Mapping forest vegetation patterns in an Atlantic-Mediterranean transitional area by integration of ordination and geostatistical techniques

Aims Forest vegetation variability may be explained by the complex interplay among several spatial structuring factors, including climate and topography. We modelled the spatial variability of forest vegetation assemblages and significant environmental variables along a complex environmental gradient or coenocline to produce a detailed cartographic database portraying the distribution of forests along it.Methods We combined an analysis of ordination coenoclines with kriging over 772 field data plots from the third Spanish National Forest Inventory in an Atlantic–Mediterranean transitional area (northern Spain).Important findings The best fitted empirical semivariogram revealed a strong spatial structure of forest species composition along the complex environmental gradient considered (the climatic–topographic gradient from north to south). The steady and gradual increase of semivariance with a marked lag distance indicates a gradual turnover of forest assemblages according to the climatic–topographic variations (regional or local). Two changes in the slope of the semivariogram suggest the existence of two different scales of spatial variation. The interpolation map by Kriging of forest vegetation assemblages along the main coenocline shows a clear spatial distribution pattern of trees and shrubs in accordance with the spatial variation of significant environmental variables. We concluded that the multivariate geostatistical approach is a suitable technique for spatial analysis of forest systems employing data from national forest inventories based on a regular network of field plots. The development of an assortment of maps describing changes in vegetation assemblages and variation in environmental variables is expected to be a suitable tool for an integrated forest management and planning.     

A new forest cover map of continental southeast Asia derived from SPOT‐VEGETATION satellite imagery

Question: Can recent satellite imagery of coarse spatial resolution support forest cover assessment and mapping at the regional level? Location: Continental southeast Asia. Methods: Forest cover mapping was based on digital classification of SPOT4‐VEGETATION satellite images of 1 km spatial resolution from the dry seasons 1998/1999 and 1999/2000. Following a geographical stratification, the spectral clusters were visually assigned to land cover classes. The forest classes were validated by an independent set of maps, derived from interpretation of satellite imagery of high spatial resolution (Landsat TM, 30 m). Forest area estimates from the regional forest cover map were compared to the forest figures of the FAO database. Results: The regional forest cover map displays 12 forest and land cover classes. The mapping of the region's deciduous and fragmented forest cover remained challenging. A high correlation was found between forest area estimates obtained from this map and from the Landsat TM derived maps. The regional and sub‐regional forest area estimates were close to those reported by FAO. Conclusion: SPOT4‐VEGETATION satellite imagery can be used for mapping consistently and uniformly the extent and distribution of the broad forest cover types at the regional scale. The new map can be considered as an update and improvement on existing regional forest cover maps.     

Mapping the spatial distribution of plant diversity indices in a tropical forest using multi-spectral satellite image classification and field measurements

The relationships among alpha and beta diversity indices, computed from 141 randomly sampled quadrats, and the vegetation classes obtained by multi-spectral satellite image classification, were used as a strategy for mapping plant diversity in a tropical landscape mosaic. A relatively high accuracy of the land cover map was revealed by the overall accuracy assessment and the Cohen's Kappa statistic. Species accumulation models were used to evaluate how representative the sample size was the different vegetation types. A standard one-way, between-subjects ANOVA confirmed a significant reduction of the within-class variance of plant diversity with respect to their total variance across the landscape. Computed uniformity indices, to assess the internal uniformity of vegetation classes on the diversity indices, confirmed the goodness of the mapped classes in stratifying variability of plant diversity. This allowed for the use of the mapped classes as spatial interpolators of plant diversity values for estimation and up-scaling purposes. Finally, it was revealed that the plant diversity of the landscape depends, to a large extent, on the diversity contained in the most mature forest class, which is also the most diverse community in the studied area. High and moderate beta diversity values between mature forests and both the secondary associations and the first stages of succession, respectively, indicated that there is a significant contribution to the diversity of the landscape by those vegetation classes.     

