteristics. Changes of LULC alter the exchange of water and energy fluxes between land and atmosphere and hence impact the meteorological conditions at local/regional scales (Chen et al., 2017; Kharol et al., 2013; Li et al., 2020; Mahmood et al., 2014; Pielke et al., 2011; Prijith et al., 2020). Even minor changes in land surface characteristics could lead to significant climatic responses (Cao et al., 2016; Zhao & Wu, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Effects of Mosaic Representation of Land Use/Land Cover on Skin Temperature and Energy Fluxes in Noah‐MP Land Surface Model Over China

Guo

Zhou

et al. 2021

JGR Atmospheres

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The representations of land use/land cover (LULC) play an important role in land surface models (LSMs) for the simulation of the energy flux partition, soil moisture redistribution, and runoff generation. This study was designed to investigate the regional effects of mosaic LULC representations on skin temperature (Ts) and energy fluxes over China at three horizontal resolutions and how these effects changed with climate regimes, using Noah with multiparameterization (Noah‐MP) LSM. The current officially released Noah‐MP only considered the most abundant LULC type within one model grid. In this study, the mosaic method considering all the LULC types existing in one model grid was implemented into Noah‐MP. Against the reference data (including MODIS land surface temperature products, FLUXCOM energy flux data and Numerical Terra dynamic Simulation Group evapotranspiration data), the mosaic method generally performed better than the default method and reduced the root‐mean‐squared‐error of Ts and energy fluxes significantly over urban region. The mosaic method affected the Ts and energy fluxes by changing leaf area index and soil moisture, mainly by the former. The warm (monthly mean air temperature larger than 10 °C) and relatively humid climate (annual total precipitation larger than 200 mm) could enlarge the effect of mosaic method on Ts and energy fluxes. The mosaic method reduced discrepancies of Ts and energy fluxes among three horizontal resolutions (0.0625°, 0.25°, and 0.50°), especially over the heterogeneous vegetated and urban region.

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Section: Resultssupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Effects of Mosaic Representation of Land Use/Land Cover on Skin Temperature and Energy Fluxes in Noah‐MP Land Surface Model Over China

Guo

Zhou

et al. 2021

JGR Atmospheres

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“…The main crops are wheat, oilseed rape, highland barley, oats, maize, broad beans, and potatoes. According to the research background presented above, and based on the GEE cloud platform, Sentinel-2 remote sensing images were used to produce the land cover classification products for the QLM, with a spatial resolution of 10 m. Specifically, the research content of this study is as follows: (1) to evaluate the performance of the GEE cloud platform in land cover research in the QLM; (2) to assess the performances and accuracies of the different remote sensing classification algorithms in the land cover classification of the QLM; (3) to analyze the impacts of different characteristic variable combinations on the classification results while participating in classification; and (4) to compare and analyze the classification results obtained in this study using existing land cover products.…”

Section: Study Areamentioning

confidence: 99%

“…With the advancement in multisource remote sensing data, land cover and its changes have been closely studied in recent years [2]. Accurate and reliable land cover data is indispensable, as it provides basic information related to scientific research in areas such as agricultural, environmental protection, and global change [3][4][5][6]. However, accurate land cover mapping and its changes are still facing many challenges due to land surface heterogeneity and spectral confusion, especially in higher-resolution mapping.…”

Section: Introductionmentioning

confidence: 99%

Testing Accuracy of Land Cover Classification Algorithms in the Qilian Mountains Based on GEE Cloud Platform

et al. 2021

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The Qilian Mountains (QLM) are an important ecological barrier in western China. High-precision land cover data products are the basic data for accurately detecting and evaluating the ecological service functions of the QLM. In order to study the land cover in the QLM and performance of different remote sensing classification algorithms for land cover mapping based on the Google Earth Engine (GEE) cloud platform, the higher spatial resolution remote sensing images of Sentinel-1 and Sentinel-2; digital elevation data; and three remote sensing classification algorithms, including the support vector machine (SVM), the classification regression tree (CART), and the random forest (RF) algorithms, were used to perform supervised classification of Sentinel-2 images of the QLM. Furthermore, the results obtained from the classification process were compared and analyzed by using different remote sensing classification algorithms and feature-variable combinations. The results indicated that: (1) the accuracy of the classification results acquired by using different remote sensing classification algorithms were different, and the RF had the highest classification accuracy, followed by the CART and the SVM; (2) the different feature variable combinations had different effects on the overall accuracy (OA) of the classification results and the performance of the identification and classification of the different land cover types; and (3) compared with the existing land cover products for the QLM, the land cover maps obtained in this study had a higher spatial resolution and overall accuracy.

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“…Land use and land cover directly reflect the impacts of human activities on the eco-environment [58]. One of the most obvious impacts is that on the vegetation coverage [59].…”

Section: ) the Construction Of Land Coverage Indicatorsmentioning

confidence: 99%

Water Benefit-Based Ecological Index for Urban Ecological Environment Quality Assessments

Jiao

Sun

Zhang

et al. 2021

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

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Urbanization and climate change cause the urban ecological environment to become increasingly dependent on water. However, open water areas and green spaces in cities are constantly decreasing, making water resources increasingly scarce. There is an urgent need for a method that aligns with the current urban status and can quickly assess the urban ecologicalenvironmental quality (UEEQ). Traditional UEEQ methods have abandoned the water factor, neglecting the influence of water on the ecological environment. In modern cities, water, which guarantees the operation and maintenance of the urban ecological environment, must be considered in the UEEQ system. Therefore, we propose a water benefit-based ecological index (WBEI). In the formulation of the WBEI, we integrate a water eco-factor, the thermal environment and the land cover type to represent the surface ecological environment. We first construct a surface potential water abundance index (SPWI) to describe the spatial distribution of water. The combination of the SPWI and the normalized difference latent heat index (NDLI) allows the WBEI to better evaluate the UEEQ around water areas. Then, we choose the land surface temperature (LST) to represent the thermal environment. To represent the land cover type, the ratio vegetation index (RVI) and the normalized difference soil index (NDSI) are adopted in the WBEI. Finally, we use an entropy-based fusion method to fuse these indicators and obtain the WBEI values. The performance of the WBEI is tested using eight datasets with a variety of environmental characteristics. The results show that 75% of the WBEI results are consistent with the EI values. The correlation coefficient between the WBEI and EI is 0.8883, which is significantly better than those of the other methods. The research shows that the UEEQ of the Qingdao West Coast Economic New Zone is declining continuously at a rate of 3.7% per year. From 2013 to 2017, the percentage of areas with good environments decreased by 21.46%, and the percentage of areas with poor environments increased by 12.76%. The UEEQ inside the city deteriorated radially outward along the main traffic route, the UEEQ in the suburbs did not change significantly, and the UEEQ in the water areas deteriorated significantly. These relevant research results can provide quantitative information for the green sustainable development of cities.

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Impacts of land use and land cover changes on regional climate in the Lhasa River basin, Tibetan Plateau

Cited by 74 publications

References 39 publications

Effects of Mosaic Representation of Land Use/Land Cover on Skin Temperature and Energy Fluxes in Noah‐MP Land Surface Model Over China

Effects of Mosaic Representation of Land Use/Land Cover on Skin Temperature and Energy Fluxes in Noah‐MP Land Surface Model Over China

Testing Accuracy of Land Cover Classification Algorithms in the Qilian Mountains Based on GEE Cloud Platform

Water Benefit-Based Ecological Index for Urban Ecological Environment Quality Assessments

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