A-Xing Zhu

A similarity model for representing soil spatial information

Abstract A fuzzy logic based model (called a similarity model) was developed to represent soil spatial information so that soil landscape is perceived as a continuum in both the parameter space and the geographic space. The similarity model consists of two components: the similarity representation component and a raster representation scheme. The similarity representation component uses a set of prescribed soil taxonomic categories as the central concepts of the fuzzy soil classes and represents a soil at a given location as a set of similarity values to these central concepts. The collection of these similarity values forms an n-element vector called a soil similarity vector. With the use of a raster representation scheme, soil spatial information over an area can be represented as an array of soil similarity vectors. This similarity model has two main advantages for representing spatial soil information over conventional polygon-based soil maps. Firstly, the details of soil spatial information can be represented at the resolution of a raster data model rather than at the minimal mapping sizes as in conventional polygon-based soil maps. secondly, under the similarity representation, the deviation of a soil at a given location from typical soil classes can be preserved and its properties can then take values intermediate to the typical values of the prescribed soil types. A case study conducted in the Lubrecht Experiment Forest of western Montana demonstrated that soil spatial information represented under the similarity model has a higher resolution at both the attribute level and the spatial level than that in the conventional soil map of the area.

Prediction of Continental-Scale Evapotranspiration by Combining MODIS and AmeriFlux Data Through Support Vector Machine

Application of remote sensing data to extrapolate evapotranspiration (ET) measured at eddy covariance flux towers is a potentially powerful method to estimate continental-scale ET. In support of this concept, we used meteorological and flux data from the AmeriFlux network and an inductive machine learning technique called support vector machine (SVM) to develop a predictive ET model. The model was then applied to the conterminous U.S. In this process, we first trained the SVM to predict 2000-2002 ET measurements from 25 AmeriFlux sites using three remotely sensed variables [land surface temperature, enhanced vegetation index (EVI), and land cover] and one ground-measured variable (surface shortwave radiation). Second, we evaluated the model performance by predicting ET for 19 flux sites in 2003. In this independent evaluation, the SVM predicted ET with a root-mean-square error (rmse) of 0.62 mm/day (approximately 23% of the mean observed values) and an R2 of 0.75. The rmse from SVM was significantly smaller than that from neural network and multiple-regression approaches in a cross-validation experiment. Among the explanatory variables, EVI was the most important factor. Indeed, removing this variable induced an rmse increase from 0.54 to 0.77 mm/day. Third, with forcings from remote sensing data alone, we used the SVM model to predict the spatial and temporal distributions of ET for the conterminous U.S. for 2004. The SVM model captured the spatial and temporal variations of ET at a continental scale

Automated soil inference under fuzzy logic

Abstract Soil information is essential to any terrestrial ecological modelling and management activity. Polygon soil maps produced from soil surveys are currently the major source of information on the spatial distribution of soil properties for a variety of land analysis and management activity. However, there are some major problems regarding the use of current soil maps in geographic analysis and especially in geographic information systems (GIS). These problems include limited coverage at a fixed scale, locational errors, attribute errors, and insufficient information in the mapping units. Much of these problems are due to the crisp logic and cartographic techniques with which soil maps are produced. Under crisp logic standardly used in soil classification and mapping, an area belongs to one and only one soil mapping unit, and is separated from other mapping units by sharp boundary lines. However, soil in a landscape is a continuum and the discretization of such a continuum into distinct spatial and categorical groups results in a significant loss of information. This paper presents a methodology to infer and represent information on the spatial distribution of soil. The methodology combines fuzzy logic with GIS and expert system development techniques to infer soil series from environmental conditions. The methodology for every point in an area produces a soil similarity vector (SSV) showing the similarity of the soil at the point to a prescribed set of soil series. The SSV produced from this methodology can be used to infer local soil properties at values intermediate to the typical or central values assigned to each possible series. Preliminary results from the methodology using a limited set of environmental variables for an experimental watershed in Montana are presented.

Effects of spatial detail of soil information on watershed modeling

The impacts of detailed and spatially continuous soil information on hydro-ecological modeling over watersheds of mesoscale size are investigated. The impacts were assessed by comparing the simulated hydro-ecological responses based on the detailed soil spatial information derived from a fuzzy logic-based inference approach with those based on the soil information derived from a conventional soil map. This study reveals that the detailed soil spatial information has impacts on the simulated hydro-ecological responses under a lumped parameter approach. Peak runoff was reduced, yielding more realistic hydrographs for forested watersheds in the area. The detailed soil spatial information strongly impacted the simulation of net photosynthesis over the period when there is a moisture stress, but negligible impacts when there is sufficient water recharge to soil profiles. Simulation of hydro-ecological responses using a distributed parameter approach is less impacted by the detailed soil spatial information. The difference in simulated net photosynthesis under the distributed approach is smaller and also only occurred during the period of moisture stress. The impacts on spatial distribution of simulated transpiration occurred mainly over south facing slopes during the period of moisture stress.

