Combined with remote sensing data and meteorological data, cold damage risk was assessed for planting area of double cropping rice (DCR) in Hunan Province, China. A new methodology of cold damage risk assessment was b...Combined with remote sensing data and meteorological data, cold damage risk was assessed for planting area of double cropping rice (DCR) in Hunan Province, China. A new methodology of cold damage risk assessment was built that apply to grid and have clear hazard-affected body. Each station cold damage annual frequency and average annual intensity of cold damage was calculated by using 1951-2010 station daily mean temperature and simple cold damage identification index. On this basis, average annual cold damage risk index was obtained by their product. The spatial analysis models of cold damage risk index about double-season early rice (DSER) and double-season later rice (DSLR) were established respectively by the relation of average annual cold damage risk index and its geographic factors. Critical threshold of level of average annual cold damage risk index for DSER and DSLR were respectively divided by the correlative equation of cold damage annual frequency and average annual intensity of cold damage. 2001-2010 planting area of DCR, acquired by time series analysis of MOD09A1 8-d composite land surface reflectance product, was as target of assessment. The results show average annual intensity of cold damage is exponential function of cold damage annual frequency, average annual cold damage risk index is directly proportional to cold damage cumulant and cold damage annual frequency, and is inversely proportional to happen times of cold damage and the square of statistical time sequence length. Cold damage risk of DSER is higher than DSLR in Hunan Province. In the 10-yr stacking map, DCR planting in low risk area accounted for 11.92% of total extraction area, in moderate risk area accounted for 69.62%, in high risk area accounted for 18.46%. According to the cold damage risk assessment result, DCR production can be guided to reduce cold damage losses.展开更多
The radial basis function (RBF) emerged as a variant of artificial neural network. Generalized regression neural network (GRNN) is one type of RBF, and its principal advantages are that it can quickly learn and rapidl...The radial basis function (RBF) emerged as a variant of artificial neural network. Generalized regression neural network (GRNN) is one type of RBF, and its principal advantages are that it can quickly learn and rapidly converge to the optimal regression surface with large number of data sets. Hyperspectral reffectance (350 to 2500 nm) data were recorded at two different rice sites in two experiment fields with two cultivars, three nitrogen treatments and one plant density (45 plants m-2). Stepwise multivariable regression model (SMR) and RBF were used to compare their predictability for the leaf area index (LAI) and green leaf chlorophyll density (GLCD) of rice based on reffectance (R) and its three different transformations, the first derivative reffectance (D1), the second derivative reffectance (D2) and the log-transformed re?ectance (LOG). GRNN based on D1 was the best model for the prediction of rice LAI and GLCD. The relationships between different transformations of reffectance and rice parameters could be further improved when RBF was employed. Owing to its strong capacity for nonlinear mapping and good robustness, GRNN could maximize the sensitivity to chlorophyll content using D1. It is concluded that RBF may provide a useful exploratory and predictive tool for the estimation of rice biophysical parameters.展开更多
Landslide susceptibility map is one of the study fields portraying the spatial distribution of future slope failure sus- ceptibility. This paper deals with past methods for producing landslide susceptibility map and d...Landslide susceptibility map is one of the study fields portraying the spatial distribution of future slope failure sus- ceptibility. This paper deals with past methods for producing landslide susceptibility map and divides these methods into 3 types. The logistic linear regression approach is further elaborated on by crosstabs method, which is used to analyze the relationship between the categorical or binary response variable and one or more continuous or categorical or binary explanatory variables derived from samples. It is an objective assignment of coefficients serving as weights of various factors under considerations while expert opinions make great difference in heuristic approaches. Different from deterministic approach, it is very applicable to regional scale. In this study, double logistic regression is applied in the study area. The