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1.
Mushtaq A. Wani N. Shaista Zahid M. Wani 《Journal of the Indian Society of Remote Sensing》2017,45(4):611-620
Soil is a suitable place for vegetation and plant growth. When this valuable resource is not preserved, shortage of food, erosion and damage of natural resources will be respected. Soil is a heterogeneous, diverse and dynamic system and investigation of its temporal and spatial changes is essential. In this paper spatial variability of some chemical and physical soil were investigated. Three hundred fifty eight soil samples were collected by systematic sampling strategy at 20 cm depth on a regular grid spacing of 500 × 500 m2 under different vegetation cover and processed for analysis in the laboratory. Soil chemical and physical parameters including pH, electrical conductivity, organic carbon, available phosphorus, available nitrogen, available potassium, sulphur, calcium, magnesium and sodium were measured. After data normalization, classical statistical analysis was used to describe soil properties and geo-statistical analysis was used to illustrate spatial correlation of soil characteristics. By using interpolating techniques, spatial distribution of these properties were prepared. Results indicated that calcium and phosphorus had strong and weak spatial dependence, respectively. 相似文献
2.
Spatial variability of soil erodibility and its correlation with soil properties in semi-arid mountainous watershed,Saudi Arabia 总被引：2，自引：0，他引：2
Soil erodibility values are best estimated from long-term direct measurements on runoff-plots; however, in lack of field tests, these values can be estimated using relationships based on physico-chemical soil properties. The study objective was to assess the erodibility and its correlation with soil properties. The average erodibility value was estimated 0.043 t ha h ha?1 MJ?1 mm?1. The areas with heavy textured soil and low organic matter content had the lowest values of erodibility. The erodibility decreases as the sand content increases, whereas silt showed a positive correlation. The erodibility factors and its relation to soil properties were evaluated using multiple regression analysis. Results revealed that sand and organic matter content of soil combinedly explained 78% of variation. Altitudinal increases also seem to affect the soil texture. This study has demonstrated that soil properties and erodibility values can be used as assistance for soil conservation practices and modelling of landscape processes. 相似文献
3.
利用粉煤灰和煤矸石作为充填基质充填沉陷地覆土造地是治理采煤塌陷区的重要方法之一.为研究煤矸石、粉煤灰充填复垦方式对土壤理化性质的影响,本文以淮南上窑粉煤灰场复垦地和新庄孜矿煤矸石充填复垦地为研究对象,分别对粉煤灰复垦地土壤样品、煤矸石复垦地土壤样品和对照地块土壤样品的土壤pH值、土壤含水量、土壤有机质、水解氮、速效磷、有效钾含量、冬小麦产量等指标监测分析.结果表明,复垦土壤pH值大于对照土壤样品,粉煤灰充填复垦土壤呈强碱性,煤矸石充填复垦土壤pH值接近对照土壤;粉煤灰充填复垦地粉煤灰层土壤含水量明显大于对照土壤样品,煤矸石充填复垦地煤矸石层土壤含水量明显低于对照土壤样品;复垦土壤有机质、水解氮、速效磷、有效钾含量、冬小麦产量低于对照土壤样品.粉煤灰充填复垦土壤水解氮含量、有效钾含量低于煤矸石充填复垦土壤,速效磷含量高于煤矸石充填复垦土壤. 相似文献
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Pramila Raina Mahesh Kumar Mohar Singh 《Journal of the Indian Society of Remote Sensing》2009,37(4):647-657
In the present study, efforts have been made to identify and map areas affected by various soil degradation processes in Hanumangarh
district of western Rajasthan. Soil degradation processes were identified by using IRS-1B satellite image of the year 1998,
SOI toposheets, ground truth verification and soil studies. The kind, extent and degree of soil degradation have been mapped
in an area of 9703 km2. The study reveals that the soil degradation problems were mainly due to wind erosion/deposition and water-logging, followed
by salinity/alkalinity, water erosion and wind and water erosion combinedly. Nearly 38.7% area is subjected to slight and
moderate degradation, which can easily be combated by adopting the suggested techniques and 17.1% area is free of hazard.
Soil degradation processes have resulted in the loss of organic carbon, available Phosphorus and Potassium. Soil degradation
due to water logging/salinization has also shown a significant increase in electrical conductivity and available potassium
content of soil. 相似文献
6.
