Methods to summarize change among land categories across time intervals

Time-series maps have become more detailed in terms of numbers of categories and time points. Our paper proposes methods for raster datasets where detailed analysis of all categorical transitions would be initially overwhelming. We create two measurements: Incidents and States. The former is the number of times a pixel’s category changes across time intervals; the latter is the number of categories that a pixel represents across time points. The combinations of Incidents and States summarize change trajectories. We also describe categorical transitions in terms of annual flow matrices, which quantify the additional information generated by intermediate time points within the temporal extent. Our approach summarizes change at the pixel and landscape levels in ways that communicate where and how categories transition over time. These methods are useful to detect hotspots of change and to consider whether the apparent changes are real or due to map error.     

Are existing vegetation maps adequate to predict bird distributions?

Bird species are selective on the vegetation types in which they are found but predictive models of bird distribution based on variables derived from land-use/land-cover maps tend to have limited success. It has been suggested that accuracy of existing maps used to derive predictors is in part responsible for the limited success of bird distribution models. In two areas of 4900 km² of Western Andalusia, Spain, we compared the predictive ability of bird distribution models derived from two existing general-purpose land-use/land-cover maps, which differ in their resolution and accuracy: a coarse scale vegetation map of Europe, the CORINE land-cover map, and a detailed regional map, the 1995 land-use/land-cover map of Andalusia from the SINAMBA (Consejerı́a de Medio Ambiente, Junta de Andalucı́a). We compared the bird distribution models derived from these general-purpose vegetation maps with models derived from two more accurate structural vegetation maps built considering directly variables that influence bird habitat selection, one built from satellite images for this study and another obtained by improving the resolution and accuracy of the SINAMBA map with satellite data. We sampled the presence/absence of bird species at 857 points using 15-min point surveys. Predictive models for 54 bird species were built with generalised additive models (GAMs), using as potential predictors the same set of landscape and vegetation structure variables measured on each map. We compared for each bird species the predictive accuracy of the best model derived from each map. Vegetation structure measured at bird sample points was used as ground-truth for comparing the accuracy of vegetation maps. Although maps differed in their resolution and accuracy, the results show that all of them produced similarly accurate bird distribution models, with a mixed map produced with both thematic and satellite information being the best. The models derived from the more accurate vegetation structure maps obtained from satellite data were not more accurate than those derived directly from the SINAMBA or CORINE maps. Our results suggest that some general-purpose land-use/land-cover maps are accurate enough to derive bird distribution models. There is a certain limit to improve vegetation maps above which there is no effect in their power to predict bird distribution.     

A geostatistical approach for mapping thematic classification accuracy and evaluating the impact of inaccurate spatial data on ecological model predictions

Spatial information in the form of geographical information system coverages and remotely sensed imagery is increasingly used in ecological modeling. Examples include maps of land cover type from which ecologically relevant properties, such as biomass or leaf area index, are derived. Spatial information, however, is not error-free: acquisition and processing errors, as well as the complexity of the physical processes involved, make remotely sensed data imperfect measurements of ecological attributes. It is therefore important to first assess the accuracy of the spatial information being used and then evaluate the impact of such inaccurate information on ecological model predictions. In this paper, the role of geostatistics for mapping thematic classification accuracy through integration of abundant image-derived (soft) and sparse higher accuracy (hard) class labels is presented. Such assessment leads to local indices of map quality, which can be used for guiding additional ground surveys. Stochastic simulation is proposed for generating multiple alternative realizations (maps) of the spatial distribution of the higher accuracy class labels over the study area. All simulated realizations are consistent with the available pieces of information (hard and soft labels) up to their validated level of accuracy. The simulated alternative class label representations can be used for assessing joint spatial accuracy, i.e., classification accuracy regarding entire spatial features read from the thematic map. Such realizations can also serve as input parameters to spatially explicit ecological models; the resulting distribution of ecological responses provides a model of uncertainty regarding the ecological model prediction. A case study illustrates the generation of alternative land cover maps for a Landsat Thematic Mapper (TM) subscene, and the subsequent construction of local map quality indices. Simulated land cover maps are then input into a biogeochemical model for assessing uncertainty regarding net primary production (NPP).     

