Open AccessArticle
Spatial Context from Open and Online Processing (SCOOP): Geographic, Temporal, and Thematic Analysis of Online Information Sources
ISPRS Int. J. Geo-Inf. 2017, 6(7), 193; doi:10.3390/ijgi6070193 -
Abstract
The Internet is increasingly a source of data for geographic information systems, as more data becomes linked, available through application programing interfaces (APIs), and more tools become available for handling unstructured web data. While many web data extraction and structuring methods exist, there
[...] Read more.
The Internet is increasingly a source of data for geographic information systems, as more data becomes linked, available through application programing interfaces (APIs), and more tools become available for handling unstructured web data. While many web data extraction and structuring methods exist, there are few examples of comprehensive data processing and analysis systems that link together these tools for geographic analyses. This paper develops a general approach to the development of spatial information context from unstructured and informal web data sources through the joint analysis of the data’s thematic, spatial, and temporal properties. We explore the utility of this derived contextual information through a case study into maritime surveillance. Extraction and processing techniques such as toponym extraction, disambiguation, and temporal information extraction methods are used to construct a semi-structured maritime context database supporting global scale analysis. Geographic, temporal, and thematic content were analyzed, extracted and processed from a list of information sources. A geoweb interface is developed to allow user visualization of extracted information, as well as to support space-time database queries. Joint keyword clustering and spatial clustering methods are used to demonstrate extraction of documents that relate to real world events in official vessel information data. The quality of contextual geospatial information sources is evaluated in reference to known maritime anomalies obtained from authoritative sources. The feasibility of automated context extraction using the proposed framework and linkage to external data using standard clustering tools is demonstrated. Full article
Figures

Figure 1

Open AccessArticle
Toward the Development of a Marine Administration System Based on International Standards
ISPRS Int. J. Geo-Inf. 2017, 6(7), 194; doi:10.3390/ijgi6070194 -
Abstract
The interests, responsibilities and opportunities of states to provide infrastructure and resource management are not limited to their land territory but extend to marine areas as well. So far, although the theoretical structure of a Marine Administration System (MAS) is based on the
[...] Read more.
The interests, responsibilities and opportunities of states to provide infrastructure and resource management are not limited to their land territory but extend to marine areas as well. So far, although the theoretical structure of a Marine Administration System (MAS) is based on the management needs of the various countries, the marine terms have not been clearly defined. In order to define an MAS that meets the spatial marine requirements, the specific characteristics of the marine environment have to be identified and integrated in a management system. Most publications that address the Marine Cadastre (MC) concept acknowledge the three-dimensional (3D) character of marine spaces and support the need for MC to function as a multipurpose instrument. The Land Administration Domain Model (LADM) conceptual standard ISO 19152 has been referenced in scholarly and professional works to have explicit relevance to 3D cadastres in exposed land and built environments. However, to date, very little has been done in any of those works to explicitly and comprehensively apply LADM to specific jurisdictional MAS or MC, although the standard purports to be applicable to those areas. Since so far the most comprehensive MC modeling approach is the S-121 Maritime Limits and Boundaries (MLB) Standard, which refers to LADM, this paper proposes several modifications including, among others, the introduction of class marine resources into the model, the integration of data on legal spaces and physical features through external classes, as well as the division of law and administrative sources. Within this context, this paper distinctly presents both appropriate modifications and applications of the IHO S-121 standard to the particular marine and maritime administrative needs of both Greece and the Republic of Trinidad and Tobago. Full article
Figures