Ecological response surfaces for North American boreal tree species and their use in forest classification

Abstract. Empirical ecological response surfaces were derived for eight dominant tree species in the boreal forest region of Canada. Stepwise logistic regression was used to model species dominance as a response to five climatic predictor variables. The predictor variables (annual snowfall, degree-days, absolute minimum temperature, annual soil moisture deficit, and actual evapotranspiration summed over the summer months) influence the response of plants more directly than the annual or monthly measures of temperature and precipitation commonly used in response surface modeling. The response surfaces provided estimates of the probability of species dominance across the spatial extent of North America with a high degree of success. Much of the variation in the probability of dominance is apparently related to the species' individualistic response to climatic constraints within different airmass regions. A forest type classification for the Canadian boreal forest region was derived by a cluster analysis based on the probability estimates. Five major forest types were distinguished by the application of a stopping rule. The predicted forest types showed a high degree of geographic correspondence with the distribution of forest types in the actual vegetation mosaic. The distribution of the predicted types also bears a direct relationship to seasonal airmass dynamics in the boreal forest region.     

The TRM Model of Potential Natural Vegetation in Mountain Forests

Due to advances in spatial modeling and improved availability of digital geodata, traditional mapping of potential natural vegetation (PNV) can be replaced by ecological modeling approaches. We developed a new model to map forest types representing the potential natural forest vegetation in the Bavarian Alps. The TRM model is founded on a three-dimensional system of the ecological gradients temperature (T), soil reaction (R), and soil moisture (M). Within such a “site cube” forest types are defined as homogenous site units that give rise to forest communities with comparable species composition, structure, production and protective functions. The three gradients were modeled using regression algorithms with area-wide, high resolution geodata on climate, relief and soil as predictors and average Ellenberg indicator values for temperature, acidity and moisture of vegetation plots as dependent variables summarizing plant responses to ecological gradients. The resulting predictor-response relationships allowed us to predict gradient positions of each raster cell in the region from geodata layers. The three-dimensional system of gradients was partitioned into 26 forest types, which can be mapped for the whole region. TRM-based units are supplemented by 22 forest types of special sites defined by other ecological factors such as geomorphology, for which individual GIS rules were developed. The application of our model results in an intermediate-scale map of potential natural forest vegetation, which is based on an explicit function of temperature, reaction and moisture and is therefore consistent and repeatable in contrast to traditional PNV maps.     

Models of large-scale breeding-bird distribution as a function of macro-climate in Ontario, Canada

Aim We modelled the relationship of breeding evidence for five species of forest songbirds (ruby-crowned kinglet (Regulus calendula) Blackburnian warbler (Dendroica fusca), black-throated blue warbler (Dendroica caerulescens), bay-breasted warbler (Dendrioca castanea) and Connecticut warbler (Oporornis agilis)) and a variety of macro-climate variables to examine the importance of climate as a factor determining distribution of breeding in these species and to assess the usefulness of spatial predictions generated from these models. Location Modelling was conducted over the entire province of Ontario, Canada, an area of ≈900,000 km². Methods Data on the distribution of breeding in the province was derived from the Breeding Bird Atlas of Ontario. We used logistic regression to model the relationship between the probability of breeding (assessed in 10 km×10 km blocks) and estimates of a variety of climate variables at the same scale. Models were selected that had the least number of explanatory variables while at the same time having close to the best possible classification accuracy. Results The final models for these five species had from one to six explanatory variables and an overall concordance of 70.4% to 86.3% indicating a good classification accuracy. Results from subsampling 50% of the original data ten times indicate that (1) the classification accuracy of the model for data used to generate the model is not very sensitive to the specific observations used to generate the model (2) the classification accuracy of test data is close to the classification accuracy of the model data and (3) the classification accuracy of the test data is not dependent on the specific observations used to generate the model. We generated a spatial prediction of the probability of occurrence of each species for Ontario using the relationships defined by the logistic regression models and using 1 km gridded estimates of the necessary climate variables. These probability maps closely matched the maps of observed evidence of breeding from the Atlas. Main conclusions Although mechanisms controlling breeding distribution cannot be determined using this method, we can conclude that (1) macro-climate is an important factor directly and/or indirectly determining distribution of breeding in these species and (2) spatial predictions of probability of breeding are accurate enough to be useful in predicting probability of breeding in unsampled areas.     

