The Experts below are selected from a list of 47406 Experts worldwide ranked by ideXlab platform
Dan Zhang - One of the best experts on this subject based on the ideXlab platform.
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Spatiotemporal analyses of Urban Vegetation structural attributes using multitemporal Landsat TM data and field measurements
Annals of Forest Science, 2017Co-Authors: Zhibin Ren, Haifeng Zheng, Dan ZhangAbstract:We conducted spatiotemporal analyses of Urban Vegetation structural attributes using multitemporal Landsat TM data and field measurements. We showed that multitemporal TM data has the potential of rapidly estimating Urban Vegetation structural attributes including LAI, CC , and BA at an Urban landscape level. Urban Vegetation structural properties/attributes are closely linked to their ecological functions and thus directly affect Urban ecosystem process such as energy, water, and gas exchange. Understanding spatiotemporal dynamics of Urban Vegetation structures is important for sustaining Urban ecosystem service and improving the Urban environment. The purposes of this study were to evaluate the potential of estimating Urban Vegetation structural attributes from multitemporal Landsat TM imagery and to analyze spatiotemporal changes of the Urban structural attributes. We first collected three scenes of TM images acquired in 1997, 2004, and 2010 and conducted a field survey to collect Urban Vegetation structural data (including crown closure (CC), tree height (H), leaf area index (LAI), basal area (BA), stem density (SD), diameter at breast height (DBH), etc.). We then calculated and normalized NDVI maps from the multitemporal TM images. Finally, spatiotemporal Urban Vegetation structural maps were created using NDVI-based Urban Vegetation structure predictive models. The results show that NDVI can be used as a predictor for some selected Urban Vegetation structural attributes (i.e., CC, LAI, and BA), but not for the other attributes (i.e., H, SD, and DBH) that are well predicted by NDVI in natural Vegetation. The results also indicate that Urban Vegetation structural attributes (i.e., CC, LAI, and BA) in the study area decreased sharply from 1997 to 2004 but increased slightly from 2004 to 2010. The CC, LAI, and BA class distributions were all skewed toward low values in 1997 and 2004. Moreover, LAI, CC, and BA of Urban Vegetation all present a decreasing trend from subUrban areas to Urban central areas. The experimental results demonstrate that Landsat TM imagery could provide a fast and cost-effective method to obtain a spatiotemporal 30-m resolution Urban Vegetation structural dataset (including CC, LAI, and BA).
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Spatiotemporal analyses of Urban Vegetation structural attributes using multitemporal Landsat TM data and field measurements
Annals of Forest Science, 2017Co-Authors: Zhibin Ren, Haifeng Zheng, Dan ZhangAbstract:AbstractKey messageWe conducted spatiotemporal analyses of Urban Vegetation structural attributes using multitemporal Landsat TM data and field measurements. We showed that multitemporal TM data has the potential of rapidly estimating Urban Vegetation structural attributes includingLAI, CC, andBAat an Urban landscape level.ContextUrban Vegetation structural properties/attributes are closely linked to their ecological functions and thus directly affect Urban ecosystem process such as energy, water, and gas exchange. Understanding spatiotemporal dynamics of Urban Vegetation structures is important for sustaining Urban ecosystem service and improving the Urban environment.AimsThe purposes of this study were to evaluate the potential of estimating Urban Vegetation structural attributes from multitemporal Landsat TM imagery and to analyze spatiotemporal changes of the Urban structural attributes.MethodsWe first collected three scenes of TM images acquired in 1997, 2004, and 2010 and conducted a field survey to collect Urban Vegetation structural data (including crown closure (CC), tree height (H), leaf area index (LAI), basal area (BA), stem density (SD), diameter at breast height (DBH), etc.). We then calculated and normalized NDVI maps from the multitemporal TM images. Finally, spatiotemporal Urban Vegetation structural maps were created using NDVI-based Urban Vegetation structure predictive models.ResultsThe results show that NDVI can be used as a predictor for some selected Urban Vegetation structural attributes (i.e., CC, LAI, and BA), but not for the other attributes (i.e., H, SD, and DBH) that are well predicted by NDVI in natural Vegetation. The results also indicate that Urban Vegetation structural attributes (i.e., CC, LAI, and BA) in the study area decreased sharply from 1997 to 2004 but increased slightly from 2004 to 2010. The CC, LAI, and BA class distributions were all skewed toward low values in 1997 and 2004. Moreover, LAI, CC, and BA of Urban Vegetation all present a decreasing trend from subUrban areas to Urban central areas.ConclusionThe experimental results demonstrate that Landsat TM imagery could provide a fast and cost-effective method to obtain a spatiotemporal 30-m resolution Urban Vegetation structural dataset (including CC, LAI, and BA).
