Temporal change in the landscape erosion pattern in the Yellow River Basin,China

Using Landsat TM data from 1995 and 2000, changes in the landscape erosion pattern of the Yellow River Basin, China were analysed. The aim was to improve our understanding of soil‐erosion change so that sustainable land use could be established. First, a soil‐erosion intensity index model was developed to study soil‐erosion intensity change in the study area. Over the 5 years, the areas of weak erosion, moderate erosion, severe erosion, and very severe erosion all increased. The area of weak erosion increased dramatically by 7.94×10⁵ ha, and areas of slight erosion and acute erosion decreased by 1.93×10⁶ ha and 4.50×10⁴ ha, respectively. The results show that while the intensity of soil erosion has gradually been decreasing as a whole, in some regions the soil erosion is becoming more severe. Based on landscape indices, the pattern of changes in soil erosion over the past 5 years was analysed. The changes in landscape pattern of soil erosion resulted from human activities. Analysis showed that human impact increases fragmentation, having three major effects on landscape pattern, reduction in patch area, variations in patch shape, and changes in spatial pattern. In the study area, population growth, farming, governmental policy and forest degradation are the major factors causing soil erosion change over a 5‐year period.     

Marine GIS: Identification of mesoscale oceanic thermal fronts

A new method (the ‘sink’ method) is proposed for the mapping of productive mesoscale oceanic thermal fronts based on the combined analysis of satellite imagery for sea surface temperature (SST) distribution and chlorophyll (CHL) concentration under a Geographic Information System (GIS). In an SST lattice data array, data value sinks describe heterogeneous drops in SST distribution as this is compared to more homogeneously distributed SST in the surrounding area. Using Arc/Info GIS, these thermal discontinuities are flagged and applied on SST and CHL imagery for the calculation of differences in SST and CHL patterns (DSST and DCHL) inside and outside of these flagged areas. Spatially connected sinks that are characterized by simultaneous negative DSST and positive DCHL pattern are mapped as thermal fronts. Results include a time series of monthly front occurrence GIS maps in Eastern Mediterranean waters along with three calculated front characteristics (DSST, DCHL and bathymetry). Comparisons between the mapped fronts and isobath distributions present clearly visible front‐to‐isobath spatial and shape associations while spatial analysis between front and wind data explains 66% of the number of mapped fronts.     

Remote sensing based water quality monitoring in Chivero and Manyame lakes of Zimbabwe

Lakes Chivero and Manyame are amongst Zimbabwe’s most polluted inland water bodies. MEdium Resolution Imaging Spectrometry level 1b full resolution imagery for 2011 and 2012 were used to derive chlorophyll-a (chl_a) and phycocyanin (blue-green algae) concentrations using a semi-empirical band ratio model; total suspended matter (TSM) concentrations were derived from the MERIS processor. In-situ measured chl_a was used to validate the remotely sensed values. Results indicate that remote sensing measurements are comparable with in situ measurements. A strong positive correlation (R² = 0.91; MAE = 2.75 mg/m³ (8.5%)) and p < 0.01 (highly significant)) between measured and modeled chl_a concentrations was obtained. Relationships between optically active water constituents were assessed. Measured chl_a correlated well with MERIS modeled phycocyanin (PC) concentration (R² = 0.9458; p < 0.01 (highly significant)) whilst chl_a and TSM gave (R² = 0.7344; p < 0.05 (significant)). Modeled TSM and PC concentrations manifested a good relationship with each other (R² = 9047; p < 0.001 (very highly significant)). We conclude that remote sensing data allow simultaneous retrieval of different water quality parameters as well as providing near real time and space results that can be used by water managers and policy makers to monitor water bodies.     

利用井中低频偶极横波进行声波远探测的新方法

为了突破目前声波远探测技术存在的局限性,提出了一种新的偶极横波远探测方法,即利用井中偶极子产生的井中弯曲波存在低频截止频率的现象,在声源截止频率以下激发偶极声波.通过对比分析井中偶极声源分别在截止频率上、下激发时,井孔内外产生的辐射声场,明确了截止频率以下井中偶极声源的远场辐射特征和低频截止频率激发偶极横波的优势,结合数值模拟,进一步对其反射声场进行了分析.结果表明,该方法可以避免艾里相的巨大振幅对数据量化产生的"饱和"效应,相比传统的远探测测井方式更具优势,常规源距即可满足专门的远探测测井仪器需求.     