Figure 1

Open AccessArticle
Cloud-Based Architectures for Auto-Scalable Web Geoportals towards the Cloudification of the GeoVITe Swiss Academic Geoportal
ISPRS Int. J. Geo-Inf. 2017, 6(7), 192; doi:10.3390/ijgi6070192 -
Abstract
Cloud computing has redefined the way in which Spatial Data Infrastructures (SDI) and Web geoportals are designed, managed, and maintained. The cloudification of a geoportal represents the migration of a full-stack geoportal application to an internet-based private or public cloud. This work introduces
[...] Read more.
Cloud computing has redefined the way in which Spatial Data Infrastructures (SDI) and Web geoportals are designed, managed, and maintained. The cloudification of a geoportal represents the migration of a full-stack geoportal application to an internet-based private or public cloud. This work introduces two generic and open cloud-based architectures for auto-scalable Web geoportals, illustrated with the use case of the cloudification efforts of the Swiss academic geoportal GeoVITe. The presented cloud-based architectural designs for auto-scalable Web geoportals consider the most important functional and non-functional requirements and are adapted to both public and private clouds. The availability of such generic cloud-based architectures advances the cloudification of academic SDIs and geoportals. Full article
Figures

Open AccessArticle
Conceptual Architecture and Service-Oriented Implementation of a Regional Geoportal for Rice Monitoring
ISPRS Int. J. Geo-Inf. 2017, 6(7), 191; doi:10.3390/ijgi6070191 -
Abstract
Agricultural monitoring has greatly benefited from the increased availability of a wide variety of remote-sensed satellite imagery, ground-sensed data (e.g., weather station networks) and crop models, delivering a wealth of actionable information to stakeholders to better streamline and improve agricultural practices. Nevertheless, as
[...] Read more.
Agricultural monitoring has greatly benefited from the increased availability of a wide variety of remote-sensed satellite imagery, ground-sensed data (e.g., weather station networks) and crop models, delivering a wealth of actionable information to stakeholders to better streamline and improve agricultural practices. Nevertheless, as the degree of sophistication of agriculture monitoring systems increases, significant challenges arise due to the handling and integration of multi-scale data sources to present information to decision-makers in a way which is useful, understandable and user friendly. To address these issues, in this article we present the conceptual architecture and service-oriented implementation of a regional geoportal, specifically focused on rice crop monitoring in order to perform unified monitoring with a supporting system at regional scale. It is capable of storing, processing, managing, serving and visualizing monitoring and generated data products with different granularity and originating from different data sources. Specifically, we focus on data sources and data flow, and their importance for and in relation to different stakeholders. In the context of an EU-funded research project, we present an implementation of the regional geoportal for rice monitoring, which is currently in use in Europe’s three largest rice-producing countries, Italy, Greece and Spain. Full article
Figures

Figure 1

Open AccessArticle
A Two-Step Method for Missing Spatio-Temporal Data Reconstruction
ISPRS Int. J. Geo-Inf. 2017, 6(7), 187; doi:10.3390/ijgi6070187 -
Abstract
Missing data reconstruction is a critical step in the analysis and mining of spatio-temporal data; however, few studies comprehensively consider missing data patterns, sample selection and spatio-temporal relationships. As a result, traditional methods often fail to obtain satisfactory accuracy or address high levels
[...] Read more.
Missing data reconstruction is a critical step in the analysis and mining of spatio-temporal data; however, few studies comprehensively consider missing data patterns, sample selection and spatio-temporal relationships. As a result, traditional methods often fail to obtain satisfactory accuracy or address high levels of complexity. To combat these problems, this study developed an effective two-step method for spatio-temporal missing data reconstruction (ST-2SMR). This approach includes a coarse-grained interpolation method for considering missing patterns, which can successfully eliminate the influence of continuous missing data on the overall results. Based on the results of coarse-grained interpolation, a dynamic sliding window selection algorithm was implemented to determine the most relevant sample data for fine-grained interpolation, considering both spatial and temporal heterogeneity. Finally, spatio-temporal interpolation results were integrated by using a neural network model. We validated our approach using Beijing air quality data and found that the proposed method outperforms existing solutions in term of estimation accuracy and reconstruction rate. Full article
Figures