Zhibin Ren - One of the best experts on this subject based on the ideXlab platform.
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Spatiotemporal analyses of Urban Vegetation structural attributes using multitemporal Landsat TM data and field measurements
Annals of Forest Science, 2017Co-Authors: Zhibin Ren, Haifeng Zheng, Dan ZhangAbstract:We conducted spatiotemporal analyses of Urban Vegetation structural attributes using multitemporal Landsat TM data and field measurements. We showed that multitemporal TM data has the potential of rapidly estimating Urban Vegetation structural attributes including LAI, CC , and BA at an Urban landscape level. Urban Vegetation structural properties/attributes are closely linked to their ecological functions and thus directly affect Urban ecosystem process such as energy, water, and gas exchange. Understanding spatiotemporal dynamics of Urban Vegetation structures is important for sustaining Urban ecosystem service and improving the Urban environment. The purposes of this study were to evaluate the potential of estimating Urban Vegetation structural attributes from multitemporal Landsat TM imagery and to analyze spatiotemporal changes of the Urban structural attributes. We first collected three scenes of TM images acquired in 1997, 2004, and 2010 and conducted a field survey to collect Urban Vegetation structural data (including crown closure (CC), tree height (H), leaf area index (LAI), basal area (BA), stem density (SD), diameter at breast height (DBH), etc.). We then calculated and normalized NDVI maps from the multitemporal TM images. Finally, spatiotemporal Urban Vegetation structural maps were created using NDVI-based Urban Vegetation structure predictive models. The results show that NDVI can be used as a predictor for some selected Urban Vegetation structural attributes (i.e., CC, LAI, and BA), but not for the other attributes (i.e., H, SD, and DBH) that are well predicted by NDVI in natural Vegetation. The results also indicate that Urban Vegetation structural attributes (i.e., CC, LAI, and BA) in the study area decreased sharply from 1997 to 2004 but increased slightly from 2004 to 2010. The CC, LAI, and BA class distributions were all skewed toward low values in 1997 and 2004. Moreover, LAI, CC, and BA of Urban Vegetation all present a decreasing trend from subUrban areas to Urban central areas. The experimental results demonstrate that Landsat TM imagery could provide a fast and cost-effective method to obtain a spatiotemporal 30-m resolution Urban Vegetation structural dataset (including CC, LAI, and BA).