土壤可蚀性（K）值图编制方法的初步研究

利用土壤普查图件和剖面理化分析的成果资料编制了土壤可蚀性（Ｋ）值图，介绍了求取剖面点Ｋ值的查图表法、图斑分并与图斑Ｋ值的确定原则和编制程序等，应用上述方法，完成了我国第一张地区级Ｋ值图，经分析，与公式算法比，查图表法的Ｋ值精度为８５．４％，与径流小区实测值比，用Ｋ值图上相应Ｋ值监测的土壤年流失量的精度为８６．０％。     

Study on the utility of IRS 1D LISS-III data and the classification techniques for mapping of Sunderban mangroves

Mangrove conservation and management is a stupendous task chiefly due to the inaccessibility and the hostile substrate conditions. Remote sensing technology serves as an important tool in providing fast, accurate and up-to-date baseline information on the status of mangroves. It is almost impossible to carry out conventional field surveys in these swampy areas. The present study aims at the classification and mapping of the mangroves in Sunderban Biosphere Reserve (SBR) in the West Bengal province of India using IRS 1D LISS-III satellite data. Different classification approaches, viz., on-screen visual interpretation, supervised and unsupervised classifications were tried. The study showed that four mangroves classes, viz., Avicennia, Phoenix, mixed mangroves, and mangrove scrub and eight non-mangrove classes could be delineated using all the three approaches. All the mangrove and non-mangrove classes were field verified and the overall accuracy as well as user’s and producer’s accuracies for each category were determined. It was observed that among the three approaches, on-screen visual interpretation yielded higher classification accuracy (91.67%) compared to supervised (79.90%) and unsupervised classifications (71.08%). The results obtained through on-screen visual interpretation showed that all mangrove categories together cover 23.21% of the total geographical area of SBR, of which the mixed mangrove category covers maximum area (18.31%). Among the non-mangrove classes, the waterbody occupies largest area (35.36%) followed by agriculture (34.51%).     

Bandwidth determination for kernel density analysis of wildfire events at forest sub-district scale

Forest fire is regarded as one of the most significant factors leading to land degradation. While evaluating fire hazard or producing fire risk zone maps, quantitative analyses using historic fire data is often required, and during all these modeling and multi-criteria analysis processes, the fire event itself is taken as the dependent variable. However, there are two main problematic issues in analyzing historic fire data. The first difficulty arises from the fact that it is in point format, whereas a continuous surface is frequently needed for statistically analyzing the relationship of fire events with other factors, such as anthropogenic, topographic and climatic conditions. Another, and probably the most bothersome challenge is to overcome inaccuracy inherent in historic fire data in point format, since the exact coordinates of ignition points are mostly unknown. In this study, kernel density mapping, a widely used method for converting discrete point data into a continuous raster surface, was used to map the historic fire data in Mumcular Forest Sub-district in Mu?la, Turkey. The historic fire data was transferred onto the digital forest stand map of the study area, where the exact locations of ignition points are unknown; however, the exact number of ignition points in each compartment of the forest stand map is known. Different random distributions of ignition points were produced, and for each random distribution, kernel density maps were produced by applying two distinct kernel functions with several smoothing parameter options. The obtained maps were compared through correlation analysis in order to illustrate the effect of randomness, choice of kernel function and smoothing parameter. The proposed method gives a range of values rather than a single bandwidth value; however, it provides a more reliable way than comparing the maps with different bandwidths subjectively by eye.     

Toward estimation of map accuracy without a probability test sample

The time and effort required of probability sampling for accuracy assessment of large-scale land cover maps often means that probability test samples are not collected. Yet, map usefulness is substantially reduced without reliable accuracy estimates. In this article, we introduce a method of estimating the accuracy of a classified map that does not utilize a test sample in the usual sense, but instead estimates the probability of correct classification for each map unit using only the classification rule and the map unit covariates. We argue that the method is an improvement over conventional estimators, though it does not eliminate the need for probability sampling. The method also provides a new and simple method of constructing accuracy maps. We illustrate some of problems associated with accuracy assessment of broad-scale land cover maps, and our method, with a set of nine Landsat Thematic Mapper satellite image-based land cover maps from Montana and Wyoming, USA.     

Applying Biodiversity Gap Analysis in a Regional Nature Reserve Design for the Edge of Appalachia, Ohio (U.S.A.)

