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1.
A. V. Murugan Suresh Kumar V. K. Dadhwal M. K. Gupta 《Journal of the Indian Society of Remote Sensing》2014,42(2):453-459
This study involves generation and logical integration of non-spatial and spatial data in a geographical information system framework to address the gap in national level soil organic carbon estimates. Remote sensing derived inputs and other spatial layers are corrected and integrated using same geographical standards. A relational data base of soil organic carbon density of Indian forest was prepared with attribute information. Hierarchical approach was followed to stratify and verify each sample from the data base using the corrected input layers in GIS and the resulting spatially distributed data is called Indian forest soil organic carbon database. The estimated mean soil organic carbon density for Indian forest is 70 t ha?1 and varied from 35.4 t ha?1 in Tropical thorn forest to 104.2 t ha?1 in Himalayan moist temperate forest in the upper 30 cm of soil depth. Due to large variations in the surface layers the estimated standard error ranged from ±1.5 to 15 % for the upper 30 cm layer which is generally higher than the bottom soil layers. The level of detail in the data base helps to establish base line information for global, national and regional level studies. 相似文献
2.
This study integrates the RUSLE, remote sensing and GIS to assess soil loss and identify sensitive areas to soil erosion in the Nilufer creek watershed in Bursa province, Turkey. The annual average soil loss was generated separately for years 1984 and 2011, in order to expose possible soil loss differences occurred in 27 years. In addition, sediment accumulation and sediment yield of the studied watershed was also predicted and discussed. The results indicated that very severe erosion risk areas in 1984 was 13.4% of the area, but it was increased to 15.3% by the year 2011, which needs immediate attention from soil conservation point of view. Furthermore, the estimated annual sediment yield of the Nilufer creek watershed was increased from 903 to 979 Mg km?2 y?1 in 27 years period. The study also provides useful information for decision-makers and planners to take appropriate land management practices in the area. 相似文献
3.
土壤有机质光谱特征研究(英文) 总被引：1，自引：0，他引：1
The study on soil spectral reflectance features is the physical basis for soil remote sensing. Soil organic matter content
influences the soil spectral reflectance dramatically. This paper studied the spectral curves between 400 nm∼2500 nm of 174
soil samples which were collected in Hengshan county and Yixing county. Fourteen types of transformations were applied to
the soil reflectance R to remove the noise and to linearize the correlation between reflectance (independent variable) and soil organic matter (SOM)
content (dependent variable). Then, the methods such as derivative spectrum technology and stepwise regression analysis, were
applied to study the relationship between these soil spectral features and soil organic matter content. It shows that order
1 derivative of the logarithm of reflectance (O1DLA) is the most sensitive to SOM among the various transform types of reflectance
in consideration. The regression model whose coefficient of determination reaches 0.885 is built. It predicted the soil organic
matter content with higher effect.
Supported by the National Natural Science Foundation of China (No. 40271007). 相似文献
4.
Soil erodibility, which is difficult to estimate and upscaling, was determined in this study using multiple spectral models of soil properties (soil organic matter (SOM), water-stable aggregates (WSA) > 0.25 mm, the geometric mean radius (Dg)). Herein, the soil erodibility indicators were calculated, and soil properties were quantitatively analyzed based on laboratory simulation experiments involving two selected contrasting soils. In addition, continuous wavelet transformation was applied to the reflectance spectra (350–2500 nm) of 65 soil samples from the study area. To build the relationship, the soil properties that control erodibility were identified prior to the spectral analysis. In this study, the SOM, Dg and WSA >0.25 mm were selected to represent the most significant soil properties controlling erodibility and describe the erodibility indicator based on a logarithmic regression model as a function of SOM or WSA > 0.25 mm. Five, six and three wavelet features were observed to calibrate the estimated soil properties model, and the best performance was obtained with a combination feature regression model for SOM (R2 = 0.86, p < 0.01), Dg (R2 = 0.79, p < 0.01) and WSA >0.25 mm (R2 = 0.61, p < 0.01), respectively. One part of the wavelet features captured amplitude variations in the broad shape of the reflectance spectra, and another part captured variations in the shape and depth of the soil dry substances. The wavelet features for the validated dataset used to predict the SOM, WSA >0.25 mm and Dg were not significantly different compared with the calibrated dataset. The synthesized spectral models of soil properties, and the formation of a new equation for soil erodibility transformed from the spectral models of soil properties are presented in this study. These results show that a spectral analytical approach can be applied to complex datasets and provide new insights into emerging dynamic variation with erodibility estimation. 相似文献
5.