Martin Yemefack Wietske Bijker Steven M. De Jong 《International Journal of Applied Earth Observation and Geoinformation》2006
This paper investigates statistical relationships between land use/land cover (LULC), Landsat-7 ETM+ imagery and landscape mosaic structure in southern Cameroon where the conversion of tropical rain forest to shifting cultivation leads to dynamic processes, acting on the spatial aggregation of various LULC types. A Global Positioning System (GPS) was used in the field to identify a total of 171 shifting cultivation patches representing eight LULC types in two sub-areas. Because of the lack of a cloud-free image for the date of field sampling, the ETM+ imagery was acquired 2 months after field survey, during which it was assumed that no significant changes in LULC occurred (all dry season). Per pixel correlations were developed between spectral reflectance data, vegetation indices and LULC. As an exploratory study, several statistical methods (analysis of variance, means separations (Tukey HSD), principal component analysis (PCA), geo-statistical analysis, image classification and landscape metrics) were applied on point data and sensor images for evaluating the spatial variability within the landscape. Most variables explained 30–72% of LULC variation in the whole dataset. Those variables with high information content of LULC (infrared bands 4, 5, 7 and derived indices and PC1) also showed long ranges (6 km) spatial dependence as compared to those varying only within 1 km range. The results of these statistical analyses suggested the need to group some LULC types and the application of the Maximum Likelihood Classifier (MLC) for supervised classification provided a LULC map with the highest accuracy (81%) after consolidation of perennial LULC types, such as bush fallow, forest fallow and cocoa plantations. Landscape metrics computed from this map showed a high level of patch diversity and connectivity within the landscape and provided input data that can further be used to simulate predictive maps as substitute to cloud-covered sensor imageries. Landsat-7 ETM+ imagery proved to be useful in discriminating (with about 80% accuracy) the most dynamic LULC types such cropped plots and young fallow patches (shifting every season) and the extension front of the agricultural landscape. 相似文献
7.
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
8.
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
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As an important GIS function, spatial interpolation is one of the most often used geographic techniques for spatial query, spatial data visualization, and spatial decision-making processes in GIS and environmental science. However, less attention has been paid on the comparisons of available spatial interpolation methods, although a number of GIS models including inverse distance weighting, spline, radial basis functions, and the typical geostatistical models (i.e. ordinary kriging, universal kriging, and cokriging) are already incorporated in GIS software packages. In this research, the conceptual and methodological aspects of regression kriging and GIS built-in interpolation models and their interpolation performance are compared and evaluated. Regression kriging is the combination of multivariate regression and kriging. It takes into consideration the spatial autocorrelation of the variable of interest, the correlation between the variable of interest and auxiliary variables (e.g., remotely sensed images are often relatively easy to obtain as auxiliary variables), and the unbiased spatial estimation with minimized variance. To assess the efficiency of regression kriging and the difference between stochastic and deterministic interpolation methods, three case studies with strong, medium, and weak correlation between the response and auxiliary variables are compared to assess interpolation performances. Results indicate that regression kriging has the potential to significantly improve spatial prediction accuracy even when using a weakly correlated auxiliary variable. 相似文献
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There is a pressing need to determine the relationships between driving variables and landscape transformations. Human activities shape landscapes and turn them into complex assemblages of highly diverse structures. Other factors, including climate and topography, also play significant roles in landscape transitions, and identifying the interactions among the variables is critical to environmental management. This study analyzed the configurations and spatial-temporal processes of landscape changes from 1998 to 2011 under different anthropogenic disturbances, identified the main variables that determine the landscape patterns and transitions, and quantified the relationships between pairs of driver sets. Landsat images of Baicheng and Tekes from 1998, 2006 and 2011 were used to classify landscapes by supervised classification. Redundancy analysis (RDA) and variation partitioning were performed to identify the main driving forces and to quantify the unique, shared, and total explained variation of the sets of variables. The results indicate that the proportions of otherwise identical landscapes in Baicheng and Tekes were very different. The area of the grassland in Tekes was much larger than that of the cropland; however, the differences between the grassland and cropland in Baicheng were not as pronounced. Much of the grassland in Tekes was located in an area that was near residents, whereas most of the grassland in Baicheng was far from residents. The slope, elevation, annual precipitation, annual temperature, and distance to the nearest resident were strong driving forces influencing the patterns and transitions of the landscapes. The results of the variation partitioning indicated complex interrelationships among all of the pairs of driver sets. All of the variable sets had significant explanatory roles, most of which had both unique and shared variations with the others. The results of this study can assist policy makers and planners in implementing sustainable landscape management and effective protection strategies. 相似文献
13.
