Florencia Sangermano

Global Trends in Seasonality of Normalized Difference Vegetation Index (NDVI), 1982–2011

A 30-year series of global monthly Normalized Difference Vegetation Index (NDVI) imagery derived from the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g archive was analyzed for the presence of trends in changing seasonality. Using the Seasonal Trend Analysis (STA) procedure, over half (56.30%) of land surfaces were found to exhibit significant trends. Almost half (46.10%) of the significant trends belonged to three classes of seasonal trends (or changes). Class 1 consisted of areas that experienced a uniform increase in NDVI throughout the year, and was primarily associated with forested areas, particularly broadleaf forests. Class 2 consisted of areas experiencing an increase in the amplitude of the annual seasonal signal whereby increases in NDVI in the green season were balanced by decreases in the brown season. These areas were found primarily in grassland and shrubland regions. Class 3 was found primarily in the Taiga and Tundra biomes and exhibited increases in the annual summer peak in NDVI. While no single attribution of cause could be determined for each of these classes, it was evident that they are primarily found in natural areas (as opposed to anthropogenic land cover conversions) and that they are consistent with climate-related ameliorations of growing conditions during the study period.

LAND CHANGE IN THE SOUTHERN YUCATÁN AND CALAKMUL BIOSPHERE RESERVE: EFFECTS ON HABITAT AND BIODIVERSITY

The southern Yucatán contains the largest expanse of seasonal tropical forests remaining in Mexico, forming an ecocline between the drier north of the peninsula and the humid Petén, Guatemala. The Calakmul Biosphere Reserve resides in the center of this region as part of the Mesoamerican Biological Corridor. The reserves functions are examined in regard to land changes throughout the region, generated over the last 40 years by increasing settlement and the expansion and intensification of agriculture. These changes are documented from 1987/1988 to 2000, and their implications regarding the capacity of the reserve to protect the ecocline, forest habitats, and butterfly diversity are addressed. The results indicate that the current landscape matrix serves the biotic diversity of the reserve, with several looming caveats involving the loss of humid forests and the interruption of biota flow across the ecocline, and the amount and proximity of older forest patches beyond the reserve. The highly dynamic land cover changes underway in this economic frontier warrant an adaptive management approach that monitors the major changes underway in mature forest types, while the paucity of systematic ecological and environment-development studies is rectified in order to inform policy and practice.

Seasonal trend analysis of image time series

A procedure is introduced for the analysis of seasonal trends in time series of Earth observation imagery. Called Seasonal Trend Analysis (STA), the procedure is based on an initial stage of harmonic analysis of each year in the series to extract the annual and semi‐annual harmonics. Trends in the parameters of these harmonics over years are then analysed using a robust median‐slope procedure. Finally, images of these trends are used to create colour composites highlighting the amplitudes and phases of seasonality trends. The technique specifically rejects high‐frequency sub‐annual noise and is robust to short‐term interannual variability up to a period of 29% of the length of the series. It is, thus, a very effective procedure for focusing on the general nature of longer‐term trends in seasonality.

Evaluation of species distribution model algorithms for fine-scale container-breeding mosquito risk prediction

The present work evaluates the use of species distribution model (SDM) algorithms to classify high densities of small container‐breeding Aedes mosquitoes (Diptera: Culicidae) on a fine scale in the Bermuda Islands. Weekly ovitrap data collected by the Department of Health, Bermuda for the years 2006 and 2007 were used for the models. The models evaluated included the algorithms Bioclim, Domain, GARP (genetic algorithm for rule‐set prediction), logistic regression and MaxEnt (maximum entropy). Models were evaluated according to performance and robustness. The area under the receiver operating characteristic curve was used to evaluate each models performance, and robustness was assessed according to the spatial correlation between classification risks for the two datasets. Relative to the other algorithms, logistic regression was the best and MaxEnt the second best model for classifying high‐risk areas. We describe the importance of covariables for these two models and discuss the utility of SDMs in vector control efforts and the potential for the development of scripts that automate the task of creating risk assessment maps.

A step-wise land-cover classification of the tropical forests of the Southern Yucatan, Mexico

Analysis of land-cover change in the seasonal tropical forests of the Southern Yucatán, Mexico presents a number of significant challenges for the fine-scale land-cover information required of land-change science. Subtle variation in mature forest types across the regional ecocline is compounded by vegetation transitions following agricultural land uses. Such complex mapping environments require innovation in multispectral classification methodologies. This research presents an application of a step-wise maximum likelihood/In-Process Classification Assessment (IPCA) procedure. This hybrid supervised and unsupervised classification methodology allows for exploration of underlying characteristics of Landsat Thematic Mapper (TM) imagery in tropical environments. Once spectrally separable classes have been identified, field data then determine the ecological definition of vegetation types with special attention paid to areas of unknown or mixed classes. A post-field assessment re-classification using the Dempster–Shafer method reduced the original 25 spectral classes to 14 ecologically distinctive classes, providing the fine-tuned land-cover distinctions that are required for both environmental and socioeconomic research questions. The overall map accuracy was 87% with an average per-class accuracy of 86%. Per-class accuracy ranged from as low as 45% for pasture grass to a high of 100% for tall-stature evergreen upland forest, low and medium-stature semi-deciduous upland forest and deciduous forest.