entire study area is first analyzed. The logistic regression equation showed that elevation, proximity to road, river and residential area are main factors triggering land- slide occurrence in this area. The prediction accuracy of the first landslide susceptibility map was showed to be 80%. Along the road and residential area, almost all areas are in high landslide susceptibility zone. Some non-landslide areas are incorrectly divided into high and medium landslide susceptibility zone. In order to improve the status, a second logistic regression was done in high landslide susceptibility zone using landslide cells and non-landslide sample cells in this area. In the second logistic regression analysis, only engineering and geological conditions are important in these areas and are entered in the new logistic regression equation indicating that only areas with unstable engineering and geological conditions are prone to landslide during large scale engineering activity. Taking these two logistic regression results into account yields a new landslide susceptibility map. Double logistic regression analysis improved the non-landslide prediction accuracy. During calculation of parameters for logistic regres- sion, landslide density is used to transform nominal variable to numeric variable and this avoids the creation of an excessively high number of dummy variables.展开更多
There is increasing interest in using hyperspectral data for quantitative characterization of vegetation in spatial and temporal scopes. Many spectral indices are being developed to improve vegetation sensitivity by m...There is increasing interest in using hyperspectral data for quantitative characterization of vegetation in spatial and temporal scopes. Many spectral indices are being developed to improve vegetation sensitivity by minimizing the background influence. The chlorophyll absorption continuum index (CACI) is such a measure to calculate the spectral continuum on which the analyses are based on the area of the troughs spanned by the spectral continuum. However, different values of CACI were obtained in this method because different positions of continuums were determined by different users. Furthermore, the sensitivity of CACI to agronomic parameters such as green leaf chlorophyll density (GLCD) has been reduced because the fixed positions of con- tinuums are determined when the red edge shifted with the change in GLCD. A modified chlorophyll absorption continuum index (MCACI) is presented in this article. The red edge inflection point (REIP) replaces the maximum reflectance point (MRP) in near-infrared (NIR) shoulder on the CACI continuum. This MCACI has been proved to increase the sensitivity and predictive power of GLCD.展开更多
This paper presents a Zhejiang Province southeastern China seasonal temperature model based on GIS techniques. Terrain variables derived from the 1 km resolution DEM are used as predictors of seasonal temperature, usi...This paper presents a Zhejiang Province southeastern China seasonal temperature model based on GIS techniques. Terrain variables derived from the 1 km resolution DEM are used as predictors of seasonal temperature, using a regression-based approach. Variables used for modelling include: longitude, latitude, elevation, distance from the nearest coast, direction to the nearest coast, slope, aspect, and the ratio of land to sea within given radii. Seasonal temperature data, for the observation period 1971 to 2000, were obtained from 59 meteorological stations. Temperature data from 52 meteorological stations were used to construct the regression model. Data from the other 7 stations were retained for model validation. Seasonal temperature surfaces were constructed using the regression equations, and refined by kriging the residuals from the regression model and subtracting the result from the predicted surface. Latitude, elevation and distance from the sea are found to be the most important predictors of local seasonal temperature. Validation determined that regression plus kriging predicts seasonal temperature with a coefficient of determination (R2), between the estimated and observed values, of 0.757 (autumn) and 0.935 (winter). A simple regression model without kriging yields less accurate results in all seasons except for the autumn temperature.展开更多