A biodiversity gap analysis is a method, now usually employing geographic information systems, for identifying deficiencies in existing biodiversity protection. Key principles of gap analysis were applied to a region of southcentral Ohio (U.S.A.) known as The Edge of Appalachia as part of a detailed, large-scale (1:24,000) nature reserve design project. By combining Landsat thematic mapper imagery with ancillary data (bedrock geology, elevation, slope, aspect, and stream proximity), a rule-based model was developed to differentiate and map the natural plant communities present in the 378-km² study area. The model was then used to generate a map depicting the most likely presettlement plant community distributions for the area. These two maps were compared against the 5273 ha owned and managed by state and local conservation organizations. For the current natural plant community distributions, regional land-protection efforts represented each plant community proportionally; however, comparison with the presettlement vegetation clearly identified serious historical losses of several plant community types. Our results suggest that future land acquisitions should emphasize those plant community types that were once more widespread in the region prior to European settlement, a time when natural processes were less compromised by human activity. Current and historical plant-community mapping results were combined and evaluated using the ownership parcel as the fundamental mapping unit. From parcel-based desirability maps a conservation plan was developed that addressed community deficiencies using a representation target of25% for each community type, as derived from the modeled presettlement landscape.     

A multiscale hierarchical Markov transition matrix model for generating and analyzing thematic raster maps

A model is described for generating hierarchically scaled spatial pattern as represented in a thematic raster map. The model involves a series of Markov transition matrices, one for each level in the scaling hierarchy. In full generality, the model allows the transition matrices to be different at each level, potentially making available a large number of parameters for landscape characterization. The model is self-similar when the transition matrices are all equal. A method is presented for fitting the model to data that take the form of a single-resolution thematic raster map. Explicit analytic solutions are obtained for the fitted parameters. The fitting method is based on a relationship between the hierarchical transitions in the model and spatial transitions at varying distance scales in the data map, a categorical analogy of the geostatistical variogram.     

Farmer models of socio-ecologic systems: Application of causal mapping across multiple locations

Modelling ecological or environmental problems has potential to provide understanding of the causes of such problems and to indicate how to better manage them. Özesmi and Özesmi (2004) showed that cognitive or causal mapping can be used to develop maps of socio-ecological systems but these maps were based on stakeholders concerned with one ecosystem. This article shows how maps from a number of different dairy farmers in different locations, but each considering his or her own farm, can be used in meta analysis to make maps that represent how farmers think their farm ecosystem works. It also shows that the combination of causal mapping with the additional technique of Q method provides a useful solution to the practical problem of selecting from a sufficiently broad range of factors with potential to use in a map. Causal mapping in single or multiple locations contributes to the goal of using peoples’ knowledge of ecosystems to improve our understanding of socio-ecological systems.     

On the latent state estimation of nonlinear population dynamics using Bayesian and non-Bayesian state-space models

Nonlinear state-space models have been increasingly applied to study population dynamics and data assimilation in environmental sciences. State-space models can account for process error and measurement error simultaneously to correct for the bias in the estimates of system state and model parameters. However, few studies have compared the performance of different nonlinear state-space models for reconstructing the state of population dynamics from noisy time series. This study compared the performance of the extended Kalman filter (EKF), unscented Kalman filter (UKF) and Bayesian nonlinear state-space models (BNSSM) through simulations. Synthetic population time series were generated using the theta logistic model with known parameters, and normally distributed process and measurement errors were introduced using the Monte Carlo simulations. At higher levels of nonlinearity, the UKF and BNSSM had lower root mean square error (RMSE) than the EKF. The BNSSM performed reliably across all levels of nonlinearity, whereas increased levels of nonlinearity resulted in higher RMSE of the EKF. The Metropolis–Hastings algorithm within the Gibbs algorithm was used to fit the theta logistic model to synthetic time series to estimate model parameters. The estimated posterior distribution of the parameter θ indicated that the 95% credible intervals included the true values of θ (=0.5 and 1.5), but did not include 1.0 and 0.0. Future studies need to incorporate the adaptive Metropolis algorithm to estimate unknown model parameters for broad applications of Bayesian nonlinear state-space models in ecological studies.     

