The responses of evapotranspiration due to changes of LUCC under seawater intrusion in a coastal region

The paper provides a coherent pattern identification analysis of the impacts of coastal land use and land cover (LULC) on evapotranspiration (ET) under the impact of seawater intrusion. The study applied the Landsat satellite data to characterize the LULC at Laizhou Bay, Shandong Province, China. Then, the ET and heat fluxes were estimated using the surface energy balance algorithm for land model with two-time phase thermal infrared band images and regional surface parameters. This allowed for the eventual linkage of seawater intrusion to land use/cover changes (LUCC) and ET variations over time. The case study discussed in this paper carried out a coastal landscape dynamics assessment using multi-source and multi-sensor remote sensing technologies. The results are: (1) due to its distance from the sea, the vegetation index (modified soil-adjusted vegetation index, MSAVI) gradually increases with the gradual increase of land use grade (Uindex); (2) there are a variety of types of relational patterns between parameters (LST, G(n), MSAVI, and Uindex) and ET (positive, negative, and no relationship); and (3) seawater intrusion significantly affected the spatial pattern of LUCC, which evidently affected the spatial distribution of ET. The spatial distribution pattern and change characteristics of ET were formed by double driving forces of seawater intrusion and LUCC under the background effects of regional climate.The paper provides a coherent pattern identification analysis of the impacts of coastal land use and land cover (LULC) on evapotranspiration (ET) under the impact of seawater intrusion. The study applied the Landsat satellite data to characterize the LULC at Laizhou Bay, Shandong Province, China. Then, the ET and heat fluxes were estimated using the surface energy balance algorithm for land model with two-time phase thermal infrared band images and regional surface parameters. This allowed for the eventual linkage of seawater intrusion to land use/cover changes (LUCC) and ET variations over time. The case study discussed in this paper carried out a coastal landscape dynamics assessment using multi-source and multi-sensor remote sensing technologies. The results are: (1) due to its distance from the sea, the vegetation index (modified soil-adjusted vegetation index, MSAVI) gradually increases with the gradual increase of land use grade (Uindex); (2) there are a variety of types of relational patterns between parameters (LST, G(n), MSAVI, and Uindex) and ET (positive, negative, and no relationship); and (3) seawater intrusion significantly affected the spatial pattern of LUCC, which evidently affected the spatial distribution of ET. The spatial distribution pattern and change characteristics of ET were formed by double driving forces of seawater intrusion and LUCC under the background effects of regional climate

Evapotranspiration analysis based on topography algorithm in the Yellow River Delta

A remote sensing regional evapotranspiration (ET) model was built on the basis of topography correction (slope, aspect and elevation), herein. A variety of satellite data which have visible, near-infrared and thermal infrared remote sensing data can be used by this improved model. Combined with conventional ground meteorological information, it can estimate regional distribution of ET under different climate and terrain conditions, expanding the scope of application. Taking into account the terrain factors, we modified the algorithm of SEBAL model. Results showed that, the modified inversion method of evapotranspiration can better reflect actual evapotranspiration condition. Evapotranspiration changes were consistent with land use types. This research indicates that application of medium or high resolution satellite data to calculate regional ET under undulating landform should consider the impact of terrain. It improves the accuracy of ET estimates and has important reference value for the work of the regional water balance and regional agricultural climate research. © Copyright 2013 SPIE

Analysis trends of ultraviolet B fluxes in the continental US with USDA and TOMS data

Many environmental factors, such as stratospheric ozone, aerosols, and clouds, may affect ultraviolet (UV) irradiance. The aim of this study was to investigate the possible association between ultraviolet B (UVB) radiation and total cloud amount, ozone, and aerosols simultaneously, leading to the assessment of possible impacts of climate change on UVB flux variations in the Continental United States (US). Findings indicate that in the past 22 years, while ozone decreased and aerosols increased across the US, the UVB decrease in the northern states was consistent with the increase in aerosols and total cloud amount. Climate change impact resulting in higher total cloud amount in the northern states might result in lower UVB in the future. © Copyright 2013 SPIE

Concomitant flow and space variations of evapotranspiration due to changes in LUCC under seawater intrusion in a coastal region

This paper provides a coherent pattern identification analysis of coastal land use and land cover (LULC) under the impact of seawater intrusion. This study analysis applied the 4-, 3-, and 2-band false color composite Landsat satellite data to characterize the LULC in the study area. The evapotranspiration (ET) and heat fluxes were estimated by using the SEBAL model with two-time phase thermal infrared band images and regional surface parameters. Our findings are as follows: 1) Due to its distance from the sea, the vegetation index gradually increases as the level of land use gradually increased. 2) The different influences of seawater intrusion in the study area resulted in significantly different influences of land surface parameters (LST, Gn, MSAVI, and Uindex) on ET. There are a variety of types of relational patterns between parameters (LST, Gn, MSAVI and Uindex) and ET (positive, negative, and no relationship). 3) Seawater intrusion significantly affected the spatial pattern of LUCC, which evidently affected the spatial distribution of ET. The spatial distribution pattern and change characteristics of ET were formed by double driving forces of seawater intrusion and LUCC under the background effects of regional climate. © Copyright 2013 SPIE