The aim of this paper is to evaluate the impacts of land use change on soil loss. Soil loss was quantified using the revised universal soil loss equation model in Darabkola catchment. Land use maps of 1992, 1998 and 2012 were derived from Landsat Thematic Mapper data. The mean annual soil loss was therefore determined for these years. The results showed open-canopy forest area decreased by 36% between 1992 and 1998. Likewise, the decreasing trend of forest lands which are near to residential areas has continued from 1795 ha in 1998 to 1765 ha in 2012. Also the results indicate that the maximum annual soil loss ranged from 5.06, 6.19 and 15.23 ton h?1 y?1 in 1992, 1998 and 2012, respectively. Also, by assuming that all watershed conditions and land uses be constant in the future, then the area of close- and open-canopy forest and dry agricultural lands will be 23.23, 2.88 and 29.89 ha in 2040, respectively. 相似文献
6.
The purpose of this study is to estimate long-term SMC and find its relation with soil moisture (SM) of climate station in different depths and NDVI for the growing season. The study area is located in agricultural regions in the North of Mongolia. The Pearson’s correlation methodology was used in this study. We used MODIS and SPOT satellite data and 14 years data for precipitation, temperature and SMC of 38 climate stations. The estimated SMC from this methodology were compared with SM from climate data and NDVI. The estimated SMC was compared with SM of climate stations at a 10-cm depth (r2 = 0.58) and at a 50-cm depth (r2 = 0.38), respectively. From the analysis, it can be seen that the previous month’s SMC affects vegetation growth of the following month, especially from May to August. The methodology can be an advantageous indicator for taking further environmental analysis in the region. 相似文献
7.
Collins O. Oduor Nancy Karanja Richard Onwong’a Stephen Mureithi David Pelster Gert Nyberg 《Carbon balance and management》2018,13(1):24
Background
Pasture enclosures play an important role in rehabilitating the degraded soils and vegetation, and may also influence the emission of key greenhouse gasses (GHGs) from the soil. However, no study in East Africa and in Kenya has conducted direct measurements of GHG fluxes following the restoration of degraded communal grazing lands through the establishment of pasture enclosures. A field experiment was conducted in northwestern Kenya to measure the emission of CO2, CH4 and N2O from soil under two pasture restoration systems; grazing dominated enclosure (GDE) and contractual grazing enclosure (CGE), and in the adjacent open grazing rangeland (OGR) as control. Herbaceous vegetation cover, biomass production, and surface (0–10 cm) soil organic carbon (SOC) were also assessed to determine their relationship with the GHG flux rate.Results
Vegetation cover was higher enclosure systems and ranged from 20.7% in OGR to 40.2% in GDE while aboveground biomass increased from 72.0 kg DM ha?1 in OGR to 483.1 and 560.4 kg DM ha?1 in CGE and GDE respectively. The SOC concentration in GDE and CGE increased by an average of 27% relative to OGR and ranged between 4.4 g kg?1 and 6.6 g kg?1. The mean emission rates across the grazing systems were 18.6 μg N m?2 h?1, 50.1 μg C m?2 h?1 and 199.7 mg C m?2 h?1 for N2O, CH4, and CO2, respectively. Soil CO2 emission was considerably higher in GDE and CGE systems than in OGR (P?<?0.001). However, non-significantly higher CH4 and N2O emissions were observed in GDE and CGE compared to OGR (P?=?0.33 and 0.53 for CH4 and N2O, respectively). Soil moisture exhibited a significant positive relationship with CO2, CH4, and N2O, implying that it is the key factor influencing the flux rate of GHGs in the area.Conclusions
The results demonstrated that the establishment of enclosures in tropical rangelands is a valuable intervention for improving pasture production and restoration of surface soil properties. However, a long-term study is required to evaluate the patterns in annual CO2, N2O, CH4 fluxes from soils and determine the ecosystem carbon balance across the pastoral landscape.8.