A Bayesian hierarchical approach to quantifying stakeholder attitudes toward conservation in the presence of reporting error

Stakeholder support is vital for achieving conservation success, yet there are few reliable mechanisms to monitor stakeholder attitudes toward conservation. Approaches used to assess attitudes rarely account for bias arising from reporting error, which can lead to falsely reporting a positive attitude toward conservation (false-positive error) or not reporting a positive attitude when the respondent has a positive attitude toward conservation (false-negative error). Borrowing from developments in applied conservation science, we used a Bayesian hierarchical model to quantify stakeholder attitudes as the probability of having a positive attitude toward wildlife notionally (or in abstract terms) and at localized scales while accounting for reporting error. We compared estimates from our model, Likert scores, and naïve estimates (i.e., proportion of respondents reporting a positive attitude in at least 1 question that was only susceptible to false-negative error) with true stakeholder attitudes through simulations. We then applied the model in a survey of tea estate staff on their attitudes toward Asian elephants (Elephas maximus) in the Kaziranga–Karbi Anglong landscape of northeast India. In simulations, Bayesian model estimates of stakeholder attitudes toward wildlife were less biased than naïve estimates or Likert scores. After accounting for reporting errors, we estimated the probability of having a positive attitude toward elephants notionally as 0.85 in the Kaziranga landscape, whereas the proportion of respondents who had positive attitudes toward elephants at a localized scale was 0.50. In comparison, without accounting for reporting errors, naïve estimates of proportions of respondents with positive attitudes toward elephants were 0.69 and 0.23 notionally and at local scales, respectively. False (positive and negative) reporting probabilities were consistently not 0 (0.22–0.68). Regular and reliable assessment of stakeholder attitudes–combined with inference on drivers of positive attitudes–can help assess the success of initiatives aimed at facilitating human behavioral change and inform conservation decision making.     

Testing spatial cluster occurrence in maps equipped with environmentally defined structures

We propose a novel tool for testing hypotheses concerning the adequacy of environmentally defined factors for local clustering of diseases, through the comparative evaluation of the significance of the most likely clusters detected under maps whose neighborhood structures were modified according to those factors. A multi-objective genetic algorithm scan statistic is employed for finding spatial clusters in a map divided in a finite number of regions, whose adjacency is defined by a graph structure. This cluster finder maximizes two objectives, the spatial scan statistic and the regularity of cluster shape. Instead of specifying locations for the possible clusters a priori, as is currently done for cluster finders based on focused algorithms, we alter the usual adjacency induced by the common geographical boundary between regions. In our approach, the connectivity between regions is reinforced or weakened, according to certain environmental features of interest associated with the map. We build various plausible scenarios, each time modifying the adjacency structure on specific geographic areas in the map, and run the multi-objective genetic algorithm for selecting the best cluster solutions for each one of the selected scenarios. The statistical significances of the most likely clusters are estimated through Monte Carlo simulations. The clusters with the lowest estimated p-values, along with their corresponding maps of enhanced environmental features, are displayed for comparative analysis. Therefore the probability of cluster detection is increased or decreased, according to changes made in the adjacency graph structure, related to the selection of environmental features. The eventual identification of the specific environmental conditions which induce the most significant clusters enables the practitioner to accept or reject different hypotheses concerning the relevance of geographical factors. Numerical simulation studies and an application for malaria clusters in Brazil are presented.     

Does scale matter in predicting species distributions? case study with the Marbled Murrelet.

Hierarchical selection orders (selection of microsite, patch, home range, population block, and geographic range) are ideal for dictating spatial grain and extent of animal habitat models, but the resultant conditional models are poor for creating predictive maps. I proposed a two-step approach for accurately mapping probability of animal use that incorporates a single-grain analysis of each selection order in the first step and creates a multi-grain model that combines key variables from each selection order in the second step. Such two-step multi-grain models are strongly recommended because they allow interpretation of the scale of selection for a variable. Using a large data set for the Marbled Murrelet (Brachyramphus marmoratus) as a case study and five selection orders, information theory criteria provided strong support that such models are superior to simpler one-step single-grain models for the murrelet. However, a single-grain model can produce high classification accuracy if it represents the most limiting scale. Notably, accuracy of the two-step multi-grain model was no better than a traditional one-step multi-grain model that ignores selection orders, indicating the advantage of two-step modeling is in elucidating scaling effects, not necessarily in improving accuracy of species distribution maps.     