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Spatiotemporal analyses of Urban Vegetation structural attributes using multitemporal Landsat TM data and field measurements
Annals of Forest Science, 2017Co-Authors: Zhibin Ren, Haifeng Zheng, Dan ZhangAbstract:AbstractKey messageWe conducted spatiotemporal analyses of Urban Vegetation structural attributes using multitemporal Landsat TM data and field measurements. We showed that multitemporal TM data has the potential of rapidly estimating Urban Vegetation structural attributes includingLAI, CC, andBAat an Urban landscape level.ContextUrban Vegetation structural properties/attributes are closely linked to their ecological functions and thus directly affect Urban ecosystem process such as energy, water, and gas exchange. Understanding spatiotemporal dynamics of Urban Vegetation structures is important for sustaining Urban ecosystem service and improving the Urban environment.AimsThe purposes of this study were to evaluate the potential of estimating Urban Vegetation structural attributes from multitemporal Landsat TM imagery and to analyze spatiotemporal changes of the Urban structural attributes.MethodsWe first collected three scenes of TM images acquired in 1997, 2004, and 2010 and conducted a field survey to collect Urban Vegetation structural data (including crown closure (CC), tree height (H), leaf area index (LAI), basal area (BA), stem density (SD), diameter at breast height (DBH), etc.). We then calculated and normalized NDVI maps from the multitemporal TM images. Finally, spatiotemporal Urban Vegetation structural maps were created using NDVI-based Urban Vegetation structure predictive models.ResultsThe results show that NDVI can be used as a predictor for some selected Urban Vegetation structural attributes (i.e., CC, LAI, and BA), but not for the other attributes (i.e., H, SD, and DBH) that are well predicted by NDVI in natural Vegetation. The results also indicate that Urban Vegetation structural attributes (i.e., CC, LAI, and BA) in the study area decreased sharply from 1997 to 2004 but increased slightly from 2004 to 2010. The CC, LAI, and BA class distributions were all skewed toward low values in 1997 and 2004. Moreover, LAI, CC, and BA of Urban Vegetation all present a decreasing trend from subUrban areas to Urban central areas.ConclusionThe experimental results demonstrate that Landsat TM imagery could provide a fast and cost-effective method to obtain a spatiotemporal 30-m resolution Urban Vegetation structural dataset (including CC, LAI, and BA).
Chudong Huang - One of the best experts on this subject based on the ideXlab platform.
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Spatial Modeling of Urban Vegetation and Land Surface Temperature: A Case Study of Beijing
Sustainability, 2015Co-Authors: Chudong HuangAbstract:The coupling relationship between Urban Vegetation and land surface temperature (LST) has been heatedly debated in a variety of environmental studies. This paper studies the Urban Vegetation information and LST by utilizing a series of remote sensing imagery covering the period from 1990 to 2007. Their coupling relationship is analyzed, in order to provide the basis for ecological planning and environment protection. The results show that the normalized difference Vegetation index (NDVI), Urban Vegetation abundance (UVA) and Urban forest abundance (UFA) are negatively correlated with LST, which means that both Urban Vegetation and Urban forest are capable in decreasing LST. The apparent influence of Urban Vegetation and Urban forest on LST varies with the spatial resolution of the imagery, and peaks at the resolutions ranging from 90 m to 120 m
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IGARSS (3) - An analysis on the coupling relationship between Urban Vegetation and land surface temperature in Hangzhou based on ASTER imagery
2009 IEEE International Geoscience and Remote Sensing Symposium, 2009Co-Authors: Chudong Huang, Qianhu Chen, Si'ai Ying, Feng Zhao, Yun Shao, Jinsong Chen, Fuying LiuAbstract:The coupling relationship between Urban Vegetation and land surface temperature (LST) has been of great interest to a variety of environmental studies. This paper retrieves the Urban Vegetation and LST utilizing Terra ASTER imagery in the year 2007, and analyzes their coupling relationships accordingly, so as to provide the basis for decision making of ecological planning and environment protection. It turns out that NDVI, Urban Vegetation abundance (UVA) and Urban forest abundance (UFA) are all in negative correlation with land surface temperature (LST), so that Urban Vegetation and Urban forest are both capable in decreasing LST. The influence of Urban Vegetation and Urban forest varies with pixel aggregation, and peaks around 90m ~ 120m resolution. At the end of this paper, some future efforts on the analysis are pointed out.
Hui Zhang - One of the best experts on this subject based on the ideXlab platform.