Figure 1

Open AccessArticle
Prediction of Suspect Location Based on Spatiotemporal Semantics
ISPRS Int. J. Geo-Inf. 2017, 6(7), 185; doi:10.3390/ijgi6070185 -
Abstract
The prediction of suspect location enables proactive experiences for crime investigations and offers essential intelligence for crime prevention. However, existing studies have failed to capture the complex social location transition patterns of suspects and lack the capacity to address the issue of data
[...] Read more.
The prediction of suspect location enables proactive experiences for crime investigations and offers essential intelligence for crime prevention. However, existing studies have failed to capture the complex social location transition patterns of suspects and lack the capacity to address the issue of data sparsity. This paper proposes a novel location prediction model called CMoB (Crime Multi-order Bayes model) based on the spatiotemporal semantics to enhance the prediction performance. In particular, the model groups suspects with similar spatiotemporal semantics as one target suspect. Then, their mobility data are applied to estimate Markov transition probabilities of unobserved locations based on a KDE (kernel density estimating) smoothing method. Finally, by integrating the total transition probabilities, which are derived from the multi-order property of the Markov transition matrix, into a Bayesian-based formula, it is able to realize multi-step location prediction for the individual suspect. Experiments with the mobility dataset covering 210 suspects and their 18,754 location records from January to June 2012 in Wuhan City show that the proposed CMoB model significantly outperforms state-of-the-art algorithms for suspect location prediction in the context of data sparsity. Full article
Figures

Figure 1

Open AccessArticle
Addressing Public Law Restrictions within a 3D Cadastral Context
ISPRS Int. J. Geo-Inf. 2017, 6(7), 182; doi:10.3390/ijgi6070182 -
Abstract
Public law affects contemporary life by imposing various regulations that apply in 3D space. However, such restrictions are either literally described in legal documents or presented on a horizontal plane, resulting in ambiguities, especially in the case of vertically overlapping restrictions with a
[...] Read more.
Public law affects contemporary life by imposing various regulations that apply in 3D space. However, such restrictions are either literally described in legal documents or presented on a horizontal plane, resulting in ambiguities, especially in the case of vertically overlapping restrictions with a significant impact on land management. This paper investigates public law restrictions (PLR) applying to 3D space and their management within a 3D cadastral context. Within this framework, a case study is examined in Greece concerning the establishment of a subway station, focusing on public utilities, archaeological legislation, and building regulations. Relative legal documentation is compiled and mapped in a 3D PLR model, presenting inefficiencies and malfunctions that can be resolved if PLRs are addressed within a 3D cadastral context. Stipulations implying restrictions in 3D space within current legislation are presented, along with the restrictions deriving from the absolute character of ownership right, thus highlighting the significance of 3D definition, modeling and recording of PLRs. Full article
Figures

Figure 1

Open AccessArticle
An Urban Heat Island Study of the Colombo Metropolitan Area, Sri Lanka, Based on Landsat Data (1997–2017)
ISPRS Int. J. Geo-Inf. 2017, 6(7), 189; doi:10.3390/ijgi6070189 -
Abstract
One of the major impacts associated with unplanned rapid urban growth is the decrease of urban vegetation, which is often replaced with impervious surfaces such as buildings, parking lots, roads, and pavements. Consequently, as the percentage of impervious surfaces continues to increase at
[...] Read more.
One of the major impacts associated with unplanned rapid urban growth is the decrease of urban vegetation, which is often replaced with impervious surfaces such as buildings, parking lots, roads, and pavements. Consequently, as the percentage of impervious surfaces continues to increase at the expense of vegetation cover, surface urban heat island (SUHI) forms and becomes more intense. The Colombo Metropolitan Area (CMA), Sri Lanka, is one of the rapidly urbanizing metropolitan regions in South Asia. In this study, we examined the spatiotemporal variations of land surface temperature (LST) in the CMA in the context of the SUHI phenomenon using Landsat data. More specifically, we examined the relationship of LST with the normalized difference vegetation index (NDVI) and the normalized difference built-up index (NDBI) at three time points (1997, 2007 and 2017). In addition, we also identified environmentally critical areas based on LST and NDVI. We found significant correlations of LST with NDVI (negative) and NDBI (positive) (p < 0.001) across all three time points. Most of the environmentally critical areas are located in the central business district (CBD), near the harbor, across the coastal belt, and along the main transportation network. We recommend that those identified environmentally critical areas be considered in the future urban planning and landscape development of the city. Green spaces can help improve the environmental sustainability of the CMA. Full article
Figures