Predicting the time needed for environmental systematic reviews and systematic maps

Systematic reviews (SRs) and systematic mapping aim to maximize transparency and comprehensiveness while minimizing subjectivity and bias. These are time-consuming and complex tasks, so SRs are considered resource intensive, but published estimates of systematic-review resource requirements are largely anecdotal. We analyzed all Collaboration for Environmental Evidence (CEE) SRs (n = 66) and maps (n = 20) published from 2012 to 2017 to estimate the average number of articles retained at each review stage. We also surveyed 33 experienced systematic reviewers to collate information on the rate at which those stages could be completed. In combination, these data showed that the average CEE SR takes an estimated 164 d (full-time equivalent) (SD 23), and the average CEE systematic map (SM) (excluding critical appraisal) takes 211 d (SD 53). While screening titles and abstracts is widely considered time-consuming, metadata extraction and critical appraisal took as long or longer to complete, especially for SMs. Given information about the planned methods and evidence base, we created a software tool that predicts time requirements of a SR or map with evidence-based defaults as a starting point. Our results shed light on the most time-consuming stages of the SR and mapping processes, will inform review planning, and can direct innovation to streamline processes. Future predictions of effort required to complete SRs and maps could be improved if authors provide more details on methods and results.     

Assessing the effects of pseudo-absences on predictive distribution model performance

Modelling species distributions with presence data from atlases, museum collections and databases is challenging. In this paper, we compare seven procedures to generate pseudo-absence data, which in turn are used to generate GLM-logistic regressed models when reliable absence data are not available. We use pseudo-absences selected randomly or by means of presence-only methods (ENFA and MDE) to model the distribution of a threatened endemic Iberian moth species (Graellsia isabelae). The results show that the pseudo-absence selection method greatly influences the percentage of explained variability, the scores of the accuracy measures and, most importantly, the degree of constraint in the distribution estimated. As we extract pseudo-absences from environmental regions further from the optimum established by presence data, the models generated obtain better accuracy scores, and over-prediction increases. When variables other than environmental ones influence the distribution of the species (i.e., non-equilibrium state) and precise information on absences is non-existent, the random selection of pseudo-absences or their selection from environmental localities similar to those of species presence data generates the most constrained predictive distribution maps, because pseudo-absences can be located within environmentally suitable areas. This study shows that if we do not have reliable absence data, the method of pseudo-absence selection strongly conditions the obtained model, generating different model predictions in the gradient between potential and realized distributions.     

Using cumulative human-impact models to reveal global threat patterns for seahorses

Understanding threats acting on marine organisms and their conservation status is vital but challenging given a paucity of data. We studied the cumulative human impact (CHI) on and conservation status of seahorses (Hippocampus spp.)—a genus of rare and data-poor marine fishes. With expert knowledge and relevant spatial data sets, we built linear-additive models to assess and map the CHI of 12 anthropogenic stressors on 42 seahorse species. We examined the utility of indices of estimated impact (impact of each stressor and CHI) in predicting conservation status for species with random forest (RF) models. The CHI values for threatened species were significantly higher than those for nonthreatened species (category based on International Union for Conservation of Nature Red List). We derived high-accuracy RF models (87% and 96%) that predicted that 5 of the 17 data-deficient species were threatened. Demersal fishing practices with high bycatch and pollution were the best predictors of threat category. Major threat epicenters were in China, Southeast Asia, and Europe. Our results and maps of CHI may help guide global seahorse conservation and indicate that modeling and mapping human impacts can reveal threat patterns and conservation status for data-poor species. We found that for exploring threat patterns of focal species, species-level CHI models are better than existing ecosystem-level CHI models.     