Effect of cover management factor in quantification of soil loss: case study of Sungai Akah subwatershed,Baram River basin Sarawak,Malaysia 总被引：1，自引：0，他引：1
The present study evaluates the effectiveness and suitability of cover management factors (C factor) generated through different techniques like land use/land cover-based arbitrary value (CLULC), Normalised Different Vegetation Index-based methods CNDVI1 and CNDVI2 and Modified Soil Adjusted Vegetation Index 2-based method (CMSAVI2). The C factors generated using these four methods were tested in the calculation and assessment of annual average soil loss from an upland forested subwatershed in the Baram river basin using the Revised Universal Soil Loss Equation (RUSLE). The four cover management factor maps generated by this analysis show some variation among the results. The LULC method uses a single arbitrary value for each LULC type mapped in the subwatershed. The other three methods show a range of C values within each mapped LULC type. The effects of these variations were tested in the RUSLE by keeping the factors such as rainfall erosivity (R), soil erodibility (K), slope-length and steepness (LS) constant. The maximum annual average soil loss is 1191 t. ha?1. y?1 based on the CLULC. Soil losses estimated with other three methods are very different compared to those estimated with the CLULC method. The highest calculated soil loss values were 1832, 1674 and 1608 t. ha?1. y?1 in the study area based, respectively, on CNDVI1, CNDVI2 and CMSAVI2 C factors. These maximum values represent the worst pixel scenario values of soil loss in the region. The statistical analysis performed indicates different relationship between the parameters and suggests the acceptance of the methodology based on CNDVI2 for the study area, instead of a single value method such as CLULC. Among the other two methods, the CMSAVI2 was found to be more consistent than the CNDVI1 method, but both methods lead to over-prediction of annual soil loss rate and therefore need to be reconsidered before applied in the RUSLE. 相似文献
9.
An automatic fractional vegetation cover (FVC) estimation method based on image characteristics in an agricultural region was proposed in this study to remove the empiricism in determining the key parameters of empirical methods. The proposed method automatically determined the soil and vegetation lines in the two-dimensional space of the red and blue band reflectances, which involved an iterative soil and vegetation pixels selection procedure, and then estimated FVC of a pixel based on its distances from the soil and vegetation lines. The accuracy assessment using field survey data indicated that the performance of the proposed method (R2 = 0.69, RMSE = 0.072, Bias = 0.014) was comparable with several commonly used empirical methods. Therefore, it was indicated that the proposed method could effectively estimate FVC in the corn-dominated region. 相似文献
10.
P. C. Padhy R. K. Nayak V. K. Dadhwal M. Salim D. Mitra S. B. Chaudhury P. R. Rao K. H. Rao C.B. S. Dutt 《Journal of the Indian Society of Remote Sensing》2016,44(1):135-143
An empirical model is developed and used with remotely sensed predictors: sea surface temperature (SST) and chlorophyll-a concentration (Chl-a), to compute surface water partial pressure of carbon dioxide (pCO2w) and air-sea fluxes of CO2 in the Hooghly estuary and its adjacent coastal oceans. In situ observations used here were based on measurements carried out in this region during winter and summer periods in 2008. The estimated pCO2w compares well with the in situ observations at root mean square error ±18 μatm. In winter, estimated pCO2w ranges between 320 and 500 μatm with large values (>400 μatm) on the south-western and south-eastern flanks of the coastal domain and lower values (340–375 μatm) on the main-channel. In summer, it remained spatially uniform at 450 μatm. Extrapolation of the results over the study region based on the Moderate Imaging Specroradiometer (MODIS) measured SST and Chl-a suggests that the region is a strong source of atmospheric CO2 during the summer with net release of 0.095 Tg C year?1 (equivalent to mean flux of 90 molC m?2 year?1) and is a weak source during the winter with net release of 0.006 Tg C yr?1 (0.5 molC m?2 year?1) from the geographical extent of 6000 Km2 area. 相似文献
11.