浙江沿海20年土地利用变化分析

在资料收集的基础上,利用遥感(RS)和地理信息系统(GIS)技术,结合经济社会因子和数学模型,以浙江沿海地区1990年、2000年和2010年的土地利用数据为基础,对20年间土地利用变化动态进行研究。利用灰色动态系统建立模型分析浙江沿海地区各类土地利用的变化趋势,在此基础上探讨其变化驱动力,揭示其变化的主要方式、原因及发展方向,预测未来土地利用的发展趋势,以期能为浙江省沿海地区土地利用规划提供科学依据。     

The Impact of Geology of Recharge Areas on Groundwater Quality: A Case Study of Zhob River Basin,Pakistan

Groundwater is a major source of water supply for domestic and irrigation uses in semiarid, remote but rapidly developing Kilasaifullah district part of Zhob River Basin, located at Pakistan–Afghanistan Border. Zhob River is among few major rivers of perennial nature in Balochistan, which flows from WSW to ENE and falls in Gomal River, a tributary of Indus River. Keeping in view the important geopolitical position and rapid development of the region, this study is primarily focused on groundwater chemistry for contamination sources as well as agriculture development. Water samples from open and tube wells are analyzed and calculated for electrical conductivity (EC), total dissolved solids (TDS), turbidity, pH, K⁺, Na⁺, Ca²⁺, Mg²⁺, HCO, Cl^?, NO, SO, PO, sodium percent (Na%), sodium adsorption ratio (SAR), Kelly's index (KI), and heavy metals (Fe, Cu, Cr, Zn, Pb, and Mn). On the basis of the chemical constituents two zones within the study area are identified and possible causes of the contaminants are pointed out. Two recharge areas were responsible for the different chemical results in groundwater, e.g., zone A was recharged from NNW saline geological formations (Nisai, Khojak, Multana, Bostan formations, and Muslim Bagh ophiolites), which are concentrated with high sodium and chloride. On the other hand Zone B was sourced from SSW from carbonate rich rocks (Alozai, Loralai, Parh formations, and Muslim Bagh ophiolites). The groundwater is classified as C2–S1, C3–S1, C3–S2, C4–S2 on the basis of EC and SAR values which indicate that most of the water of both zones can be used for irrigation safely except the samples plotted in C3–S2 and C4–S2 categories which could be dangerous for soil and crops. Groundwater samples are plotted in good to permissible limits with some samples excellent to good and few samples belong to doubtful category based on sodium percent. Groundwater of zone A is unsuitable for irrigation use due to higher values of KI (more than one) but water of zone B are good for irrigation based on KI. In general, water of both zones is suitable for irrigation but care should be taken during the selection of crops which are sensitive to alkalinity or sodium hazards particularly in zone A.     

Discharge and suspended sediment flux estimated along the mainstream of the Amazon and the Madeira Rivers (from in situ and MODIS Satellite Data)

Water and suspended sediment fluxes are considered during the period 2000–2008 in a region including the full Amazon River from the confluence of the Negro River to Santarém, the end part of the Solimões River, and the lower part of the Madeira River. Three types of data are used: water discharge estimated from field measurements, and suspended sediment obtained from field measurements and derived from MODIS satellite data. A generalized least square method including a propagating term is developed in order to propagate the signal upward and downward the river. The approach is introduced and tested. Several experiments are considered in order, first, to estimate the ability to propagate the signal from stations located before the confluences of Negro and Madeira Rivers to stations located on the Amazon River; second to investigate the possibility to propagate the signal along the Amazon River which dynamics is coupled with floodplains dynamics; and third to produce optimal solutions of water and sediment fluxes. For each experiment, the influence of field and satellite data is compared. The approach is efficient in the upper part of the region of study where the Solimões, the Negro and the Madeira Rivers meet and fails in the lower part of the region where interactions between Amazon River and floodplains play an important role on the fluxes’ dynamics. The optimal experiment includes in situ and satellite data from all the stations available and is used to analyse the recent evolution of suspended sediment flux along the Amazon River and its interaction with the large coupled floodplains. A high accumulation rate is observed during the 2000–2002 period, followed by decreasing rates until 2005 and by increasing values in 2006 and 2007. Our results suggest that floodplains extending along a river reach of 390 km-long between Itacoatiara and Óbidos trap about 15% of the suspended sediment flux passing at Óbidos. The simulated deposition rate is of about 0.3 Mt km⁻¹ yr⁻¹ corresponding to an accretion rate of about 27 mm yr⁻¹.     

