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Terrestrial water storage (TWS) is an essential element of the water cycle and a key state variable for land surface–atmosphere interaction. This study investigates the changes of TWS in Xinjiang of China in response to climate change, using Gravity Recovery and Climate Experiment (GRACE)-derived data and ground hydrological/meteorological observations in 2003–2015. The results show that the TWS has an overall significant decreasing trend of −2.8 mm per year. TWS is in a state of surplus in the first half year (from February to July), while it is in a state of deficit in the second half year (from August to next January). The change rates in TWS exhibit strong regional disparities, with profound decreasing trends in Tianshan Mountains and increasing in Kunlun Mountains. Further analyses suggest that the spatial-temporal change of TWS was closely related to the variations in seasonal snow melt and glacier retreat due to temperature and precipitation changes. [ABSTRACT FROM AUTHOR]

Timely and reliable estimation of regional crop yield is a vital component of food security assessment, especially in developing regions. The traditional crop forecasting methods need ample time and labor to collect and process field data to release official yield reports. Satellite remote sensing data is considered a cost-effective and accurate way of predicting crop yield at pixel-level. In this study, maximum Enhanced Vegetation Index (EVI) during the crop-growing season was integrated with Machine Learning Regression (MLR) models to estimate wheat and rice yields in Pakistan's Punjab province. Five MLR models were compared using a fivefold cross-validation method for their predictive accuracy. The study results revealed that the regression model based on the Gaussian process outperformed over other models. The best performing model attained coefficient of determination (R2), Root Mean Square Error (RMSE, t/ha), and Mean Absolute Error (MAE, t/ha) of 0.75, 0.281, and 0.236 for wheat; 0.68, 0.112, and 0.091 for rice, respectively. The proposed method made it feasible to predict wheat and rice 6–8 weeks before the harvest. The early prediction of crop yield and its spatial distribution in the region can help formulate efficient agricultural policies for sustainable social, environmental, and economic progress. [ABSTRACT FROM AUTHOR]

High-performance simulation of flow dynamics remains a major challenge in the use of physical-based, fully distributed hydrologic models. Parallel computing has been widely used to overcome efficiency limitation by partitioning a basin into sub-basins and executing calculations among multiple processors. However, existing partition-based parallelization strategies are still hampered by the dependency between inter-connected sub-basins. This study proposed a particle-set strategy to parallelize the flow-path network (FPN) model for achieving higher performance in the simulation of flow dynamics. The FPN model replaced the hydrological calculations on sub-basins with the movements of water packages along the upstream and downstream flow paths. Unlike previous partition-based task decomposition approaches, the proposed particle-set strategy decomposes the computational workload by randomly allocating runoff particles to concurrent computing processors. Simulation experiments of the flow routing process were undertaken to validate the developed particle-set FPN model. The outcomes of hourly outlet discharges were compared with field gauged records, and up to 128 computing processors were tested to explore its speedup capability in parallel computing. The experimental results showed that the proposed framework can achieve similar prediction accuracy and parallel efficiency to that of the Triangulated Irregular Network (TIN)-based Real-Time Integrated Basin Simulator (tRIBS). [ABSTRACT FROM AUTHOR]

Landslides are one of the most destructive disasters that cause damage to both property and life every year. The mountainous terrain, heavy and prolonged rainfall and dense development near steep hillsides make Hong Kong as one of the most vulnerable metropolitans to the risk of landslides. Therefore, regional-specific landslide susceptibility assessment in Hong Kong is necessary for hazard management and effective land use planning. Various methodologies have been proposed for landslide susceptibility mappings. By applying the statistically based frequency ratio and logistic regression models in Lantau Island, Hong Kong, this study attempts to add extra value to the literature of evaluating their ‘prediction rate’ (rather than ‘success rate’) for landslide susceptibility mapping in a temporal context. The spatial relationship among landslide occurrence and nine causative factors (elevation, slope, aspect, plan curvature, profile curvature, normalized difference vegetation index (NDVI), distance to river, distance to fault and lithology) were examined, and landslide susceptibility maps were generated by frequency ratio and logistic regression model. Validations of the mapping results were performed by calculating relative operating characteristics (ROC). Virtually all previous studies randomly extracted and reserved a portion of historical landslide records to perform the model evaluation. In contrast, in this study, 1864 (70%) landslide records in the Enhanced Natural Terrain Landslide Inventory from 2000 to 2008 are utilized to train the model, while the subsequent 799 (30%) landslides occurred from 2008 to 2009 are used for model validation. The validation result shows that logistic regression model (84.05%) possesses a better prediction power than frequency ratio model (76.64%) for the study area. This study attempts to offer a new model assessment strategy for landslide susceptibility mapping. The resultant maps also provide a scientific assessment of the risk areas with respect to landslides on Lantau Island and could serve as a basis for decisions or justification of the Lantau development planning. [ABSTRACT FROM PUBLISHER]