Spatial predictions of forest variables are required for supporting modern national and sub-national forest planning strategies, especially in the framework of a climate change scenario. Nowadays methods for constructing wall-to-wall maps and calculating small-area estimates of forest parameters are becoming essential components of most advanced National Forest Inventory (NFI) programs. Such methods are based on the assumption of a relationship between the forest variables and predictor variables that are available for the entire forest area. Many commonly used predictors are based on data obtained from active or passive remote sensing technologies. Italy has almost 40% of its land area covered by forests. Because of the great diversity of Italian forests with respect to composition, structure and management and underlying climatic, morphological and soil conditions, a relevant question is whether methods successfully used in less complex temperate and boreal forests may be applied successfully at country level in Italy.For a study area of more than 48,657 km2 in central Italy of which 43% is covered by forest, the study presents the results of a test regarding wall-to-wall, spatially explicit estimation of forest growing stock volume (GSV) based on field measurement of 1350 plots during the last Italian NFI. For the same area, we used potential predictor variables that are available across the whole of Italy: cloud-free mosaics of multispectral optical satellite imagery (Landsat 5 TM), microwave sensor data (JAXA PALSAR), a canopy height model (CHM) from satellite LiDAR, and auxiliary variables from climate, temperature and precipitation maps, soil maps, and a digital terrain model.Two non-parametric (random forests and k-NN) and two parametric (multiple linear regression and geographically weighted regression) prediction methods were tested to produce wall-to-wall map of growing stock volume at 23-m resolution. Pixel level predictions were used to produce small-area, province-level model-assisted estimates. The performances of all the methods were compared in terms of percent root mean-square error using a leave-one-out procedure and an independent dataset was used for validation. Results were comparable to those available for other ecological regions using similar predictors, but random forests produced the most accurate results with a pixel level R2 = 0.69 and RMSE% = 37.2% against the independent validation dataset. Model-assisted estimates were more precise than the original design-based estimates provided by the NFI. 相似文献
14.
太阳光入射与地表、大气相互作用,会在天空中出现较为稳定的天空偏振模式图,即以太阳为中心,天空中的偏振信号呈现一定的规律分布。影响天空偏振模式图的强度及形态主要由地表反射性质、空气分子散射性质及气溶胶光学性质共同影响。本研究利用矢量辐射传输模型,以海洋下垫面为例,在获取沙尘非球形与煤烟非球型气溶胶单次散射性质的基础上,模拟了不同气溶胶光学厚度情况下的全天空偏振模式图。结果表明,天空以太阳入射方向为中心呈现一个较为稳定的天空偏振模式图。沙尘型散射气溶胶光学厚度的增大会减弱该模式图的强度,而煤烟型吸收气溶胶光学厚度的增大会增大该模式图的强度。利用双模态(沙尘型与煤烟型混合)气溶胶模型,系统分析不同气溶胶比例情况下的天空偏振模式图,结果表明全天空偏振模式图的基本模态依旧存在,但是其强度受气溶胶模型与光学厚度双重影响。因此在利用全天空偏振模式图进行气溶胶光学性质反演时需要注意气溶胶模态信息的选择。 相似文献
15.
提出一种基于多目标微观邻域粒子群的土壤空间优化抽样方法。方法面向土壤空间调查的多目标特征,构建了基于最小克里金方差（MKV）和极大熵准则（ME）的粒子群多目标适应度函数,设计了最小样本量限制、样点可达性、采样成本限制和最小空间关联性四类粒子微观邻域操作策略,能高效协调土壤空间抽样精度、代表性、成本、样本量与样点布局等多目标冲突。实验结果表明,相比单目标粒子群算法和模拟退火算法,该方法的目标冲突协同能力强、收敛效率高,所设计抽样方案最优,为土壤质量精确调查与高效监测提供了技术支持。 相似文献
16.