Figure 1

Open AccessArticle
Unveiling E-Bike Potential for Commuting Trips from GPS Traces
ISPRS Int. J. Geo-Inf. 2017, 6(7), 190; doi:10.3390/ijgi6070190 -
Abstract
Common goals of sustainable mobility approaches are to reduce the need for travel, to facilitate modal shifts, to decrease trip distances and to improve energy efficiency in the transportation systems. Among these issues, modal shift plays an important role for the adoption of
[...] Read more.
Common goals of sustainable mobility approaches are to reduce the need for travel, to facilitate modal shifts, to decrease trip distances and to improve energy efficiency in the transportation systems. Among these issues, modal shift plays an important role for the adoption of vehicles with fewer or zero emissions. Nowadays, the electric bike (e-bike) is becoming a valid alternative to cars in urban areas. However, to promote modal shift, a better understanding of the mobility behaviour of e-bike users is required. In this paper, we investigate the mobility habits of e-bikers using GPS data collected in Belgium from 2014 to 2015. By analysing more than 10,000 trips, we provide insights about e-bike trip features such as: distance, duration and speed. In addition, we offer a deep look into which routes are preferred by bike owners in terms of their physical characteristics and how weather influences e-bike usage. Results show that trips with higher travel distances are performed during working days and are correlated with higher average speeds. Usage patterns extracted from our data set also indicate that e-bikes are preferred for commuting (home-work) and business (work related) trips rather than for recreational trips. Full article
Figures

Figure 1

Open AccessArticle
Participatory Land Administration on Customary Lands: A Practical VGI Experiment in Nanton, Ghana
ISPRS Int. J. Geo-Inf. 2017, 6(7), 186; doi:10.3390/ijgi6070186 -
Abstract
Land information is one of the basic requirements for land management activities such as land consolidation. However, the dearth of land information on customary lands limits the development and application of land consolidation. This paper presents and discusses the results of an experiment
[...] Read more.
Land information is one of the basic requirements for land management activities such as land consolidation. However, the dearth of land information on customary lands limits the development and application of land consolidation. This paper presents and discusses the results of an experiment carried out to test the potential of participatory land administration applied on customary lands in support of land consolidation. A brief overview of the evolution of crowdsourced, voluntary, and participatory approaches is provided alongside newly related insights into neogeography and neo-cadastre, and fit-for-purpose and pro-poor land administration. The concept of participatory land administration is then developed in this context. The area of the experiment is in Northern Ghana where the process was developed together with the local farming community. The study involved collecting land information relating to farms over a two-week period, using a mobile app and a satellite image, based on participatory land administration. The results show that Participatory Land Administration can potentially support land consolidation, though further investigation is needed on how it can be integrated into the formal land registration system, into an actual land consolidation project. Full article
Figures