This paper proposes a flow-path network (FPN) model to simulate complex surface flow based on a drainage-constrained triangulated irregular network (TIN). The TIN was constructed using critical points and drainage lines extracted from a digital terrain surface. Runoff generated on the surface was simplified as ‘water volumes’ at constrained random points that were then used as the starting points of flow paths (i.e. flow source points). The flow-path for each ‘water volume’ was constructed by tracing the direction of flow from the flow source point over the TIN surface to the stream system and then to the outlet of the watershed. The FPN was represented by a set of topologically defined one-dimensional line segments and nodes. Hydrologic variables, such as flow velocity and volume, were computed and integrated into the FPN to support dynamic surface flow simulation. A hypothetical rainfall event simulation on a hilly landscape showed that the FPN model was able to simulate the dynamics of surface flow over time. A real-world catchment test demonstrated that flow rates predicted by the FPN model agreed well with field observations. Overall, the FPN model proposed in this study provides a vector-based modeling framework for simulating surface flow dynamics. Further studies are required to enhance the simulations of individual hydrologic processes such as flow generation and overland and channel flows, which were much simplified in this study. [ABSTRACT FROM PUBLISHER]

Due to the coarse spatial resolution of imagery and heterogeneity of snow physical parameters, the currently available snow depth data sets derived from passive microwave sensors have shown significant accuracy variations in different parts of the world. This study aims to analyse the reliability of existing remote sensing snow depth products for the arid zone of China. Two long-term products were compared and evaluated including the GlobSnow Snow Water Equivalent product (‘the GlobSnow product’) and the Long-term Snow Depth Dataset of China (‘the WestDC product’). Nine-year ground measurements from 35 sampling sites with diverse topographical conditions were used as ground references for the accuracy assessment. Statistical analysis methods such as intra-class correlation coefficient and root mean square error (RMSE) were adopted for the consistency test and accuracy assessment. Analysis of variance (ANOVA) was employed to examine whether the data reliability varied by seasonal or locational factors. The results show that the two products in general do not agree well in the study region. However, the generalized data with different temporal resolutions tend to yield better results in terms of data consistency and accuracy. Compared to the GlobSnow product, the WestDC product has shown a better accuracy with the RMSE of 8.23, 7.43, and 6.56 cm for the original daily, and generalized weekly, and monthly data, respectively. According to the ANOVA test, season and latitude show statistically significant impacts on data reliability, while altitude and terrain complexity do not. Data reliability declines with higher latitude and it rapidly falls down to an unacceptable level in snow melting periods. This study provides a quantitative assessment on the quality of the snow depth products and raises the awareness of data consistency issues of these products for regional applications. [ABSTRACT FROM AUTHOR]

A scale-adaptive digital elevation model (S-DEM) method is proposed for multi-scale terrain analysis using a single high-resolution digital elevation model (DEM) database. The motivation is to construct a DEM that is self-adaptive to a given scale of an application, rather than letting the application fit into the built-in scale of the DEM. The method is based on an adaptive compound point extraction (CPE) algorithm that extracts surface ‘significant points’ from a high-resolution DEM according to their degree of importance (DOI) to the scale of an application. A data structure can be established to match the demand from an application at a coarser scale. Based on the data structure, a triangulated irregular network (TIN) model can be generated to support the terrain analysis at the desired scale. The aim of the S-DEM is to support multi-scale applications in three aspects, namely, ‘one database for all scales and scale-adaptive’ (i.e. matching any application scale using a single high-resolution DEM), ‘consistent measurement’ (i.e. delivering more constant measurements of terrain parameters with changing scales), and ‘skeleton preservation’ (i.e. preserving basic streamlines with changing scales). Compared with the raster resampling algorithm and the maximum z-tolerance algorithm, we find that the proposed method offers better performance, providing values that meet the accuracy requirements set by DEM data standards for different scales, and producing analytical derivatives that retain terrain features with consistent measurements of terrain parameters. [ABSTRACT FROM PUBLISHER]

The urban fringe is the transition zone between urban land use and rural land use. It represents the most active part of the urban expansion process. Change detection using multi-temporal imagery is proven to be an efficient way to monitor land-use/land-cover change caused by urban expansion. In this study, we propose a new multi-temporal classification method for change detection in the urban fringe area. The proposed method extracts and integrates spatio-temporal contextual information into multi-temporal image classification. The spatial information is extracted by object-oriented image segmentation. The temporal information is modelled with temporal trajectory analysis with a two-step calibration. A probabilistic schema that employs a global membership function is then used to integrate the spectral, spatial and temporal information. A trajectory accuracy measurement is proposed to assist the comparison on the performances of the integrated spatio-temporal method and classical pixel- and ‘snapshot’-based classification methods. The experiment shows that the proposed method can significantly improve the accuracies of both single scene classification and temporal trajectory analysis. [ABSTRACT FROM PUBLISHER]

This study develops a practical methodology to assess the accuracy of multi-temporal change detection using a trajectory error matrix (TEM). In this error matrix one axis represents the land-cover change trajectory categories derived from single-date classified images, and the other represents the land-cover change trajectories identified from reference data. The overall accuracies of change trajectories and states of change/no-change are used as indices for accuracy assessment. As the number of possible land-cover change trajectories can be enormous, a practical processing flow for computing accuracy assessment indices has also been developed to avoid listing all possible change trajectories in the error matrix. A case study using this method was conducted to assess the accuracy of land-cover change over a period with five observations in a study area in China's arid zone. This method simplifies the process of estimating overall accuracy in the change trajectory analysis, and provides a more realistic and detailed assessment of the results of multi-temporal change detection using post-classification comparison methods. [ABSTRACT FROM AUTHOR]