Sudhir Kumar Singh Szilárd Szabó George P. Petropoulos Manika Gupta Tanvir Islam 《国际地球制图》2017,32(2):113-127
Analysis of Earth observation (EO) data, often combined with geographical information systems (GIS), allows monitoring of land cover dynamics over different ecosystems, including protected or conservation sites. The aim of this study is to use contemporary technologies such as EO and GIS in synergy with fragmentation analysis, to quantify the changes in the landscape of the Rajaji National Park (RNP) during the period of 19 years (1990–2009). Several statistics such as principal component analysis (PCA) and spatial metrics are used to understand the results. PCA analysis has produced two principal components (PC) and explained 84.1% of the total variance, first component (PC1) accounted for the 57.8% of the total variance while the second component (PC2) has accounted for the 26.3% of the total variance calculated from the core area metrics, distance metrics and shape metrics. Our results suggested that notable changes happened in the RNP landscape, evidencing the requirement of taking appropriate measures to conserve this natural ecosystem. 相似文献
17.
Basnyat P. McConkey B. Meinert B. Gatkze C. Noble G. 《Geoscience and Remote Sensing Letters, IEEE》2004,1(1):7-10
Successfully delineating management zones that differ in crop productivity is an important component of site-specific management. We compared the effectiveness of the digitally scanned color aerial infrared photographs and digital Landsat Thematic Mapper (TM) data for delineating within-field zones. The zones delineated using normalized difference vegetation index (NDVI) from TM data explained 34% of field yield variance compared with 9% for that from digitally scanned color aerial infrared photograph data. The zones from NDVI using Landsat TM were better able to capture spatial differences in plant growth and relatively stable soil attribute of surface soil organic carbon. 相似文献
18.
Biological soil crusts (BSCs) modify numerous soil surface properties and affect many key ecosystem processes. As BSCs are considered one of the most important components of semiarid ecosystems, accurate characterisation of their spatial distribution is increasingly in demand. This paper describes a novel methodology for identifying the areas dominated by different types of BSCs and quantifying their relative cover at subpixel scale in a semiarid ecosystem of SE Spain. The approach consists of two consecutive steps: (i) First, Support Vector Machine (SVM) classification to identify the main ground units, dominated by homogenous surface cover (bare soil, cyanobacteria BSC, lichen BSC, green and dry vegetation), which are of strong ecological relevance. (ii) Spectral mixture analysis (SMA) of the ground units to quantify the proportion of each type of surface cover within each pixel, to correctly characterize the complex spatial heterogeneity inherent to semiarid ecosystems. SVM classification showed very good results with a Kappa coefficient of 0.93%, discriminating among areas dominated by bare soil, cyanobacteria BSC, lichen BSC, green and dry vegetation. Subpixel relative abundance images achieved relatively high accuracy for both types of BSCs (about 80%), whereas general overestimation of vegetation was observed. Our results open the possibility of introducing the effect of presence and of relative cover of BSCs in spatially distributed hydrological and ecological models, and assessment and monitoring aimed at reducing degradation in these areas. 相似文献
19.
WOFOST模型与遥感数据同化的土壤速效养分反演 总被引：1，自引：1，他引：1
土壤速效养分是作物生长的必要条件,合理控制土壤速效养分含量对粮食增产、农民增收以及环境保护都有重要意义。随着现代农业技术的发展,可以通过变量施肥将土壤速效养分含量控制在最佳状态,这也对土壤养分的获取精度提出了更高的要求。当前的主要土壤速效养分遥感监测方法在监测精度、稳定性、成本控制和可推广性依然存在一定不足,甚至限制对变量施肥的指导作用。本文针对传统土壤速效养分估算方法的不足,提出了利用作物模型与时间序列遥感数据相结合实现耕层土壤速效养分反演的新思路,该思路以养分缺失引起的作物长势参数的变化为切入点,在数据同化算法设计和养分模块优化改造的基础上,利用作物长势参数遥感监测结果与模型模拟结果的差异设计了土壤速效养分反演算法,实现速效养分含量信息的有效获取。设计地面观测实验并利用地面观测数据对反演精度进行评价,结果表明该方法可以对土壤中的速效养分进行实时、高精度的稳定反演,3种主要的速效养分速效氮、有效磷和速效钾的R2分别达到了0.68、0.74和0.52,平均相对误差分别为7.45%、6.17%和9.97%。 相似文献
20.
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