Figure 1

Open AccessArticle
Fusion of Multi-Temporal Interferometric Coherence and Optical Image Data for the 2016 Kumamoto Earthquake Damage Assessment
ISPRS Int. J. Geo-Inf. 2017, 6(7), 188; doi:10.3390/ijgi6070188 -
Abstract
Earthquakes are one of the most devastating types of natural disasters, and happen with little to no warning. This study combined Landsat-8 and interferometric ALOS-2 coherence data without training area techniques by classifying the remote sensing ratios of specific features for damage assessment.
[...] Read more.
Earthquakes are one of the most devastating types of natural disasters, and happen with little to no warning. This study combined Landsat-8 and interferometric ALOS-2 coherence data without training area techniques by classifying the remote sensing ratios of specific features for damage assessment. Waterbodies and highly vegetated areas were extracted by the modified normalized difference water index (MNDWI) and normalized difference vegetation index (NDVI), respectively, from after-earthquake images in order to improve the accuracy of damage maps. Urban areas were classified from pre-event interferometric coherence data. The affected areas from the earthquake were detected with the normalized difference (ND) between the pre- and co-event interferometric coherence. The results presented three damage types; namely, damage to buildings caused by ground motion, liquefaction, and landslides. The overall accuracy (94%) of the confusion matrix was excellent. Results for urban areas were divided into three damage levels (e.g., none–slight, slight–heavy, heavy–destructive) at a high (90%) overall accuracy level. Moreover, data on buildings damaged by liquefaction and landslides were in good agreement with field survey information. Overall, this study illustrates an effective damage assessment mapping approach that can support post-earthquake management activities for future events, especially in areas where geographical data are sparse. Full article
Figures

Open AccessArticle
Retrieval and Comparison of Forest Leaf Area Index Based on Remote Sensing Data from AVNIR-2, Landsat-5 TM, MODIS, and PALSAR Sensors
ISPRS Int. J. Geo-Inf. 2017, 6(6), 179; doi:10.3390/ijgi6060179 -
Abstract
Remote sensing data from multi-source optical and SAR (Synthetic Aperture Radar) sensors have been widely utilized to detect forest dynamics under a variety of conditions. Due to different temporal coverage, spatial resolution, and spectral characteristics, these sensors usually perform differently from one another.
[...] Read more.
Remote sensing data from multi-source optical and SAR (Synthetic Aperture Radar) sensors have been widely utilized to detect forest dynamics under a variety of conditions. Due to different temporal coverage, spatial resolution, and spectral characteristics, these sensors usually perform differently from one another. To conduct statistical modeling accuracies evaluation and comparison among several sensors, a linear statistical model was applied in this study for retrieval and comparative analysis based on remote-sensing indices from optical sensors of ALOS AVNIR-2 (Advanced Land Observing Satellite Advanced Visible and Near Infrared Radiometer type 2), Landsat-5 TM (Thematic Mapper), MODIS NBAR (Moderate Resolution Imaging Spectroradiometer Nadir BRDF-Adjusted Reflectance), and the SAR sensor of ALOS PALSAR (Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar), respectively. This modeling used the forest leaf area index (LAI) as the field measured variable. During modeling, six optical vegetation indices were selected for evaluation and comparison between the three optical sensors, while simultaneously, two radar indices were calculated for the comparison between ALOS AVNIR-2 and PALSAR sensors. The gap between the spatial resolution of remote-sensing data and field plot size can account for the different accuracies found in this study. This study provides a reference for the selection of remote-sensing data types and spatial resolution in specific forest monitoring applications with different data acquisition costs and accuracy needs. Normally, at regional and national scales, remote sensing data with 30 m spatial resolution (e.g., Landsat) could provide significant results in the statistical modelling and retrieval of LAI while the MODIS cannot always meet the requirements. Full article
Figures

Figure 1

Open AccessArticle
Experimental Evaluation of the Usability of Cartogram for Representation of GlobeLand30 Data
ISPRS Int. J. Geo-Inf. 2017, 6(6), 180; doi:10.3390/ijgi6060180 -
Abstract
GlobeLand30 is the world’s first global land cover dataset at 30 m resolution for two epochs, i.e., 2000 and 2010. On the official website, the data are represented by qualitative thematic maps which show the distribution of global land cover, and some proportional
[...] Read more.
GlobeLand30 is the world’s first global land cover dataset at 30 m resolution for two epochs, i.e., 2000 and 2010. On the official website, the data are represented by qualitative thematic maps which show the distribution of global land cover, and some proportional symbol maps which are quantitative representations of land cover data. However, researchers have also argued that the cartogram, a kind of value-by-area representation, has some advantages over these maps in some cases, while others doubt their usability because of the possible distortion in shape. This led us to conduct an experimental evaluation of the usability of the cartogram for the representation of GlobeLand30. This experimental evaluation is a comparative analysis between the cartogram and the proportional symbol map to examine which is more effective in various kinds of quantitative analyses. The results show that the thematic map is better than the cartogram for the representation of quantity (e.g., area size), but the cartogram performs better in the representation of tendency distribution and areas’ multiple relationships. The usability of the cartogram is notably affected by map projection and the irregularity in area shapes, but the equal-area projection does not necessarily perform better than equidistance projection, especially at high latitudes. Full article
Figures

