Quantitative Spatial Upscaling of Categorical Data in the Context of Landscape Ecology: A New Scaling Algorithm

Spatially explicit ecological models rely on spatially exhaustive data layers that have scales appropriate to the ecological processes of interest. Such data layers are often categorical raster maps derived from high-resolution, remotely sensed data that must be scaled to a lower spatial resolution to make them compatible with the scale of ecological analysis. Statistical functions commonly used to aggregate categorical data are majority-, nearest-neighbor- and random-rule. For heterogeneous landscapes and large scaling factors, however, use of these functions results in two critical issues: (1) ignoring large portions of information present in the high-resolution grid cells leads to high and uncontrolled loss of information in the scaled dataset; and (2) maintaining classes from the high-resolution dataset at the lower spatial resolution assumes validity of the classification scheme at the low-resolution scale, failing to represent recurring mixes of heterogeneous classes present in the low-resolution grid cells. The proposed new scaling algorithm resolves these issues, aggregating categorical data while simultaneously controlling for information loss by generating a non-hierarchical, representative, classification system valid at the aggregated scale. Implementing scaling parameters, that control class-label precision effectively reduced information loss of scaled landscapes as class-label precision increased. In a neutral-landscape simulation study, the algorithm consistently preserved information at a significantly higher level than the other commonly used algorithms. When applied to maps of real landscapes, the same increase in information retention was observed, and the scaled classes were detectable from lower-resolution, remotely sensed, multi-spectral reflectance data with high accuracy. The framework developed in this research facilitates scaling-parameter selection to address trade-offs among information retention, label fidelity, and spectral detectability of scaled classes. When generating high spatial resolution land-cover maps, quantifying effects of sampling intensity, feature-space dimensionality and classifier method on overall accuracy, confidence estimates, and classifier efficiency allowed optimization of the mapping method. Increase in sampling intensity boosted accuracies in a reasonably predictable fashion. However, adding a second image acquired when ground conditions and vegetation phenology differed from those of the first image had a much greater impact, increasing classification accuracy even at low sampling intensities, to levels not reached with a single season image

Spatial ecological complexity measures in GRASS GIS

Good estimates of ecosystem complexity are essential for a number of ecological tasks: from biodiversity estimation, to forest structure variable retrieval, to feature extraction by edge detection and generation of multifractal surface as neutral models for e.g. feature change assessment. Hence, measuring ecological complexity over space becomes crucial in macroecology and geography. Many geospatial tools have been advocated in spatial ecology to estimate ecosystem complexity and its changes over space and time. Among these tools, free and open source options especially offer opportunities to guarantee the robustness of algorithms and reproducibility. In this paper we will summarize the most straightforward measures of spatial complexity available in the Free and Open Source Software GRASS GIS, relating them to key ecological patterns and processes

Methods and workflow for spatial conservation prioritization using Zonation

Spatial conservation prioritization concerns the effective allocation of conservation action. Its stages include development of an ecologically based model of conservation value, data pre-processing, spatial prioritization analysis, and interpretation of results for conservation action. Here we investigate the details of each stage for analyses done using the Zonation prioritization framework. While there is much literature about analytical methods implemented in Zonation, there is only scattered information available about what happens before and after the computational analysis. Here we fill this information gap by summarizing the pre-analysis and post-analysis stages of the Zonation framework. Concerning the entire process, we summarize the full workflow and list examples of operational best-case, worst- case, and typical scenarios for each analysis stage. We discuss resources needed in different analysis stages. We also discuss benefits, disadvantages, and risks involved in the application of spatial prioriti- zation from the perspective of different stakeholders. Concerning pre-analysis stages, we explain the development of the ecological model and discuss the setting of priority weights and connectivity re- sponses. We also explain practical aspects of data pre-processing and the post-processing interpretation of results for different conservation objectives. This work facilitates well-informed design and application of Zonation analyses for the purpose of spatial conservation planning. It should be useful for both sci- entists working on conservation related research as well as for practitioners looking for useful tools for conservation resource allocationPeer reviewe

Simulating Land Use Land Cover Change Using Data Mining and Machine Learning Algorithms

