<div style="text-align:justify;"> This research is based on Landsat5 TM, Landsat8 OLI/TIRS remote sensing data using RSEI model to analyze and monitor the ecological environment and its temporal and sp...<div style="text-align:justify;"> This research is based on Landsat5 TM, Landsat8 OLI/TIRS remote sensing data using RSEI model to analyze and monitor the ecological environment and its temporal and spatial changes in the forest-grass transition zone in Northeast China from 2004 to 2019. The change characteristics of the ecological environment of different types of land cover types are monitored by RSEI method, and the response of different land cover types to natural factors such as precipitation and temperature is analyzed at the same time. The distribution of RSEI in the study area presents the characteristics of high in the east and low in the west. The eastern mountainous area is densely covered with woodland, which is the area with the best ecological environment quality in the study area. The grassland in the western plain and the saline-alkali land around the river are the areas with poor ecological environment in the study area. Climate, precipitation, topography and other natural elements work together to form the quality of the ecological environment in the study area roughly bounded by 120?E. In years with poor natural conditions, this dividing line will have a clear eastward shifting trend, especially in the northern part of the study area. The spatial distribution of RSEI in the study area has a high degree of spatial autocorrelation, and Global Moran’s I has been above 0.8 over the years. In terms of temporal changes in ecological conditions, the ecological environment in the study area was basically stable from 2004 to 2008, with a slight deterioration;it improved significantly from 2008 to 2011;however, it deteriorated significantly from 2011 to 2019. According to the results of partial correlation analysis, the ecological environment of the former is highly correlated with natural elements such as climate and precipitation, while the latter is mainly affected by human factors. </div>展开更多
The aim of this research is to map the salt-affected soil in an arid environment using an advanced semi-empirical predictive model, Operational Land Imager (OLI) data, a digital elevation model (DEM), field soil sampl...The aim of this research is to map the salt-affected soil in an arid environment using an advanced semi-empirical predictive model, Operational Land Imager (OLI) data, a digital elevation model (DEM), field soil sampling, and laboratory and statistical analyses. To achieve our objectives, the OLI data were atmospherically corrected, radiometric sensor drift was calibrated, and distortions of topography and geometry were corrected using a DEM. Then, the soil salinity map was derived using a semi-empirical predictive model based on the Soil Salinity and Sodicity Index-2 (SSSI-2). The vegetation cover map was extracted from the Transformed Difference Vegetation Index (TDVI). In addition, accurate DEM of 5-m pixels was used to derive topographic attributes (elevation and slope). Visual comparisons and statistical validation of the semi-empirical model using ground truth were undertaken in order to test its capability in an arid environment for moderate and strong salinity mapping. To accomplish this step, fieldwork was organized and 120 soil samples were collected with various degrees of salinity, including non-saline soil samples. Each one was automatically labeled using a digital camera and an accurate global positioning system (GPS) survey (σ ≤ ± 30 cm) connected in real time to the geographic information system (GIS) database. Subsequently, in the laboratory, the major exchangeable cations (Ca2+, Mg2+, Na+, K+, Cl- and SO42-), pH and the electrical conductivity (EC-Lab) were extracted from a saturated soil paste, as well as the sodium adsorption ratio (SAR) being calculated. The EC-Lab, which is generally accepted as the most effective method for soil salinity quantification was used for statistical analysis and validation purposes. The obtained results demonstrated a very good conformity between the derived soil salinity map from OLI data and the ground truth, highlighting six major salinity classes: Extreme, very high, high, moderate, low and non-saline. The laboratory chemical analyses corroborate these results. Furthermore, the semi-empirical predictive model provides good global results in comparison to the ground truth and laboratory analysis (EC-Lab), with correlation coefficient (R2) of 0.97, an index of agreement (D) of 0.84 (p < 0.05), and low overall root mean square error (RMSE) of 11%. Moreover, we found that topographic attributes have a substantial impact on the spatial distribution of salinity. The areas at a relatively high altitude and with hard bedrock are less susceptible to salinity, while areas at a low altitude and slope (≤2%) composed of Quaternary soil are prone to it. In these low areas, the water table is very close to the surface (≤1 m), and the absence of an adequate drainage network contributes significantly to waterlogging. Consequently, the intrusion and emergence of seawater at the surface, coupled with high temperature and high evaporation rates, contribute extensively to the soil salinity in the study area.展开更多