Figure 1

Open AccessArticle
“Turn Left after the WC, and Use the Lift to Go to the 2nd Floor”—Generation of Landmark-Based Route Instructions for Indoor Navigation
ISPRS Int. J. Geo-Inf. 2017, 6(6), 183; doi:10.3390/ijgi6060183 -
Abstract
People in unfamiliar environments often need navigation guidance to reach a destination. Research has found that compared to outdoors, people tend to lose orientation much more easily within complex buildings, such as university buildings and hospitals. This paper proposes a category-based method to
[...] Read more.
People in unfamiliar environments often need navigation guidance to reach a destination. Research has found that compared to outdoors, people tend to lose orientation much more easily within complex buildings, such as university buildings and hospitals. This paper proposes a category-based method to generate landmark-based route instructions to support people’s wayfinding activities in unfamiliar indoor environments. Compared to other methods relying on detailed instance-level data about the visual, semantic, and structural characteristics of individual spatial objects, the proposed method relies on commonly available data about categories of spatial objects, which exist in most indoor spatial databases. With this, instructions like “Turn right after the second door, and use the elevator to go to the second floor” can be generated for indoor navigation. A case study with a university campus shows that the method is feasible in generating landmark-based route instructions for indoor navigation. More importantly, compared to metric-based instructions (i.e., the benchmark for indoor navigation), the generated landmark-based instructions can help users to unambiguously identify the correct decision point where a change of direction is needed, as well as offer information for the users to confirm that they are on the right way to the destination. Full article
Figures

Figure 1

Open AccessArticle
Towards a Planetary Spatial Data Infrastructure
ISPRS Int. J. Geo-Inf. 2017, 6(6), 181; doi:10.3390/ijgi6060181 -
Abstract
Planetary science is the study of planets, moons, irregular bodies such as asteroids and the processes that create and modify them. Like terrestrial sciences, planetary science research is heavily dependent on collecting, processing and archiving large quantities of spatial data to support a
[...] Read more.
Planetary science is the study of planets, moons, irregular bodies such as asteroids and the processes that create and modify them. Like terrestrial sciences, planetary science research is heavily dependent on collecting, processing and archiving large quantities of spatial data to support a range of activities. To address the complexity of storing, discovering, accessing, and utilizing spatial data, the terrestrial research community has developed conceptual Spatial Data Infrastructure (SDI) models and cyberinfrastructures. The needs that these systems seek to address for terrestrial spatial data users are similar to the needs of the planetary science community: spatial data should just work for the non-spatial expert. Here we discuss a path towards a Planetary Spatial Data Infrastructure (PSDI) solution that fulfills this primary need. We first explore the linkage between SDI models and cyberinfrastructures, then describe the gaps in current PSDI concepts, and discuss the overlap between terrestrial SDIs and a new, conceptual PSDI that best serves the needs of the planetary science community. Full article
Figures