Hyperspectral reflectance (350~2500 nm) data were recorded at two different sites of rice in two experiment fields including two cultivars, and three levels of nitrogen (N) application. Twenty-five Vegetation Indices ...Hyperspectral reflectance (350~2500 nm) data were recorded at two different sites of rice in two experiment fields including two cultivars, and three levels of nitrogen (N) application. Twenty-five Vegetation Indices (VIs) were used to predict the rice agronomic parameters including Leaf Area Index (LAI, m2 green leaf/m2 soil) and Green Leaf Chlorophyll Density (GLCD, mg chlorophyll/m2 soil) by the traditional regression models and Radial Basis Function Neural Network (RBF). RBF emerged as a variant of Artificial Neural Networks (ANNs) in the late 1980’s. A large variety of training algorithms has been tested for training RBF networks. In this study, Original RBF (ORBF), Gradient Descent RBF (GDRBF), and Generalized Regression Neural Network (GRNN) were employed. Results showed that green waveband Normalized Difference Vegetation Index (NDVIgreen) and TCARI/OSAVI have the best prediction power for LAI by exponent model and ORBF respectively, and that TCARI/OSAVI has the best prediction power for GLCD by exponent model and GDRBF. The best performances of RBF are compared with the traditional models, showing that the relationship between VIs and agronomic variables are further improved when RBF is used. Compared with the best traditional models, ORBF using TCARI/OSAVI improves the prediction power for LAI by lowering the Root Mean Square Error (RMSE) for 0.1119, and GDRBF using TCARI/OSAVI improves the prediction power for GLCD by lowering the RMSE for 26.7853. It is concluded that RBF provides a useful exploratory and predictive tool when applied to the sensitive VIs.展开更多
Large-scale farming of agriculture crops requires real-time detection of disease for field pest management. Hyperspectral remote sensing data generally have high spectral resolution, which could be very useful for det...Large-scale farming of agriculture crops requires real-time detection of disease for field pest management. Hyperspectral remote sensing data generally have high spectral resolution, which could be very useful for detecting disease stress in green vegetation at the leaf and canopy levels. In this study, hyperspectral reflectances of rice in the laboratory and field were measured to characterize the spectral regions and wavebands, which were the most sensitive to rice brown spot infected by Bipolaris oryzae (Helminthosporium oryzae Breda. de Hann). Leaf reflectance increased at the ranges of 450 to 500 nm and 630 to 680 nm with the increasing percentage of infected leaf surface, and decreased at the ranges of 520 to 580 nm, 760 to 790 nm, 1550 to 1750 nm, and 2080 to 2350 nm with the increasing percentage of infected leaf surface respectively. The sensitivity analysis and derivative technique were used to select the sensitive wavebands for the detection of rice brown spot infected by B. oryzae. Ratios of rice leaf reflectance were evaluated as indicators of brown spot. R669/R746 (the reflectance at 669 nm divided by the reflectance at 746 nm, the following ratios may be deduced by analogy), R702/R718, R692/R530, R692/R732, R535/R746, R521/R718, and R569/R718 increased significantly as the incidence of rice brown spot increased regardless of whether it’s at the leaf or canopy level. R702/R718, R692/R530, R692/R732 were the best three ratios for estimating the disease severity of rice brown spot at the leaf and canopy levels. This result not only confirms the capability of hyperspectral remote sensing data in characterizing crop disease for precision pest management in the real world, but also testifies that the ratios of crop reflectance is a useful method to estimate crop disease severity.展开更多
Tea(Camellia sinensis) is one of the most valuable cash crops in southern China;however,the planting distribution of tea crops is not optimal and the production and cultivation regions of tea crops are restricted by l...Tea(Camellia sinensis) is one of the most valuable cash crops in southern China;however,the planting distribution of tea crops is not optimal and the production and cultivation regions of tea crops are restricted by law and custom.In order to evaluate the suitability of tea crops in Zhejiang Province,the annual mean temperature,the annual accumulated temperature above 10 C,the frequency of extremely low temperature below 13 C,the mean humidity from April to October,slope,aspect,altitude,soil type,and soil texture were selected from climate,topography,and soil factors as factors for land ecological evaluation by the Delphi method based on the ecological characteristics of tea crops.These nine factors were quantitatively analyzed using a geographic information system(GIS).The grey relational analysis(GRA) was combined with the analytic hierarchy process(AHP) to address the uncertainties during the process of evaluating the traditional land ecological suitability,and a modified land ecological suitability evaluation(LESE) model was built.Based on the land-use map of