Predicting vegetation types at treeline using topography and biophysical disturbance variables

Abstract. The relationships between four vegetation types and variables representing topography and biophysical disturbance gradients were modeled for a study area in east-central Glacier National Park, Montana. Four treeline transition vegetation types including closed-canopy forest, open-canopy forest, meadow, and unvegetated surfaces (e.g. rock, snow, and ice) were identified and mapped through classification of satellite data and subsequent field verification. Topographic characteristics were represented using a digital elevation model and three variables derived from topoclimatic potential models (solar radiation potential, snow accumulation potential, and soil saturation potential). A combination of generalized additive and generalized linear modeling (GAM and GLM, respectively) techniques was used to construct logistic regression models representing the distributions of the four vegetation types. The variables explained significant amounts of variation in the vegetation types, but high levels of variation remained unexplained. A comparison of ‘expected’ and ‘observed’ vegetation patterns suggested that some unexplained variation may have occurred at the basin scale. A suite of tools and techniques is presented that facilitates predicting landscape-scale vegetation patterns and testing hypotheses about the spatial controls on those patterns.     

Classification and Mapping of Coenotic Diversity of Forests

The proposed approach to the study of regularities of spatial variability of plant cover and to mapping forest vegetation is illustrated by the example of European Russia. It is shown that remote sensing and GIS technologies require particular standards of plant cover classification and reflection in maps. The given principles of classification and compilation of explications for maps of forest cover enable an assessment of its status and dynamics and a comparison of materials of different scales. We use the ecological–phytocoenotic approach to classifying forest vegetation. The specified units correspond to the categories of the main classifications of plant cover used in Russian geobotanics. In our classification, we have verified some parameters and the semantics of the mapped units, using satellite images, for their definite identification and interpretation. The elaborated approach to the classification and mapping of forest cover is applied for the study of the diversity of spruce forests under different climatic conditions in two regions, where they occupy about 20% of the total area. The first example characterizes the northern taiga subzone of forests of eastern Fennoscandia in the center of Murmansk oblast, and the second one represents the subzone of broad-leaved–coniferous forest in the southwest of Moscow oblast.     

Climate and vegetation hierarchically structure patterns of songbird distribution in the Canadian boreal region

Environmental factors controlling the distribution and abundance of boreal avifauna are not fully understood, limiting our ability to predict the consequences of a changing climate and industrial development activities underway. We used a compilation of avian point‐count data, collected over 1990–2008 from nearly 36 000 locations, to model the abundance of individual forest songbird species within the Canadian boreal forest. We evaluated 30 vegetation and 101 climatic variables, representing most of the widely‐used dimensions of climate space, along with less usual measures of inter‐annual variability. Regression tree models allowed us to calculate the relative importance of climate and vegetation variable classes according to avian migration strategy without the need for a priori variable selection or dimension reduction. We tested for hierarchical habitat selection by formulating hypotheses on the locations of variables within the model tree structures. Climate variables explained the majority (77%) of deviance explained over 98 species modelled. As may be expected at high latitudes, we found energy availability (temperature, 65%) to be more important than moisture availability (precipitation, 12%). The contributions of inter‐ and intra‐annual climate variability (28%) were about half that of mean conditions. The relatively large contribution of remotely‐sensed vegetation metrics (23%) highlighted the importance of local vegetation heterogeneity controlled by non‐climatic factors. The two most important vegetation variables were landcover type and April leaf area index. When selected, these generally occurred in a model's right subtree, consistent with predictions from hierarchical habitat selection theory. When occupying the root node, landcover effectively delineated the historical forest‐prairie ecotone, reflecting the current disequilibrium between climate and vegetation due to human land use. Our findings suggest a large potential for avian distributional shifts in response to climate change, but also demonstrate the importance of finer scale vegetation heterogeneity in the spatial distribution of boreal birds.     

Ecosystem classification and inventory maps as surrogates for ground beetle assemblages in boreal forest