Figure 1

Open AccessArticle
Quality Assessment Method for Linear Feature Simplification Based on Multi-Scale Spatial Uncertainty
ISPRS Int. J. Geo-Inf. 2017, 6(6), 184; doi:10.3390/ijgi6060184 -
Abstract
This study discusses a method for quantitative quality assessment for the simplification of linear features. Considering the multi-scale nature of linear features, this paper combines the improved Douglas–Peucker method without threshold and the multiway tree model to construct a weighted hierarchical linear feature
[...] Read more.
This study discusses a method for quantitative quality assessment for the simplification of linear features. Considering the multi-scale nature of linear features, this paper combines the improved Douglas–Peucker method without threshold and the multiway tree model to construct a weighted hierarchical linear feature representation model called the Douglas–Peucker Multiway Tree (DMC-tree). Subsequently, the uncertainty computation is conducted from the root of the DMC-Tree top-down level by level to obtain the quality indexes. Then, the quality index of the whole linear feature is obtained by combining the indexes of every layer together with their weights. The results of the presented method are compared with those of the length ratio method and the Hausdorff distance method. The results show the advantages of the presented method over the others, including (1) its sensitivity to feature points of multiple scales, (2) the quantitative characteristics of the indexes, and (3) the finer granularity in assessment. Full article
Figures

Figure 1

Open AccessArticle
Adaptive Surface Modeling of Soil Properties in Complex Landforms
ISPRS Int. J. Geo-Inf. 2017, 6(6), 178; doi:10.3390/ijgi6060178 -
Abstract
Abstract:Spatial discontinuity often causes poor accuracy when a single model is used for the surface modeling of soil properties in complex geomorphic areas. Here we present a method for adaptive surface modeling of combined secondary variables to improve prediction accuracy during
[...] Read more.
Abstract:Spatial discontinuity often causes poor accuracy when a single model is used for the surface modeling of soil properties in complex geomorphic areas. Here we present a method for adaptive surface modeling of combined secondary variables to improve prediction accuracy during the interpolation of soil properties (ASM-SP). Using various secondary variables and multiple base interpolation models, ASM-SP was used to interpolate soil K+ in a typical complex geomorphic area (Qinghai Lake Basin, China). Five methods, including inverse distance weighting (IDW), ordinary kriging (OK), and OK combined with different secondary variables (e.g., OK-Landuse, OK-Geology, and OK-Soil), were used to validate the proposed method. The mean error (ME), mean absolute error (MAE), root mean square error (RMSE), mean relative error (MRE), and accuracy (AC) were used as evaluation indicators. Results showed that: (1) The OK interpolation result is spatially smooth and has a weak bull's-eye effect, and the IDW has a stronger ‘bull’s-eye’ effect, relatively. They both have obvious deficiencies in depicting spatial variability of soil K+. (2) The methods incorporating combinations of different secondary variables (e.g., ASM-SP, OK-Landuse, OK-Geology, and OK-Soil) were associated with lower estimation bias. Compared with IDW, OK, OK-Landuse, OK-Geology, and OK-Soil, the accuracy of ASM-SP increased by 13.63%, 10.85%, 9.98%, 8.32%, and 7.66%, respectively. Furthermore, ASM-SP was more stable, with lower MEs, MAEs, RMSEs, and MREs. (3) ASM-SP presents more details than others in the abrupt boundary, which can render the result consistent with the true secondary variables. In conclusion, ASM-SP can not only consider the nonlinear relationship between secondary variables and soil properties, but can also adaptively combine the advantages of multiple models, which contributes to making the spatial interpolation of soil K+ more reasonable. Full article
Figures