Zhejiang Province,the regions that were completely unsuitable for tea cultivation in the province were eliminated and then the spatial distribution of the ecological suitability of tea crops was generated using the modified LESE model and GIS.The results demonstrated that the highly,moderately,and non-suitable regions for the cultivation of tea crops in Zhejiang Province were 27 552.66,42 724.64,and 26 507.97 km 2,and accounted for 28.47%,44.14%,and 27.39% of the total evaluation area,respectively.Validation of the method showed a high degree of coincidence with the current planting distribution of tea crops in Zhejiang Province.The modified LESE model combined with GIS could be useful in quickly and accurately evaluating the land ecological suitability of tea crops,providing a scientific basis for the rational distribution of tea crops and acting as a reference to land policy makers and land use planners.展开更多
Soil moisture has been considered as one of the main indicators that are widely used in the fields of hydrology,climate,ecology and others.The land surface temperature-vegetation index(LST-VI) space has comprehensive ...Soil moisture has been considered as one of the main indicators that are widely used in the fields of hydrology,climate,ecology and others.The land surface temperature-vegetation index(LST-VI) space has comprehensive information of the sensor from the visible to thermal infrared band and can well reflect the regional soil moisture conditions.In this study,9 pairs of moderate-resolution imaging spectroradiometer(MODIS) products(MOD09A1 and M0D11A2),covering 5 provinces in Southwest China,were chosen to construct the LST-VI space,and then the spatial distribution of soil moisture in 5 provinces of Southwest China was monitored by the temperature vegetation dryness index(TVDI).Three LST-VI spaces were constructed by normalized difference vegetation index(NDVI),enhanced vegetation index(EVI),and modified soil-adjusted vegetation index(MSAVI),respectively.The correlations between the soil moisture data from 98 sites and the 3 TVDIs calculated by LST-NDVI,LST-EVI and LST-MSAVI,respectively,were analyzed.The results showed that TVDI was a useful parameter for soil surface moisture conditions.The TVDI calculated from the LST-EVI space(TVDIE) revealed a better correlation with soil moisture than those calculated from the LST-NDVI and LST-MSAVI spaces.Prom the different stages of the TVDIE space,it is concluded that TVDIE can effectively show the temporal and spatial differences of soil moisture,and is an effective approach to monitor soil moisture condition.展开更多
基金funded by the Key Technologies R&D Program of China during the 12th Five-Year Plan period(2011BAD32B01)the National Natural Science Foundation of China(40875070)the Research Fund for Doctoral Program of Higher Education,China(20100101110035)
文摘Combined with remote sensing data and meteorological data, cold damage risk was assessed for planting area of double cropping rice (DCR) in Hunan Province, China. A new methodology of cold damage risk assessment was built that apply to grid and have clear hazard-affected body. Each station cold damage annual frequency and average annual intensity of cold damage was calculated by using 1951-2010 station daily mean temperature and simple cold damage identification index. On this basis, average annual cold damage risk index was obtained by their product. The spatial analysis models of cold damage risk index about double-season early rice (DSER) and double-season later rice (DSLR) were established respectively by the relation of average annual cold damage risk index and its geographic factors. Critical threshold of level of average annual cold damage risk index for DSER and DSLR were respectively divided by the correlative equation of cold damage annual frequency and average annual intensity of cold damage. 2001-2010 planting area of DCR, acquired by time series analysis of MOD09A1 8-d composite land surface reflectance product, was as target of assessment. The results show average annual intensity of cold damage is exponential function of cold damage annual frequency, average annual cold damage risk index is directly proportional to cold damage cumulant and cold damage annual frequency, and is inversely proportional to happen times of cold damage and the square of statistical time sequence length. Cold damage risk of DSER is higher than DSLR in Hunan Province. In the 10-yr stacking map, DCR planting in low risk area accounted for 11.92% of total extraction area, in moderate risk area accounted for 69.62%, in high risk area accounted for 18.46%. According to the cold damage risk assessment result, DCR production can be guided to reduce cold damage losses.