It is widely accepted that natural resources should only be sustainably exploited and utilized to effectively preserve our planet for future generations.To better manage the natural resources,and to better understand ...It is widely accepted that natural resources should only be sustainably exploited and utilized to effectively preserve our planet for future generations.To better manage the natural resources,and to better understand the closely linked Earth systems,the concept of Digital Earth has been strongly promoted since US Vice President Al Gore’s speech in 1998.One core element of Digital Earth is the use and integration of remote sensing data.Only satellite imagery can cover the entire globe repeatedly at a sufficient high-spatial resolution to map changes in land cover and land use,but also to detect more subtle changes related for instance to climate change.To uncover global change effects on vegetation activity and phenology,it is important to establish high quality time series characterizing the past situation against which the current state can be compared.With the present study we describe a time series of vegetation activity at 10-daily time steps between 1998 and 2008 covering large parts of South America at 1 km spatial resolution.Particular emphasis was put on noise removal.Only carefully filtered time series of vegetation indices can be used as a benchmark and for studying vegetation dynamics at a continental scale.Without temporal smoothing,subtle spatio-temporal patterns in vegetation composition,density and phenology would be hidden by atmospheric noise and undetected clouds.Such noise is immanent in data that have undergone solely a maximum value compositing.Within the present study,the Whittaker smoother(WS)was applied to a SPOT VGT time series.The WS balances the fidelity to the observations with the roughness of the smoothed curve.The algorithm is extremely fast,gives continuous control over smoothness with only one parameter,and interpolates automatically.The filtering efficiently removed the negatively biased noise present in the original data,while preserving the overall shape of the curves showing vegetation growth and development.Geostatistical variogram analysis revealed a significantly increased signal-to-noise ratio compared to the raw data.Analysis of the data also revealed spatially consistent key phenological markers.Extracted seasonality parameters followed a clear meridional trend.Compared to the unfiltered data,the filtered time series increased the separability of various land cover classes.It is thus expected that the data set holds great potential for environmental and vegetation related studies within the frame of Digital Earth.展开更多
文摘<div style="text-align:justify;"> This research is based on Landsat5 TM, Landsat8 OLI/TIRS remote sensing data using RSEI model to analyze and monitor the ecological environment and its temporal and spatial changes in the forest-grass transition zone in Northeast China from 2004 to 2019. The change characteristics of the ecological environment of different types of land cover types are monitored by RSEI method, and the response of different land cover types to natural factors such as precipitation and temperature is analyzed at the same time. The distribution of RSEI in the study area presents the characteristics of high in the east and low in the west. The eastern mountainous area is densely covered with woodland, which is the area with the best ecological environment quality in the study area. The grassland in the western plain and the saline-alkali land around the river are the areas with poor ecological environment in the study area. Climate, precipitation, topography and other natural elements work together to form the quality of the ecological environment in the study area roughly bounded by 120?E. In years with poor natural conditions, this dividing line will have a clear eastward shifting trend, especially in the northern part of the study area. The spatial distribution of RSEI in the study area has a high degree of spatial autocorrelation, and Global Moran’s I has been above 0.8 over the years. In terms of temporal changes in ecological conditions, the ecological environment in the study area was basically stable from 2004 to 2008, with a slight deterioration;it improved significantly from 2008 to 2011;however, it deteriorated significantly from 2011 to 2019. According to the results of partial correlation analysis, the ecological environment of the former is highly correlated with natural elements such as climate and precipitation, while the latter is mainly affected by human factors. </div>