Similarity Weighted Instance-based Learning for the Generation of Transition Potentials in Land Use Change Modeling

Land use change models are increasingly being used to evaluate the effect of land change on climate and biodiversity and to generate scenarios of deforestation. Although many methods are available to model land transition potentials, they are usually not user-friendly and require the specification of many parameters, making the task difficult for decision makers not familiar with the tools, as well as making the process difficult to interpret. In this article we propose a simple method for modeling transition potentials. SimWeight is an instance-based learning algorithm based on the logic of the K-Nearest Neighbor algorithm. The method identifies the relevance of each driver variable and predicts the transition potential of locations given known instances of change. A case study was used to demonstrate and validate the method. Comparison of results with the Multi-Layer Perceptron neural network (MLP) suggests that SimWeight performs similarly in its capacity to predict transition potentials, without the need for complex parameters. Another advantage of SimWeight is that it is amenable to parallelization for deployment on a cloud computing platform.

A GIS framework for the refinement of species geographic ranges

The archives of species range polygons developed under comprehensive assessments of the conservation status of species, such as the IUCNs Global Assessments, are a significant resource in the analysis of biodiversity for conservation planning. Species range polygons obtained from these studies are known to exhibit omissions (because of knowledge gaps) and imprecision in their boundaries. In this work, we present a method to refine those species range polygons in order to create more realistic representations of species geographic ranges. Using range polygons of four species of mammals in South America and environmental variables at a 1 km resolution, combined with a set of GIS algorithms, a procedure was developed to map the confidence that sub-polygon elements belong to a logical species range. The confidence map is then used as a weight for a Mahalanobis typicality empirical modelling procedure to generate a map of species-weighted typicalities that is then thresholded to generate the refined species range map. Methods for variable selection and quality assessment of the refined range are also included in the procedure. Analysis using independent validation data shows the power of this methodology to redefine species ranges in a more biophysically reasonable way. The quality of the final-range map depends on the habitat suitability threshold used to define the species range. The report of quality assessment produced is useful for identifying not only the threshold that produces the highest match to the original expert range but also for flagging those ranges with higher discrepancies, facilitating the identification of ranges that need further revision.

Forest baseline and deforestation map of the Dominican Republic through the analysis of time series of MODIS data.

Deforestation is one of the major threats to habitats in the Dominican Republic. In this work we present a forest baseline for the year 2000 and a deforestation map for the year 2011. Maps were derived from Moderate Resolution Imaging Radiometer (MODIS) products at 250 m resolution. The vegetation continuous fields product (MOD44B) for the year 2000 was used to produce the forest baseline, while the vegetation indices product (MOD13Q1) was used to detect change between 2000 and 2011. Major findings based on the data presented here are reported in the manuscript “Habitat suitability and protection status of four species of amphibians in the Dominican Republic” (Sangermano et al., Appl. Geogr.,) [7].63, 2015, 55–65

Modeling the risk of spread and establishment for Asian longhorned beetle (Anoplophora glabripennis) in Massachusetts from 2008-2009

Abstract Land managers responsible for invasive species removal in the USA require tools to prevent the Asian longhorned beetle (Anoplophora glabripennis) (ALB) from decimating the maple-dominant hardwood forests of Massachusetts and New England. Species distribution models (SDMs) and spread models have been applied individually to predict the invasion distribution and rate of spread, but the combination of both models can increase the accuracy of predictions of species spread over time when habitat suitability is heterogeneous across landscapes. First, a SDM was fit to 2008 ALB presence-only locations. Then, a stratified spread model was generated to measure the probability of spread due to natural and human causes. Finally, the SDM and spread models were combined to evaluate the risk of ALB spread in Central Massachusetts in 2008–2009. The SDM predicted many urban locations in Central Massachusetts as having suitable environments for species establishment. The combined model shows the greatest risk of spread and establishment in suitable locations immediately surrounding the epicentre of the ALB outbreak in Northern Worcester with lower risk areas in suitable locations only accessible through long-range dispersal from access to human transportation networks. The risk map achieved an accuracy of 67% using 2009 ALB locations for model validation. This model framework can effectively provide risk managers with valuable information concerning the timing and spatial extent of spread/establishment risk of ALB and potential strategies needed for effective future risk management efforts.