基金Project supported by the National Natural Science Foundation of China (No.40571115)the National High Tech-nology Research and Development Program (863 Program) of China (Nos.2006AA120101 and 2007AA10Z205)
文摘The radial basis function (RBF) emerged as a variant of artificial neural network. Generalized regression neural network (GRNN) is one type of RBF, and its principal advantages are that it can quickly learn and rapidly converge to the optimal regression surface with large number of data sets. Hyperspectral reffectance (350 to 2500 nm) data were recorded at two different rice sites in two experiment fields with two cultivars, three nitrogen treatments and one plant density (45 plants m-2). Stepwise multivariable regression model (SMR) and RBF were used to compare their predictability for the leaf area index (LAI) and green leaf chlorophyll density (GLCD) of rice based on reffectance (R) and its three different transformations, the first derivative reffectance (D1), the second derivative reffectance (D2) and the log-transformed re?ectance (LOG). GRNN based on D1 was the best model for the prediction of rice LAI and GLCD. The relationships between different transformations of reffectance and rice parameters could be further improved when RBF was employed. Owing to its strong capacity for nonlinear mapping and good robustness, GRNN could maximize the sensitivity to chlorophyll content using D1. It is concluded that RBF may provide a useful exploratory and predictive tool for the estimation of rice biophysical parameters.
基金Project supported by the Natural Science Foundation of ZhejiangProvince (No. 30295) and the Key Project of Zhejiang Province (No.011103192), China
文摘Landslide susceptibility map is one of the study fields portraying the spatial distribution of future slope failure sus- ceptibility. This paper deals with past methods for producing landslide susceptibility map and divides these methods into 3 types. The logistic linear regression approach is further elaborated on by crosstabs method, which is used to analyze the relationship between the categorical or binary response variable and one or more continuous or categorical or binary explanatory variables derived from samples. It is an objective assignment of coefficients serving as weights of various factors under considerations while expert opinions make great difference in heuristic approaches. Different from deterministic approach, it is very applicable to regional scale. In this study, double logistic regression is applied in the study area. The entire study area is first analyzed. The logistic regression equation showed that elevation, proximity to road, river and residential area are main factors triggering land- slide occurrence in this area. The prediction accuracy of the first landslide susceptibility map was showed to be 80%. Along the road and residential area, almost all areas are in high landslide susceptibility zone. Some non-landslide areas are incorrectly divided into high and medium landslide susceptibility zone. In order to improve the status, a second logistic regression was done in high landslide susceptibility zone using landslide cells and non-landslide sample cells in this area. In the second logistic regression analysis, only engineering and geological conditions are important in these areas and are entered in the new logistic regression equation indicating that only areas with unstable engineering and geological conditions are prone to landslide during large scale engineering activity. Taking these two logistic regression results into account yields a new landslide susceptibility map. Double logistic regression analysis improved the non-landslide prediction accuracy. During calculation of parameters for logistic regres- sion, landslide density is used to transform nominal variable to numeric variable and this avoids the creation of an excessively high number of dummy variables.
基金Project (Nos. 40571115 and 40271078) supported by the NationalNatural Science Foundation of China
文摘There is increasing interest in using hyperspectral data for quantitative characterization of vegetation in spatial and temporal scopes. Many spectral indices are being developed to improve vegetation sensitivity by minimizing the background influence. The chlorophyll absorption continuum index (CACI) is such a measure to calculate the spectral continuum on which the analyses are based on the area of the troughs spanned by the spectral continuum. However, different values of CACI were obtained in this method because different positions of continuums were determined by different users. Furthermore, the sensitivity of CACI to agronomic parameters such as green leaf chlorophyll density (GLCD) has been reduced because the fixed positions of con- tinuums are determined when the red edge shifted with the change in GLCD. A modified chlorophyll absorption continuum index (MCACI) is presented in this article. The red edge inflection point (REIP) replaces the maximum reflectance point (MRP) in near-infrared (NIR) shoulder on the CACI continuum. This MCACI has been proved to increase the sensitivity and predictive power of GLCD.