A novel approach to estimate canopy height using ICESat/GLAS data: A case study in the New Forest National Park,UK

The Geoscience Laser Altimeter System (GLAS) aboard Ice, Cloud and land Elevation Satellite (ICESat) is a spaceborne LiDAR sensor. It is the first LiDAR instrument which can digitize the backscattered waveform and offer near global coverage. Among others, scientific objectives of the mission include precise measurement of vegetation canopy heights. Existing approaches of waveform processing for canopy height estimation suggest Gaussian decomposition of the waveform which has the limitation to properly characterize significant peaks and results in discrepant information. Moreover, in most cases, Digital Terrain Models (DTMs) are required for canopy height estimation. This paper presents a new automated method of GLAS waveform processing for extracting vegetation canopy height in the absence of a DTM. Canopy heights retrieved from GLAS waveforms were validated with field measured heights. The newly proposed method was able to explain 79% of variation in canopy heights with an RMSE of 3.18 m, in the study area. The unexplained variation in canopy heights retrieved from GLAS data can be due to errors introduced by footprint eccentricity, decay of energy between emitted and received signals, uncertainty in the field measurements and limited number of sampled footprints.Results achieved with the newly proposed method were encouraging and demonstrated its potential of processing full-waveform LiDAR data for estimating forest canopy height. The study also had implications on future full-waveform spaceborne missions and their utility in vegetation studies.     

Clustering based on eigenspace transformation – CBEST for efficient classification

Large remote sensing datasets, that either cover large areas or have high spatial resolution, are often a burden of information mining for scientific studies. Here, we present an approach that conducts clustering after gray-level vector reduction. In this manner, the speed of clustering can be considerably improved. The approach features applying eigenspace transformation to the dataset followed by compressing the data in the eigenspace and storing them in coded matrices and vectors. The clustering process takes the advantage of the reduced size of the compressed data and thus reduces computational complexity. We name this approach Clustering Based on Eigen-space Transformation (CBEST). In our experiment with a subscene of Landsat Thematic Mapper (TM) imagery, CBEST was found to be able to improve speed considerably over conventional K-means as the volume of data to be clustered increases. We assessed information loss and several other factors. In addition, we evaluated the effectiveness of CBEST in mapping land cover/use with the same image that was acquired over Guangzhou City, South China and an AVIRIS hyperspectral image over Cappocanoe County, Indiana. Using reference data we assessed the accuracies for both CBEST and conventional K-means and we found that the CBEST was not negatively affected by information loss during compression in practice. We discussed potential applications of the fast clustering algorithm in dealing with large datasets in remote sensing studies.     

Change detection from remotely sensed images: From pixel-based to object-based approaches

The appetite for up-to-date information about earth’s surface is ever increasing, as such information provides a base for a large number of applications, including local, regional and global resources monitoring, land-cover and land-use change monitoring, and environmental studies. The data from remote sensing satellites provide opportunities to acquire information about land at varying resolutions and has been widely used for change detection studies. A large number of change detection methodologies and techniques, utilizing remotely sensed data, have been developed, and newer techniques are still emerging. This paper begins with a discussion of the traditionally pixel-based and (mostly) statistics-oriented change detection techniques which focus mainly on the spectral values and mostly ignore the spatial context. This is succeeded by a review of object-based change detection techniques. Finally there is a brief discussion of spatial data mining techniques in image processing and change detection from remote sensing data. The merits and issues of different techniques are compared. The importance of the exponential increase in the image data volume and multiple sensors and associated challenges on the development of change detection techniques are highlighted. With the wide use of very-high-resolution (VHR) remotely sensed images, object-based methods and data mining techniques may have more potential in change detection.