Figure 1

Open AccessArticle
Multi-Feature Joint Sparse Model for the Classification of Mangrove Remote Sensing Images
ISPRS Int. J. Geo-Inf. 2017, 6(6), 177; doi:10.3390/ijgi6060177 -
Abstract
Mangroves are valuable contributors to coastal ecosystems, and remote sensing is an indispensable way to obtain knowledge of the dynamics of mangrove ecosystems. Due to the similar spectral features between mangroves and other land cover types, challenges are posed since the accuracy is
[...] Read more.
Mangroves are valuable contributors to coastal ecosystems, and remote sensing is an indispensable way to obtain knowledge of the dynamics of mangrove ecosystems. Due to the similar spectral features between mangroves and other land cover types, challenges are posed since the accuracy is sometimes unsatisfactory in distinguishing mangroves from other land cover types with traditional classification methods. In this paper, we propose a classification method named the multi-feature joint sparse algorithm (MF-SRU), in which spectral, topographic, and textural features are integrated as the decision-making features, and sparse representation of both center pixels and their eight neighborhood pixels is proposed to represent the spatial correlation of neighboring pixels, which can make good use of the spatial correlation of adjacent pixels. Experiments are performed on Landsat Thematic Mapper multispectral remote sensing imagery in the Zhangjiang estuary in Southeastern China, and the results show that the proposed method can effectively improve the extraction accuracy of mangroves. Full article
Figures

Figure 1

Open AccessArticle
Generalized Aggregation of Sparse Coded Multi-Spectra for Satellite Scene Classification
ISPRS Int. J. Geo-Inf. 2017, 6(6), 175; doi:10.3390/ijgi6060175 -
Abstract
Satellite scene classification is challenging because of the high variability inherent in satellite data. Although rapid progress in remote sensing techniques has been witnessed in recent years, the resolution of the available satellite images remains limited compared with the general images acquired using
[...] Read more.
Satellite scene classification is challenging because of the high variability inherent in satellite data. Although rapid progress in remote sensing techniques has been witnessed in recent years, the resolution of the available satellite images remains limited compared with the general images acquired using a common camera. On the other hand, a satellite image usually has a greater number of spectral bands than a general image, thereby permitting the multi-spectral analysis of different land materials and promoting low-resolution satellite scene recognition. This study advocates multi-spectral analysis and explores the middle-level statistics of spectral information for satellite scene representation instead of using spatial analysis. This approach is widely utilized in general image and natural scene classification and achieved promising recognition performance for different applications. The proposed multi-spectral analysis firstly learns the multi-spectral prototypes (codebook) for representing any pixel-wise spectral data, and then, based on the learned codebook, a sparse coded spectral vector can be obtained with machine learning techniques. Furthermore, in order to combine the set of coded spectral vectors in a satellite scene image, we propose a hybrid aggregation (pooling) approach, instead of conventional averaging and max pooling, which includes the benefits of the two existing methods, but avoids extremely noisy coded values. Experiments on three satellite datasets validated that the performance of our proposed approach is very impressive compared with the state-of-the-art methods for satellite scene classification. Full article
Figures

Figure 1

Open AccessArticle
Development and Comparison of Species Distribution Models for Forest Inventories
ISPRS Int. J. Geo-Inf. 2017, 6(6), 176; doi:10.3390/ijgi6060176 -
Abstract
A comparison of several statistical techniques common in species distribution modeling was developed during this study to evaluate and obtain the statistical model most accurate to predict the distribution of different forest tree species (in our case presence/absence data) according environmental variables. During
[...] Read more.
A comparison of several statistical techniques common in species distribution modeling was developed during this study to evaluate and obtain the statistical model most accurate to predict the distribution of different forest tree species (in our case presence/absence data) according environmental variables. During the process we have developed maximum entropy (MaxEnt), classification and regression trees (CART), multivariate adaptive regression splines (MARS), showing the statistical basis of each model and, at the same time, we have developed a specific additive model to compare and validate their capability. To compare different results, the area under the receiver operating characteristic (ROC) function (AUC) was used. Every AUC value obtained with those models is significant and all of the models could be useful to represent the distribution of each species. Moreover, the additive model with thin plate splines gave the best results. The worst capability was obtained with MARS. This model’s performance was below average for several species. The additive model developed obtained better results because it allowed for changes and calibrations. In this case we were aware of all of the processes that occurred during the modeling. By contrast, models obtained using specific software, in general, perform like “hermetic machines”, because it could sometimes be impossible to understand the stages that led to the final results. Full article
Figures

Figure 1