基金Project supported by the Natural Science Foundation of ZhejiangProvince (No. 30295) and the Key Project of Zhejiang Province (No.011103192), China
文摘This paper presents a Zhejiang Province southeastern China seasonal temperature model based on GIS techniques. Terrain variables derived from the 1 km resolution DEM are used as predictors of seasonal temperature, using a regression-based approach. Variables used for modelling include: longitude, latitude, elevation, distance from the nearest coast, direction to the nearest coast, slope, aspect, and the ratio of land to sea within given radii. Seasonal temperature data, for the observation period 1971 to 2000, were obtained from 59 meteorological stations. Temperature data from 52 meteorological stations were used to construct the regression model. Data from the other 7 stations were retained for model validation. Seasonal temperature surfaces were constructed using the regression equations, and refined by kriging the residuals from the regression model and subtracting the result from the predicted surface. Latitude, elevation and distance from the sea are found to be the most important predictors of local seasonal temperature. Validation determined that regression plus kriging predicts seasonal temperature with a coefficient of determination (R2), between the estimated and observed values, of 0.757 (autumn) and 0.935 (winter). A simple regression model without kriging yields less accurate results in all seasons except for the autumn temperature.
基金Project (Nos. 40571115 and 40271078) supported by the National Natural Science Foundation of China
文摘Hyperspectral reflectance (350~2500 nm) data were recorded at two different sites of rice in two experiment fields including two cultivars, and three levels of nitrogen (N) application. Twenty-five Vegetation Indices (VIs) were used to predict the rice agronomic parameters including Leaf Area Index (LAI, m2 green leaf/m2 soil) and Green Leaf Chlorophyll Density (GLCD, mg chlorophyll/m2 soil) by the traditional regression models and Radial Basis Function Neural Network (RBF). RBF emerged as a variant of Artificial Neural Networks (ANNs) in the late 1980’s. A large variety of training algorithms has been tested for training RBF networks. In this study, Original RBF (ORBF), Gradient Descent RBF (GDRBF), and Generalized Regression Neural Network (GRNN) were employed. Results showed that green waveband Normalized Difference Vegetation Index (NDVIgreen) and TCARI/OSAVI have the best prediction power for LAI by exponent model and ORBF respectively, and that TCARI/OSAVI has the best prediction power for GLCD by exponent model and GDRBF. The best performances of RBF are compared with the traditional models, showing that the relationship between VIs and agronomic variables are further improved when RBF is used. Compared with the best traditional models, ORBF using TCARI/OSAVI improves the prediction power for LAI by lowering the Root Mean Square Error (RMSE) for 0.1119, and GDRBF using TCARI/OSAVI improves the prediction power for GLCD by lowering the RMSE for 26.7853. It is concluded that RBF provides a useful exploratory and predictive tool when applied to the sensitive VIs.
基金supported by the National High Technology Research and Development Program of China (Grant No. 2006AA10Z203) the National Natural Science Foundation of China (Grant No. 40571115).
文摘Large-scale farming of agriculture crops requires real-time detection of disease for field pest management. Hyperspectral remote sensing data generally have high spectral resolution, which could be very useful for detecting disease stress in green vegetation at the leaf and canopy levels. In this study, hyperspectral reflectances of rice in the laboratory and field were measured to characterize the spectral regions and wavebands, which were the most sensitive to rice brown spot infected by Bipolaris oryzae (Helminthosporium oryzae Breda. de Hann). Leaf reflectance increased at the ranges of 450 to 500 nm and 630 to 680 nm with the increasing percentage of infected leaf surface, and decreased at the ranges of 520 to 580 nm, 760 to 790 nm, 1550 to 1750 nm, and 2080 to 2350 nm with the increasing percentage of infected leaf surface respectively. The sensitivity analysis and derivative technique were used to select the sensitive wavebands for the detection of rice brown spot infected by B. oryzae. Ratios of rice leaf reflectance were evaluated as indicators of brown spot. R669/R746 (the reflectance at 669 nm divided by the reflectance at 746 nm, the following ratios may be deduced by analogy), R702/R718, R692/R530, R692/R732, R535/R746, R521/R718, and R569/R718 increased significantly as the incidence of rice brown spot increased regardless of whether it’s at the leaf or canopy level. R702/R718, R692/R530, R692/R732 were the best three ratios for estimating the disease severity of rice brown spot at the leaf and canopy levels. This result not only confirms the capability of hyperspectral remote sensing data in characterizing crop disease for precision pest management in the real world, but also testifies that the ratios of crop reflectance is a useful method to estimate crop disease severity.