The objectives of this dissertation are to: (1) review the breadth and depth of land use land cover (LUCC) issues that are being addressed by the land change science community by discussing how an existing model, Purdue\u27s Land Transformation Model (LTM), has been used to better understand these very important issues; (2) summarize the current state-of-the-art in LUCC modeling in an attempt to provide a context for the advances in LUCC modeling presented here; (3) use a variety of statistical, data mining and machine learning algorithms to model single LUCC transitions in diverse regions of the world (e.g. United States and Africa) in order to determine which tools are most effective in modeling common LUCC patterns that are nonlinear; (4) develop new techniques for modeling multiple class (MC) transitions at the same time using existing LUCC models as these models are rare and in great demand; (5) reconfigure the existing LTM for urban growth boundary (UGB) simulation because UGB modeling has been ignored by the LUCC modeling community, and (6) compare two rule based models for urban growth boundary simulation for use in UGB land use planning. The review of LTM applications during the last decade indicates that a model like the LTM has addressed a majority of land change science issues although it has not explicitly been used to study terrestrial biodiversity issues. The review of the existing LUCC models indicates that there is no unique typology to differentiate between LUCC model structures and no models exist for UGB. Simulations designed to compare multiple models show that ANN-based LTM results are similar to Multivariate Adaptive Regression Spline (MARS)-based models and both ANN and MARS-based models outperform Classification and Regression Tree (CART)-based models for modeling single LULC transition; however, for modeling MC, an ANN-based LTM-MC is similar in goodness of fit to CART and both models outperform MARS in different regions of the world. In simulations across three regions (two in United States and one in Africa), the LTM had better goodness of fit measures while the outcome of CART and MARS were more interpretable and understandable than the ANN-based LTM. Modeling MC LUCC require the examination of several class separation rules and is thus more complicated than single LULC transition modeling; more research is clearly needed in this area. One of the greatest challenges identified with MC modeling is evaluating error distributions and map accuracies for multiple classes. A modified ANN-based LTM and a simple rule based UGBM outperformed a null model in all cardinal directions. For UGBM model to be useful for planning, other factors need to be considered including a separate routine that would determine urban quantity over time

Data infrastructures and spatial models for biodiversity assessment and analysis: applications to vertebrate communities.

In conservation biology the computation of biodiversity maps, based on statistical models is a central concern. These maps, produced with objective and repeatable methods are an essential tool for conservation and monitoring programs as well as for landuse planning. Since the computation of biodiversity maps requires complex and time consuming procedures for data processing and analysis, it is necessary to design methods for homogeneous, scalable and repeatable data management and analysis. Moreover, the huge volume of data used in ecological modelling requires suitable software architectures to store, analyze, retrieve and distribute information in order to support research and management actions in due time. First of all we developed an analysis system (SOS - Species Open Spreader) providing statistical and mathematical models to predict species distribution in relation to a set of predictive environmental and geographical variables The system is composed of a module for data input/output toward and from the GIS and of a package of scripts for the application of different modelling techniques. At present, three statistical techniques are integrated in SOS: Logistic Regression Analysis (LRA), Environmental Niche Factor Analysis (ENFA) and flexible Discriminant Analysis with method BRUTO. Furthermore, two empirical spatial methods of analysis are available within SOS: Habitat Suitability Index (HSI) and Spatial Overlay. The system is designed to work with the GIS (Geographical Information System) soft-ware GRASS and the statistical environment R, coupled together through the SPGRASS6 library. Three different outputs are expected: text and graphical outputs with statistical results and suitability maps. Second, we tested the use of spatial Database Management Systems (Spatial DBMS) to handle wildlife and socio-economic data and we developed a web database application to provide facilities for database access. The information system was built for the Meru district (Tanzania) in the context of an Italian cooperation project of land use planning in Maasai rural areas. We tested two di_erent solutions: SpatiaLite and PostgreSQL-PostGIS; they both offer advanced technical facilities and spatial extensions to analyze spatial data. SpatiaLite is a new solution and offers the main advantages to consist of a unique file and to present a user-friendly interface, which make it the best solution for many applications. in spite of this we used PostgreSQL-PostGIS since it represents a well-established information system supported by libraries for web applications development. We applied SOS to three case studies at different spatial scale: Brescia plain (small scale), Mount Meru region - Tanzania (medium scale) and Lombardy region (big scale) in order to produce maps of species potential distribution and biodiversity maps for planning and management. We applied logistic regression analyses to compute models and ROC analysis for classification performance evaluation. The automation of processes through SOS gave us the possibility to build models for a large number of vertebrate species. The analysis produced very reliable results at middle and big scale while regression methods did not converge at small scale. This is probably due to habitat homogeneity and to the use of environmental variables with an insufficient level of detail. The potential distribution and biodiversity maps produced also had in all cases an applicative use in fact we used mammal species models computed for Mt. Meru region to produce a map of biodiversity within the area: this map represents an informative base for land use planning at village level within a cooperation project for Maasai economic development and environmental redemption. Amphibians and reptiles models, computed for Lombardy, represent a good informative base for planning management actions in the region