This study attempts to develop a methodology to quantify spatial patterns of land cover change using landscape metrics. First, multitemporal land cover types are derived based on a unified land cover classification scheme and from the classification of multitemporal remotely sensed imagery. Categorical land cover change trajectories are then established and reclassified according to the nature and driving forces of the change. Finally, spatial pattern metrics of the land cover change trajectory classes are computed and their relationships to human activities and environmental factors are analysed. A case study in the middle reach of Tarim River in the arid zone of China from 1973 to 2000 shows that during the 30-year study period, the natural force is dominant in environmental change, although the human impact through altering water resources and surface materials has increased dramatically in recent years. The human-induced change trajectories generally show lower normalized landscape shape index (NLSI), interspersion and juxtaposition index (IJI) and area-weighted mean patch fractal dimension (FARC_AM), indicating greater aggregation, less association with others and simpler and larger patches in shape, respectively. The results suggest that spatial pattern metrics of land cover change trajectories can provide a good quantitative measurement for better understanding of the spatio-temporal pattern of land cover change due to different causes. [ABSTRACT FROM AUTHOR]

The objective of this research is to study the relationship between terrain complexity and terrain analysis results from grid‐based digital elevation models (DEMs). The impact of terrain complexity represented by terrain steepness and orientation on derived parameters such as slope and aspect has been analysed. Experiments have been conducted to quantify the uncertainties created by digital terrain analysis algorithms. The test results show that (a) the RMSE of derived slope and aspect is negatively correlated with slope steepness; (b) the RMSE of derived aspect is more sensitive to terrain complexity than that of derived slope; and (c) the uncertainties in derived slope and aspect tend to be found in flatter areas, and decrease with increasing terrain complexity. The study shows that although primary surface parameters can be well defined mathematically, the implementation of those mathematical models in a GIS environment may generate considerable uncertainties related to terrain complexity. In general, when terrain is rugged with steep slopes, the uncertainty of derived parameters is quite minimal. While in flatter areas, the DEM‐based derivatives, particularly the aspect, may contain a great amount of uncertainty, causing significant limitation in applying the analytical results. [ABSTRACT FROM AUTHOR]

This paper reports an investigation on the accuracy of grid-based routing algorithms used in hydrological models. A quantitative methodology has been developed for objective and data-independent assessment of errors generated from the algorithms that extract hydrological parameters from gridded DEM. The generic approach is to use artificial surfaces that can be described by a mathematical model, thus the 'true' output value can be pre-determined to avoid uncertainty caused by uncontrollable data errors. Four mathematical surfaces based on an ellipsoid (representing convex slopes), an inverse ellipsoid (representing concave slopes), saddle and plane were generated and the theoretical 'true' value of the Specific Catchment Area (SCA) at any given point on the surfaces could be computed using mathematical inference. Based on these models, tests were made on a number of algorithms for SCA computation. The actual output values from these algorithms on the convex, concave, saddle and plane surfaces were compared with the theoretical 'true' values, and the errors were then analysed statistically. The strengths and weaknesses of the selected algorithms are also discussed. [ABSTRACT FROM AUTHOR]

At present, application of GIS network analysis for agricultural drainage management is limited, although the nature of water flow within a drainage network is by and large in a similar way to that of, for example, the traffic flow over a road network. This is due to the limitation of the current GIS network model, which has difficulties in handling the complexity and event-based dynamic nature of the drainage network. This research therefore focuses on drainage management problems in a floodplain where drainage is controlled by the natural and artificial drainage networks. GIS network capabilities were employed, with enhancements of specific needs for the drainage simulation. A specialized three-dimensional network model was developed to allow computation of important parameters for network analysis, making dynamic simulation of drainage corresponding to individual rainfall events possible. Test simulation results indicate that the simulation tends to overestimate the discharge volume and pollutant, but the relative error level is low with an average of 7.3% and maximum of 12.7%. [ABSTRACT FROM AUTHOR]

In the real estate business, it is a common understanding that the value and potential of a property are fundamentally determined by its location. This emphasises the significance of spatial factors in decision making in real estate. A geographical information system (GIS) is undoubtedly useful in this decision making. This paper reports on the development of a prototype real estate GIS (REGIS) by integrating fuzzy set (FZ) theory, a rule-based system (RBS) and GIS. The role of this system in the real estate business is in assisting decision makers for sellers and buyers, as well as property managers. For real estate agencies, the system can be used as an aid to selling and managing properties. For the buyers, the REGIS can function as a consultant in their decision making in purchasing properties. Methodologies are demonstrated using case studies. Developed as a generic tool with capabilities to deal with uncertainties, this prototype REGIS can also be applied to other fields, which involves optimal spatial decision making. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including digital terrain analysis and modeling, the definition of the third-order geomorphometric variables, and the expert knowledge-based approach to landslide susceptibility mapping.