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The dust retention capacities of Urban Vegetation—a case study of Guangzhou, South China
Environmental science and pollution research international, 2013Co-Authors: Lu Liu, Dong-sheng Guan, M. R. Peart, Gang Wang, Hui ZhangAbstract:Urban Vegetation increasingly plays an important role in the improvement of the Urban atmospheric environment. This paper deals with the dust retention capacities of four Urban tree species (Ficus virens var. sublanceolata, Ficus microcarpa, Bauhinia blakeana, and Mangifera indica Linn) in Guangzhou. The dust-retaining capacities of four tree species are studied under different pollution intensities and for different seasons. Remote sensing imagery was used to estimate the total aboveground Urban Vegetation biomass in different functional areas of Urban Guangzhou, information that was then used to estimate the dust-retaining capacities of the different functional areas and the total removal of airborne particulates in Urban Guangzhou by foliage. The results showed that Urban Vegetation can remove dust from the atmosphere thereby improving air quality. The major findings are that dust retention, or capture, vary between the four species of tree studied; it also varied between season and between types of Urban functional area, namely industrial, commercial/road traffic, residential, and clean areas. Dust accumulation over time was also studied and reached a maximum, and saturation, after about 24 days. The overall aboveground biomass of Urban Vegetation in Guangzhou was estimated to be 52.0 × 10(5) t, its total leaf area 459.01 km(2), and the dust-retaining capacity was calculated at 8012.89 t per year. The present study demonstrated that the foliage of tree species used in Urban greening make a substantial contribution to atmospheric dust removal and retention in Urban Guangzhou.
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the dust retention capacities of Urban Vegetation a case study of guangzhou south china
Environmental Science and Pollution Research, 2013Co-Authors: Lu Liu, Dong-sheng Guan, M. R. Peart, Gang Wang, Hui ZhangAbstract:Urban Vegetation increasingly plays an important role in the improvement of the Urban atmospheric environment. This paper deals with the dust retention capacities of four Urban tree species (Ficus virens var. sublanceolata, Ficus microcarpa, Bauhinia blakeana, and Mangifera indica Linn) in Guangzhou. The dust-retaining capacities of four tree species are studied under different pollution intensities and for different seasons. Remote sensing imagery was used to estimate the total aboveground Urban Vegetation biomass in different functional areas of Urban Guangzhou, information that was then used to estimate the dust-retaining capacities of the different functional areas and the total removal of airborne particulates in Urban Guangzhou by foliage. The results showed that Urban Vegetation can remove dust from the atmosphere thereby improving air quality. The major findings are that dust retention, or capture, vary between the four species of tree studied; it also varied between season and between types of Urban functional area, namely industrial, commercial/road traffic, residential, and clean areas. Dust accumulation over time was also studied and reached a maximum, and saturation, after about 24 days. The overall aboveground biomass of Urban Vegetation in Guangzhou was estimated to be 52.0 × 10(5) t, its total leaf area 459.01 km(2), and the dust-retaining capacity was calculated at 8012.89 t per year. The present study demonstrated that the foliage of tree species used in Urban greening make a substantial contribution to atmospheric dust removal and retention in Urban Guangzhou.
Damir Medak - One of the best experts on this subject based on the ideXlab platform.
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Spatial video remote sensing for Urban Vegetation mapping using Vegetation indices
Urban Ecosystems, 2020Co-Authors: Luka Rumora, Ivan Majić, Mario Miler, Damir MedakAbstract:Urban Vegetation is important because of the fast-growing Urbanization. If we want cities to have sustainable growth and well-kept ecology, we need to develop a smart and efficient Urban Vegetation monitoring system. This paper examines the possibility of using a modified GoPro camera mounted on a car. The lens of a GoPro camera was replaced with the NDVI-7 lens to obtain blue, green and near-infrared band. The performance of four Vegetation indices was tested: Blue normalized difference Vegetation index (BNDVI), Green normalized difference Vegetation index (GNDVI), Green-blue normalized difference Vegetation index (GBNDVI), Blue-wide dynamic range Vegetation index (BWDRVI). Based on those indices, binary classification was performed to classify objects in the scene as either Vegetation or non-Vegetation. Finally, the accuracy of each index was assessed on three different study sites. Results show that GBNDVI performs best for the given task with average classification accuracy of 95.10% for all study sites.