基金Supported by the Agricultural Science and Technology Achievement Transformation Foundation of the Ministry of Sciences and Technology,China (No. 2008GB24160442)the National Natural Science Foundation of China (Nos. 40871158and 51108405 )
文摘Tea(Camellia sinensis) is one of the most valuable cash crops in southern China;however,the planting distribution of tea crops is not optimal and the production and cultivation regions of tea crops are restricted by law and custom.In order to evaluate the suitability of tea crops in Zhejiang Province,the annual mean temperature,the annual accumulated temperature above 10 C,the frequency of extremely low temperature below 13 C,the mean humidity from April to October,slope,aspect,altitude,soil type,and soil texture were selected from climate,topography,and soil factors as factors for land ecological evaluation by the Delphi method based on the ecological characteristics of tea crops.These nine factors were quantitatively analyzed using a geographic information system(GIS).The grey relational analysis(GRA) was combined with the analytic hierarchy process(AHP) to address the uncertainties during the process of evaluating the traditional land ecological suitability,and a modified land ecological suitability evaluation(LESE) model was built.Based on the land-use map of Zhejiang Province,the regions that were completely unsuitable for tea cultivation in the province were eliminated and then the spatial distribution of the ecological suitability of tea crops was generated using the modified LESE model and GIS.The results demonstrated that the highly,moderately,and non-suitable regions for the cultivation of tea crops in Zhejiang Province were 27 552.66,42 724.64,and 26 507.97 km 2,and accounted for 28.47%,44.14%,and 27.39% of the total evaluation area,respectively.Validation of the method showed a high degree of coincidence with the current planting distribution of tea crops in Zhejiang Province.The modified LESE model combined with GIS could be useful in quickly and accurately evaluating the land ecological suitability of tea crops,providing a scientific basis for the rational distribution of tea crops and acting as a reference to land policy makers and land use planners.
基金Supported by the National Key Technologies Research and Development Program of the Ministry of Science and Technology of China during the 12th Five-Year Plan Period(Nos.2011BAD32B01 and 2012BAH29B02)
文摘Soil moisture has been considered as one of the main indicators that are widely used in the fields of hydrology,climate,ecology and others.The land surface temperature-vegetation index(LST-VI) space has comprehensive information of the sensor from the visible to thermal infrared band and can well reflect the regional soil moisture conditions.In this study,9 pairs of moderate-resolution imaging spectroradiometer(MODIS) products(MOD09A1 and M0D11A2),covering 5 provinces in Southwest China,were chosen to construct the LST-VI space,and then the spatial distribution of soil moisture in 5 provinces of Southwest China was monitored by the temperature vegetation dryness index(TVDI).Three LST-VI spaces were constructed by normalized difference vegetation index(NDVI),enhanced vegetation index(EVI),and modified soil-adjusted vegetation index(MSAVI),respectively.The correlations between the soil moisture data from 98 sites and the 3 TVDIs calculated by LST-NDVI,LST-EVI and LST-MSAVI,respectively,were analyzed.The results showed that TVDI was a useful parameter for soil surface moisture conditions.The TVDI calculated from the LST-EVI space(TVDIE) revealed a better correlation with soil moisture than those calculated from the LST-NDVI and LST-MSAVI spaces.Prom the different stages of the TVDIE space,it is concluded that TVDIE can effectively show the temporal and spatial differences of soil moisture,and is an effective approach to monitor soil moisture condition.