Characterizing land condition variability in Northern China from 1982 to 2011

For the last three decades, Northern China has been considered as one of the most sensitive areas regarding global environmental change. The integration of AVHRR GIMMS and MODIS NDVI data (1982-2011), of which for the overlapping period of 2000-2006 show good consistency, were used for characterizing land condition variability. The trends of standardized annually I NDVI pound, temperature, precipitation and PDSI were obtained using a linear regression model. The results showed that Northern China has a general increase in greenness for the period 1982-2011 (a = 0.05). Also, annually I NDVI pound is significantly correlated with temperature and precipitation data at the regional scale (p < 0.05), implying that temperature and precipitation are the dominant limiting factors for vegetation growth. Since the greening is not uniform, factors other than temperature and precipitation may contribute to greening in some areas, while the grassland and cropland ecosystem are becoming increasingly vulnerable to drought. The results of trend analysis indicate that greenness seems to be evident in most of the study areas.For the last three decades, Northern China has been considered as one of the most sensitive areas regarding global environmental change. The integration of AVHRR GIMMS and MODIS NDVI data (1982-2011), of which for the overlapping period of 2000-2006 show good consistency, were used for characterizing land condition variability. The trends of standardized annually I NDVI pound, temperature, precipitation and PDSI were obtained using a linear regression model. The results showed that Northern China has a general increase in greenness for the period 1982-2011 (a = 0.05). Also, annually I NDVI pound is significantly correlated with temperature and precipitation data at the regional scale (p < 0.05), implying that temperature and precipitation are the dominant limiting factors for vegetation growth. Since the greening is not uniform, factors other than temperature and precipitation may contribute to greening in some areas, while the grassland and cropland ecosystem are becoming increasingly vulnerable to drought. The results of trend analysis indicate that greenness seems to be evident in most of the study areas

Trend analysis for evaluating the consistency of Terra MODIS and SPOT VGT NDVI time series products in China

The Normalized Difference Vegetation Index (NDVI) is an important vegetation greenness indicator. Compared to the AVHRR GIMMS NDVI data, the availability of two datasets with 1 km spatial resolution, i.e., Terra MODIS (MOD13A3) monthly composite and SPOT Vegetation (VGT) 10-day composite NDVI, extends the application dimensions at spatial and temporal scales. An overlapping period of 12 years between the datasets now makes it possible to investigate the consistency of the two datasets. Linear regression trend analysis was performed to compare the two datasets in this study. The results show greater consistency in regression slopes in the semi-arid regions of northern China. Alternatively, the results show only slight changes in the Terra MODIS NDVI regression slope in most areas of southern China whereas the SPOT VGT NDVI shows positive changes over a large area. The corresponding regression slope values between Terra MODIS and SPOT VGT NDVI datasets from the linear fit had a fair agreement in the spatial dimension. However, larger positive and negative differences were observed at the junction of the three regions (East China, Central China, and North China). These differences can be partially explained by the positive standard deviation differences distributed over a large area at the junction of these three regions. This study demonstrated that Terra MODIS and SPOT VGT NDVI have a relatively robust basis for characterizing vegetation changes in annual NDVI in most of the semi-arid and arid regions in northern China.The Normalized Difference Vegetation Index (NDVI) is an important vegetation greenness indicator. Compared to the AVHRR GIMMS NDVI data, the availability of two datasets with 1 km spatial resolution, i.e., Terra MODIS (MOD13A3) monthly composite and SPOT Vegetation (VGT) 10-day composite NDVI, extends the application dimensions at spatial and temporal scales. An overlapping period of 12 years between the datasets now makes it possible to investigate the consistency of the two datasets. Linear regression trend analysis was performed to compare the two datasets in this study. The results show greater consistency in regression slopes in the semi-arid regions of northern China. Alternatively, the results show only slight changes in the Terra MODIS NDVI regression slope in most areas of southern China whereas the SPOT VGT NDVI shows positive changes over a large area. The corresponding regression slope values between Terra MODIS and SPOT VGT NDVI datasets from the linear fit had a fair agreement in the spatial dimension. However, larger positive and negative differences were observed at the junction of the three regions (East China, Central China, and North China). These differences can be partially explained by the positive standard deviation differences distributed over a large area at the junction of these three regions. This study demonstrated that Terra MODIS and SPOT VGT NDVI have a relatively robust basis for characterizing vegetation changes in annual NDVI in most of the semi-arid and arid regions in northern China