An adaptive approach to selecting a flow-partition exponent for a multiple-flow-direction algorithm

Most multiple‐flow‐direction algorithms (MFDs) use a flow‐partition coefficient (exponent) to determine the fractions draining to all downslope neighbours. The commonly used MFD often employs a fixed exponent over an entire watershed. The fixed coefficient strategy cannot effectively model the impact of local terrain conditions on the dispersion of local flow. This paper addresses this problem based on the idea that dispersion of local flow varies over space due to the spatial variation of local terrain conditions. Thus, the flow‐partition exponent of an MFD should also vary over space. We present an adaptive approach for determining the flow‐partition exponent based on local topographic attribute which controls local flow partitioning. In our approach, the influence of local terrain on flow partition is modelled by a flow‐partition function which is based on local maximum downslope gradient (we refer to this approach as MFD based on maximum downslope gradient, MFD‐md for short). With this new approach, a steep terrain which induces a convergent flow condition can be modelled using a large value for the flow‐partition exponent. Similarly, a gentle terrain can be modelled using a small value for the flow‐partition exponent. MFD‐md is quantitatively evaluated using four types of mathematical surfaces and their theoretical ‘true’ value of Specific Catchment Area (SCA). The Root Mean Square Error (RMSE) shows that the error of SCA computed by MFD‐md is lower than that of SCA computed by the widely used SFD and MFD algorithms. Application of the new approach using a real DEM of a watershed in Northeast China shows that the flow accumulation computed by MFD‐md is better adapted to terrain conditions based on visual judgement.

Knowledge discovery from soil maps using inductive learning

This paper develops a knowledge discovery procedure for extracting knowledge of soil-landscape models from a soil map. It has broad relevance to knowledge discovery from other natural resource maps. The procedure consists of four major steps: data preparation, data preprocessing, pattern extraction, and knowledge consolidation. In order to recover true expert knowledge from the error-prone soil maps, our study pays specific attention to the reduction of representation noise in soil maps. The data preprocessing step has exhibited an important role in obtaining greater accuracy. A specific method for sampling pixels based on modes of environmental histograms has proven to be effective in terms of reducing noise and constructing representative sample sets. Three inductive learning algorithms, the See5 decision tree algorithm, Naïve Bayes, and artificial neural network, are investigated for a comparison concerning learning accuracy and result comprehensibility. See5 proves to be an accurate method and produces the most comprehensible results, which are consistent with the rules (expert knowledge) used in producing the soil map. The incorporation of spatial information into the knowledge discovery process is found not only to improve the accuracy of the extracted knowledge, but also to add to the explicitness and extensiveness of the extracted soil-landscape model.

A Markov chain-based probability vector approach for Modeling spatial uncertainties of soil classes

Integrating livestock with cotton (Gossypium hirsutum L.) offers profitable alternatives for producers in the southeastern USA, but could result in soil water depletion and soil compaction. We conducted a 3-yr field study on a Dothan loamy sand (fine-loamy, kaolinitic, thermic Plinthic Kandiudult) in southern Alabama to develop a conservation tillage system for integrating cotton with winter-annual grazing of stocker cattle under rainfed conditions. Winter annual forages and tillage systems were evaluated in a strip-plot design where winter forages were oat (Avena sativa L.) and annual ryegrass (Lolium mutiflorum L.). Tillage systems included moldboard and chisel plowing and combinations of noninversion deep tillage (none, in-row subsoil, or paratill) with or without disking. We evaluated forage dry matter, N concentration, average daily gain, net returns from grazing, soil water content, and cotton leaf stomatal conductance, plant populations, and yield. Net returns from winter-annual grazing were between US

Evaluation of Three Satellite Precipitation Products TRMM 3B42, CMORPH, and PERSIANN over a Subtropical Watershed in China

185 to US

A new approach to the nearest‐neighbour method to discover cluster features in overlaid spatial point processes

200 ha - yr - . Soil water content was reduced by 15% with conventional tillage or deep tillage, suggesting that cotton rooting was increased by these systems. Oat increased cotton stands by 25% and seed-cotton yields by 7% compared with ryegrass. Strict no-till resulted in the lowest yields-30% less than the overall mean (3.69 Mg ha -1 ). Noninversion deep tillage in no-till (especially paratill) following oat was the best tillage system combination (3.97 Mg ha -1 ) but deep tillage did not increase cotton yields with conventional tillage. Integrating winter-annual grazing can be achieved using noninversion deep tillage following oat in a conservation tillage system, providing producers extra income while protecting the soil resource.

How to Apply the Geospatial Data Abstraction Library (GDAL) Properly to Parallel Geospatial Raster I/O?

This study conducted a comprehensive evaluation of three satellite precipitation products (TRMM (Tropical Rainfall Measuring Mission) 3B42, CMORPH (the Climate Prediction Center (CPC)Morphing algorithm), and PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks)) using data from 52 rain gauge stations over the Meichuan watershed, which is a representative watershed of the Poyang Lake Basin in China. All the three products were compared and evaluated during a 9-year period at different spatial (grid and watershed) and temporal (daily, monthly, and annual) scales. The results showed that at daily scale, CMORPH had the best performance with coefficients of determination (?2) of 0.61 at grid scale and 0.74 atwatershed scale. For precipitation intensities larger than or equal to 25 mm,RMSE%ofCMORPHandTRMM3B42were less than 50%, indicating CMORPH and TRMM 3B42 might be useful for hydrological applications at daily scale. At monthly and annual temporal scales,TRMM3B42 had the best performances, with high ? 2 ranging from0.93 to 0.99, and thus was deemed to be reliable and had good potential for hydrological applications at monthly and annual scales. PERSIANN had the worst performance among the three products at all cases.