Geochemical interpretation of cancer maps of Finland

In order to reveal possible cause-and-effect relationships and correlations between geochemical variables and the incidences of various forms of cancer, geochemical maps (soil and groundwater) and cancer maps of Finland are compared using standard methods of correlation analysis. The cancer incidence maps published by the Finnish Cancer Registry and soil and groundwater geochemical maps published by the Geological Survey of Finland, both in colour, were decoded to numerical incidence or concentration values by placing a rectangular grid of 684 evenly spaced observation points over each map representing the entire area of the mainland of Finland,i.e. the points were located at intervals of about 25 kilometres on the ground. Bivariate correlation coefficients were calculated between the variables for cancer incidence and the geochemical data matrices. As a general rule, the results show a low degree of correlation between the variables (r = 0.00 – 0.40), which suggests that the types studied of cancer are not related to the geochemical variables. There are a few possible exceptions, however, such as cancer of the colon in males and females in relation to arsenic and uranium in the soil and hardness of the groundwater, where the Spearman product-moment correlation coefficients are 0.59, 0.55 and 0.51 respectively, so that the cancer case may have a geochemical factor implicated in their aetiology, albeit very vaguely. The relatively high correlation coefficients (0.61, 0.62 and 0.63 respectively) recorded for the dependence of total cancer in females on groundwater hardness and uranium and arsenic in till must be regarded as meaningless in view of the multicausative aetiology of total cancer (all forms combined).     

Null model analysis of species associations using abundance data

The influence of negative species interactions has dominated much of the literature on community assembly rules. Patterns of negative covariation among species are typically documented through null model analyses of binary presence/absence matrices in which rows designate species, columns designate sites, and the matrix entries indicate the presence (1) or absence (0) of a particular species in a particular site. However, the outcome of species interactions ultimately depends on population-level processes. Therefore, patterns of species segregation and aggregation might be more clearly expressed in abundance matrices, in which the matrix entries indicate the abundance or density of a species in a particular site. We conducted a series of benchmark tests to evaluate the performance of 14 candidate null model algorithms and six covariation metrics that can be used with abundance matrices. We first created a series of random test matrices by sampling a metacommunity from a lognormal species abundance distribution. We also created a series of structured matrices by altering the random matrices to incorporate patterns of pairwise species segregation and aggregation. We next screened each algorithm-index combination with the random and structured matrices to determine which tests had low Type I error rates and good power for detecting segregated and aggregated species distributions. In our benchmark tests, the best-performing null model does not constrain species richness, but assigns individuals to matrix cells proportional to the observed row and column marginal distributions until, for each row and column, total abundances are reached. Using this null model algorithm with a set of four covariance metrics, we tested for patterns of species segregation and aggregation in a collection of 149 empirical abundance matrices and 36 interaction matrices collated from published papers and posted data sets. More than 80% of the matrices were significantly segregated, which reinforces a previous meta-analysis of presence/absence matrices. However, using two of the metrics we detected a significant pattern of aggregation for plants and for the interaction matrices (which include plant-pollinator data sets). These results suggest that abundance matrices, analyzed with an appropriate null model, may be a powerful tool for quantifying patterns of species segregation and aggregation.     

Crustacean habitat potential mapping in a tidal flat using remote sensing and GIS

To make a macrofaunal (crustacean) habitat potential map, the spatial distribution of ecological variables in the Hwangdo tidal flat, Korea, was explored. Spatial variables were mapped using remote sensing and a geographic information system (GIS) combined with field observations. A frequency ratio (FR) and logistic regression (LR) model were employed to map the macrofauna potential area for the Ilyoplax dentimerosa, a crustacean species. Spatial variables affecting the tidal macrofauna distribution were selected based on abundance and biomass and used within a spatial database derived from remotely sensed data of various types of sensors. The spatial variables included the intertidal digital elevation model (DEM), slope, distance from a tidal channel, tidal channel density, surface sediment facies, spectral reflectance of the near infrared (NIR) bands and the tidal exposure duration. The relation between the I. dentimerosa and each spatial variable was calculated using the FR and LR. The species was randomly divided into a training set (70%) to analyse habitat potential using FR and LR and a test set (30%) to validate the predicted habitat potential map. The relations were overlaid to produce a habitat potential map with the species potential index (SPI) value for each pixel. The potential habitat maps were compared with the surveyed habitat locations such as validation data set. The comparison results showed that the LR model (accuracy is 85.28%) is better in prediction than the FR (accuracy is 78.96%) model. The performance of models gave satisfactory accuracies. The LR provides the quantitative influence of variables on a potential habitat of species; otherwise, the FR shows the quantitative influence of a class in each variable. The combination of a GIS-based frequency ratio and logistic regression models and remote sensing with field observations is an effective method to determine locations favorable for macrofaunal species occurrences in a tidal flat.