Soil erosion prediction based on land cover dynamics at the Semenyih watershed in Malaysia using LTM and USLE models 总被引：1，自引：0，他引：1
This study attempts to identify and forecast future land cover (LC) by using the Land Transformation Model (LTM), which considers pixel changes in the past and makes predictions using influential spatial features. LTM applies the Artificial Neural Networks algorithm) in conducting the analysis. In line with these objectives, two satellite images (Spot 5 acquired in 2004 and 2010) were classified using the Maximum Likelihood method for the change detection analysis. Consequently, LC maps from 2004 to 2010 with six classes (forest, agriculture, oil palm cultivations, open area, urban, and water bodies) were generated from the test area. A prediction was made on the actual soil erosion and the soil erosion rate using the Universal Soil Loss Equation (USLE) combined with remote sensing and GIS in the Semenyih watershed for 2004 and 2010 and projected to 2016. Actual and potential soil erosion maps from 2004 to 2010 and projected to 2016 were eventually generated. The results of the LC change detections indicated that three major changes were predicted from 2004 to 2016 (a period of 12 years): (1) forest cover and open area significantly decreased at rates of almost 30 and 8 km2, respectively; (2) cultivated land and oil palm have shown an increment in sizes at rates of 25.02 and 5.77 km2, respectively; and, (3) settlement and Urbanization has intensified also by almost 5 km2. Soil erosion risk analysis results also showed that the Semenyih basin exhibited an average annual soil erosion between 143.35 ton ha?1 year?1 in 2004 and 151 in 2010, followed by the expected 162.24 ton ha?1 year?1. These results indicated that Semenyih is prone to water erosion by 2016. The wide range of erosion classes were estimated at a very low level (0–1 t/ha/year) and mainly located on steep lands and forest areas. This study has shown that using both LTM and USLE in combination with remote sensing and GIS is a suitable method for forecasting LC and accurately measuring the amount of soil losses in the future. 相似文献
12.
This paper evaluates the renaturation activities applying the quantification of vegetation cover (VC), the site suitability analysis (SSA) based on the predefined criteria (slope steepness category (SSC), soil erodibility factor (K) and VC) and soil erosion model (SEM) results within the terrain units (TUs) along pipeline rights-of-way (RoW). Quantification of VC percentage is performed to assess the overall restored VC from 2005 to 2007. The results of the quantitative analysis in 2007 show that the total area of restored VC is 10.7 km2, and 8.9 km2 still needs to be restored to comply with the environmental acceptance criteria. As a result of SSA, TUs were prioritized by erosion vulnerability and this allowed to better understand the landscape behaviour in regards to erosion processes. SEM provided more detailed predictions of erosion classes falling into TUs. SEM identified 40% of erosion sites occurred from 2005 to 2010. 相似文献
13.
14.
Developing spectral models of soil properties is an important frontier in remote sensing and soil science. Several studies have focused on modeling soil properties such as total pools of soil organic matter and carbon in bare soils. We extended this effort to model soil parameters in areas densely covered with coastal vegetation. Moreover, we investigated soil properties indicative of soil functions such as nutrient and organic matter turnover and storage. These properties include the partitioning of mineral and organic soil between particulate (>53 μm) and fine size classes, and the partitioning of soil carbon and nitrogen pools between stable and labile fractions. Soil samples were obtained from Avicennia germinans mangrove forest and Juncus roemerianus salt marsh plots on the west coast of central Florida. Spectra corresponding to field plot locations from Hyperion hyperspectral image were extracted and analyzed. The spectral information was regressed against the soil variables to determine the best single bands and optimal band combinations for the simple ratio (SR) and normalized difference index (NDI) indices. The regression analysis yielded levels of correlation for soil variables with R2 values ranging from 0.21 to 0.47 for best individual bands, 0.28 to 0.81 for two-band indices, and 0.53 to 0.96 for partial least-squares (PLS) regressions for the Hyperion image data. Spectral models using Hyperion data adequately (RPD > 1.4) predicted particulate organic matter (POM), silt + clay, labile carbon (C), and labile nitrogen (N) (where RPD = ratio of standard deviation to root mean square error of cross-validation [RMSECV]). The SR (0.53 μm, 2.11 μm) model of labile N with R2 = 0.81, RMSECV= 0.28, and RPD = 1.94 produced the best results in this study. Our results provide optimism that remote-sensing spectral models can successfully predict soil properties indicative of ecosystem nutrient and organic matter turnover and storage, and do so in areas with dense canopy cover. 相似文献
15.