文摘The aim of this research is to map the salt-affected soil in an arid environment using an advanced semi-empirical predictive model, Operational Land Imager (OLI) data, a digital elevation model (DEM), field soil sampling, and laboratory and statistical analyses. To achieve our objectives, the OLI data were atmospherically corrected, radiometric sensor drift was calibrated, and distortions of topography and geometry were corrected using a DEM. Then, the soil salinity map was derived using a semi-empirical predictive model based on the Soil Salinity and Sodicity Index-2 (SSSI-2). The vegetation cover map was extracted from the Transformed Difference Vegetation Index (TDVI). In addition, accurate DEM of 5-m pixels was used to derive topographic attributes (elevation and slope). Visual comparisons and statistical validation of the semi-empirical model using ground truth were undertaken in order to test its capability in an arid environment for moderate and strong salinity mapping. To accomplish this step, fieldwork was organized and 120 soil samples were collected with various degrees of salinity, including non-saline soil samples. Each one was automatically labeled using a digital camera and an accurate global positioning system (GPS) survey (σ ≤ ± 30 cm) connected in real time to the geographic information system (GIS) database. Subsequently, in the laboratory, the major exchangeable cations (Ca2+, Mg2+, Na+, K+, Cl- and SO42-), pH and the electrical conductivity (EC-Lab) were extracted from a saturated soil paste, as well as the sodium adsorption ratio (SAR) being calculated. The EC-Lab, which is generally accepted as the most effective method for soil salinity quantification was used for statistical analysis and validation purposes. The obtained results demonstrated a very good conformity between the derived soil salinity map from OLI data and the ground truth, highlighting six major salinity classes: Extreme, very high, high, moderate, low and non-saline. The laboratory chemical analyses corroborate these results. Furthermore, the semi-empirical predictive model provides good global results in comparison to the ground truth and laboratory analysis (EC-Lab), with correlation coefficient (R2) of 0.97, an index of agreement (D) of 0.84 (p < 0.05), and low overall root mean square error (RMSE) of 11%. Moreover, we found that topographic attributes have a substantial impact on the spatial distribution of salinity. The areas at a relatively high altitude and with hard bedrock are less susceptible to salinity, while areas at a low altitude and slope (≤2%) composed of Quaternary soil are prone to it. In these low areas, the water table is very close to the surface (≤1 m), and the absence of an adequate drainage network contributes significantly to waterlogging. Consequently, the intrusion and emergence of seawater at the surface, coupled with high temperature and high evaporation rates, contribute extensively to the soil salinity in the study area.
文摘It is widely accepted that natural resources should only be sustainably exploited and utilized to effectively preserve our planet for future generations.To better manage the natural resources,and to better understand the closely linked Earth systems,the concept of Digital Earth has been strongly promoted since US Vice President Al Gore’s speech in 1998.One core element of Digital Earth is the use and integration of remote sensing data.Only satellite imagery can cover the entire globe repeatedly at a sufficient high-spatial resolution to map changes in land cover and land use,but also to detect more subtle changes related for instance to climate change.To uncover global change effects on vegetation activity and phenology,it is important to establish high quality time series characterizing the past situation against which the current state can be compared.With the present study we describe a time series of vegetation activity at 10-daily time steps between 1998 and 2008 covering large parts of South America at 1 km spatial resolution.Particular emphasis was put on noise removal.Only carefully filtered time series of vegetation indices can be used as a benchmark and for studying vegetation dynamics at a continental scale.Without temporal smoothing,subtle spatio-temporal patterns in vegetation composition,density and phenology would be hidden by atmospheric noise and undetected clouds.Such noise is immanent in data that have undergone solely a maximum value compositing.Within the present study,the Whittaker smoother(WS)was applied to a SPOT VGT time series.The WS balances the fidelity to the observations with the roughness of the smoothed curve.The algorithm is extremely fast,gives continuous control over smoothness with only one parameter,and interpolates automatically.The filtering efficiently removed the negatively biased noise present in the original data,while preserving the overall shape of the curves showing vegetation growth and development.Geostatistical variogram analysis revealed a significantly increased signal-to-noise ratio compared to the raw data.Analysis of the data also revealed spatially consistent key phenological markers.Extracted seasonality parameters followed a clear meridional trend.Compared to the unfiltered data,the filtered time series increased the separability of various land cover classes.It is thus expected that the data set holds great potential for environmental and vegetation related studies within the frame of Digital Earth.