Soil organic carbon (SOC) is an important aspect of soil quality and plays an imperative role in soil productivity in the agriculture ecosystems. The present study was applied to estimate the SOC stock using space-borne satellite data (Landsat 4–5 Thematic Mapper [TM]) and ground verification in the Medinipur Block, Paschim Medinipur District and West Bengal in India. In total, 50 soil samples were collected randomly from the region according to field surveys using a hand-held Global Positioning System (GPS) unit to estimate the surface SOC concentrations in the laboratory. Bare soil index (BSI) and normalized difference vegetation ndex (NDVI) were explored from TM data. The satellite data-derived indices were used to estimate spatial distribution of SOC using multivariate regression model. The regression analysis was performed to determine the relationship between SOC and spectral indices (NDVI and BSI) and compared the observed SOC (field measure) to predict SOC (estimated from satellite images). Goodness fit test was performed to determine the significance of the relationship between observed and predicted SOC at p ≤ 0.05 level. The results of regression analysis between observed SOC and NDVI values showed significant relationship (R2 = 0.54; p < 0.0075). A significant statistical relationship (r = ?0.72) was also observed between SOC and BSI. Finally, our model showed nearly 71% of the variance of SOC distribution could be explained by SOC and NDVI values. The information from this study has advanced our understanding of the ongoing ecological development that affects SOC dissemination and might be valuable for effective soil management. 相似文献
16.
土壤有机物质对土壤介电常数的影响 总被引：4，自引：0，他引：4
土壤介电常数是开展微波土壤水分和冻融状态的监测的基础,也是植被和积雪的下垫面边界条件,然而目前已有的介电常数研究都没有对高有机质含量的土壤开展系统观测。本文将土壤中的自然有机物质分为腐殖质和植物性残留物两类。采用控制变量实验方法,通过测量5种不同有机质含量的东北黑土和加入不同比例毛白杨碎屑的扁都口草甸土,研究了腐殖质和植物性残留物的对土壤介电常数的影响。结果表明,腐殖质会降低干燥土壤的容重,从而发挥间接作用,使介电常数降低;而对于相同容重下观测的潮湿土壤,腐殖质含量较多的土壤介电常数更大。与Dobson模型的比较显示,在29℃室温下,腐殖质对25%重量含水量潮湿土壤实部的影响在±2左右,虚部能达到1。与腐殖质相比,植物性残留物对风干土壤和潮湿土壤的影响都十分明显。植物性残留物能有效地疏松土壤并代入植物组分的介电特征。当重量含水量为30%时,含毛白杨含量为20%的混合土壤比纯扁都口土壤在实部平均减小3—7左右,虚部减小 1—3左右。因此,根据实验观测以及和模型的比较,土壤中的有机物质会改变土壤介电性质,对微波遥感造成影响。 相似文献
17.
Applying remote sensing techniques to develop the retrieval models and further to obtain the spatiotemporal information of water quality parameters is necessary for understanding, managing, and protecting lake ecosystems. This study aimed to calibrate and validate the retrieval models for estimating the concentrations of chlorophyll a (CCHL), suspended particulate matter (CSPM), and dissolved organic carbon (CDOC) with the in situ hyperspectral measurements in Poyang Lake, China in 2010 and 2011. The model calibration and validation results indicated that: (1) for CCHL retrieval, significantly strong and moderate correlations existed between the measured and estimated values (with the correlation coefficient r = 0.92 and r = 0.76) using the exponential model and the three-band model, respectively, with biased estimation observed for the exponential model; (2) for retrieving CSPM, there was a strong correlation between the measured and estimated values (r = 0.95) using the exponential model; and (3) no significant correlation between measured and estimated CDOC values was found with our developed models. More work is needed to allow the water quality of Poyang Lake to be accurately and steadily estimated, especially for CCHL and CDOC. 相似文献
18.
19.
Abstract The feasibility of using a geographic information system (GIS) to evaluate the subsurface transport of volatile organic compounds (VOCs) is evaluated. A GIS analysis based on selected soil properties and a flow path model were used to demonstrate the visualization of a probable path taken by diffusing VOC vapors in the vadose zone. The model uses soil properties such as texture, organic matter content, water content, bulk density, and permeability as path finding indices. The values of the soil properties used in the validation of this model were obtained from the respective County Soil Survey Reports. The various combinations of these soil properties resulted in deviations from the anticipated path taken by the diffusing organic vapors as they migrated through the subsurface. The model was evaluated using two soil systems and estimated diffusion coefficients. A proportionality constant (k) was calculated and used in conjunction with the assigned soil property values in the calculation of diffusion coefficients (Df) for trichloroethylene (TCE) vapors for each soil system. For each case, the model depicts the probable path taken by the VOC vapors as being the route through the soils having characteristics most favorable for chemical migration. Specifically, the soils along the predicted flow path have combinations of high moisture contents, low bulk densities, clay and organic matter contents as well as higher porosity and permeability than soils along any other possible flow path in the given soil systems. 相似文献
20.