Aims We compare performance of ecosystem classification maps and provincial forest inventory data derived from air photography in reflecting ground beetle (Coleoptera: Carabidae) biodiversity patterns that are related to the forest canopy mosaic. Our biodiversity surrogacy model based on remotely sensed tree canopy cover is validated against field-collected ground data.Methods We used a systematic sampling grid of 198 sites, covering 84 km 2 of boreal mixedwood forest in northwestern Alberta, Canada. For every site, we determined tree basal area, characterized the ground beetle assemblage and obtained corresponding provincial forest inventory and ecosystem classification information. We used variation partitioning, ordination and misclassification matrices to compare beetle biodiversity patterns explained by alternative databases and to determine model biases originating from air photo-interpretation.Important findings Ecosystem classification data performed better than canopy cover derived from forest inventory maps in describing ground beetle biodiversity patterns. The biodiversity surrogacy models based on provincial forest inventory maps and field survey generally detected similar patterns but inaccuracies in air photo-interpretation of relative canopy cover led to differences between the two models. Compared to field survey data, air photo-interpretation tended to confuse two Picea species and two Populus species present and homogenize stand mixtures. This generated divergence in models of ecological association used to predict the relationship between ground beetle assemblages and tree canopy cover. Combination of relative canopy cover from provincial inventory with other geo-referenced land variables to produce the ecosystem classification maps improved biodiversity predictive power. The association observed between uncommon surrogates and uncommon ground beetle species emphasizes the benefits of detecting these surrogates as a part of landscape management. In order to complement conservation efforts established in protected areas, accurate, high resolution, wide ranging and spatially explicit knowledge of landscapes under management is primordial in order to apply effective biodiversity conservation strategies at the stand level as required in the extensively harvested portion of the boreal forest. In development of these strategies, an in-depth understanding of vegetation is key.     

A GIS simulation of potential vegetation in China under different climate scenarios at the end of the 21st century

The study of potential vegetation can reveal the impact of climate on changes in vegetation patterns. It is the starting point for studying vegetation-environmental classification and relationships, and it is the key point for studying global change and terrestrial ecosystems. By using the Comprehensive Sequential Classification System (CSCS) and the meteorological data under the four climate change scenarios from the IPCC5 publication, the present paper carries out a GIS simulation study of the spatial distribution of potential vegetation in China at the end of the 21st century. The results indicate that under the four climate scenarios at the end of the 21st century: (1) The potential vegetation in China shows significant horizontal and vertical distribution, which corresponds well to those of natural topographic features. (2) There are 40 classes of potential vegetation in China. Tropical-extrarid tropical desert (VIIA), which has no corresponding condition of growth in China, is commonly lacking, and differences exist among the potential vegetation classes and among the ratios of the classes under different scenarios. (3) From the perspective of categories, temperate forest is the most widely distributed, and savanna is the least widely distributed. Together with the strengthening of the radiation intensity according to RCP2.6 → RCP4.5 → RCP6.0 → RCP8.5, the area covered by cold-dry potential vegetation decreases as the area covered by warm-humid potential vegetation increases. As a result, the areas of tundra and alpine steppe, frigid desert, steppe, and temperate humid grassland tend to decrease, and those of semi-desert, temperate forest, sub-tropical forest, tropical forest, warm desert, and savanna tend to increase. Moreover, the potential vegetation in China at the end of the 21st century would change at different levels and in different directions when compared with that at the end of the 20th century. (4) In the same period, potential vegetation in different regions shows differences in their sensitivity to climate change, and by the end of the 21st century, 30.73% of land in China would be classified as a sensitive region, which highly corresponds to the current ecologically vulnerable zone, and whose potential vegetation easily evolves along with changes of climate scenarios.     

Ningaloo Reef: Shallow Marine Habitats Mapped Using a Hyperspectral Sensor

Research, monitoring and management of large marine protected areas require detailed and up-to-date habitat maps. Ningaloo Marine Park (including the Muiron Islands) in north-western Australia (stretching across three degrees of latitude) was mapped to 20 m depth using HyMap airborne hyperspectral imagery (125 bands) at 3.5 m resolution across the 762 km² of reef environment between the shoreline and reef slope. The imagery was corrected for atmospheric, air-water interface and water column influences to retrieve bottom reflectance and bathymetry using the physics-based Modular Inversion and Processing System. Using field-validated, image-derived spectra from a representative range of cover types, the classification combined a semi-automated, pixel-based approach with fuzzy logic and derivative techniques. Five thematic classification levels for benthic cover (with probability maps) were generated with varying degrees of detail, ranging from a basic one with three classes (biotic, abiotic and mixed) to the most detailed with 46 classes. The latter consisted of all abiotic and biotic seabed components and hard coral growth forms in dominant or mixed states. The overall accuracy of mapping for the most detailed maps was 70% for the highest classification level. Macro-algal communities formed most of the benthic cover, while hard and soft corals represented only about 7% of the mapped area (58.6 km²). Dense tabulate coral was the largest coral mosaic type (37% of all corals) and the rest of the corals were a mix of tabulate, digitate, massive and soft corals. Our results show that for this shallow, fringing reef environment situated in the arid tropics, hyperspectral remote sensing techniques can offer an efficient and cost-effective approach to mapping and monitoring reef habitats over large, remote and inaccessible areas.     

Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem

Soil organic carbon (SOC) plays an important role in soil fertility and carbon sequestration, and a better understanding of the spatial patterns of SOC is essential for soil resource management. In this study, we used boosted regression tree (BRT) and random forest (RF) models to map the distribution of topsoil organic carbon content at the northeastern edge of the Tibetan Plateau in China. A set of 105 soil samples and 12 environmental variables (including topography, climate and vegetation) were analyzed. The performance of the models was evaluated using a 10-fold cross-validation procedure. Maps of the mean values and standard deviations of SOC were generated to illustrate model variability and uncertainty. The results indicate that the BRT and RF models exhibited very similar performance and yielded similar predicted distributions of SOC. The two models explained approximately 70% of the total SOC variability. The BRT and RF models robustly predicted the SOC at low observed SOC values, whereas they underestimated high observed SOC values. This underestimation may have been caused by biased distributions of soil samples in the SOC space. Vegetation-related variables were assigned the highest importance in both models, followed by climate and topography. Both models produced spatial distribution maps of SOC that were closely related to vegetation cover. The SOC content predicted by the BRT model was clearly higher than that of the RF model in areas with greater vegetation cover because the contributions of vegetation-related variables in the two models (65% and 43%, respectively) differed significantly. The predicted SOC content increased from the northwestern to the southeastern part of the study area, average values produced by the BRT and RF models were 27.3 g kg⁻¹ and 26.6 g kg⁻¹, respectively. We conclude that the BRT and RF methods should be calibrated and compared to obtain the best prediction of SOC spatial distribution in similar regions. In addition, vegetation variables, including those obtained from remote sensing imagery, should be taken as the main environmental indicators and explicitly included when generating SOC maps in Alpine environments.     

Sensitivity of tropical forests to climate change in the humid tropics of north Queensland

An analysis using an artificial neural network model suggests that the tropical forests of north Queensland are highly sensitive to climate change within the range that is likely to occur in the next 50–100 years. The distribution and extent of environments suitable for 15 structural forest types were estimated, using the model, in 10 climate scenarios that include warming up to 1°C and altered precipitation from –10% to +20%. Large changes in the distribution of forest environments are predicted with even minor climate change. Increased precipitation favours some rainforest types, whereas decreased rainfall increases the area suitable for forests dominated by sclerophyllous genera such as Eucalyptus and Allocasuarina. Rainforest environments respond differentially to increased temperature. The area of lowland mesophyll vine forest environments increases with warming, whereas upland complex notophyll vine forest environments respond either positively or negatively to temperature, depending on precipitation. Highland rainforest environments (simple notophyll and simple microphyll vine fern forests and thickets), the habitat for many of the region’s endemic vertebrates, decrease by 50% with only a 1°C warming. Estimates of the stress to present forests resulting from spatial shifts of forest environments (assuming no change in the present forest distributions) indicate that several forest types would be highly stressed by a 1°C warming and most are sensitive to any change in rainfall. Most forests will experience climates in the near future that are more appropriate to some other structural forest type. Thus, the propensity for ecological change in the region is high and, in the long term, significant shifts in the extent and spatial distribution of forests are likely. A detailed spatial analysis of the sensitivity to climate change indicates that the strongest effects of climate change will be experienced at boundaries between forest classes and in ecotonal communities between rainforest and open woodland.     

Mapping vegetation in an arid,mountainous region of Western Australia

Abstract. Predictive mapping of vegetation using models linking vegetation units to mapped environmental variables has been advocated for remote areas. In this study, three different types of model were employed (within a GIS) to produce vegetation maps of the Hamersley Ranges region of Western Australia. The models were: (1) decision trees; (2) statistical models; and (3) heuristic/conceptual models. Maps were produced for three different levels of a floristic classification, i.e. 16 communities in two community groups with eight subgroups. All models satisfactorily established relationships between the vegetation units and available predictor variables, except where the number of sites of a particular unit was small. The different models often made similar predictions, especially for more widespread vegetation units. Map accuracy (as determined by field testing of maps) improved with increasing level of abstraction, with plant community maps ca. 50 % correct, subgroup maps ca. 60 % correct and group maps 90 % correct. Map inaccuracies were due to several factors, including low sample numbers producing unrepresentative models, poor resolution of and errors in available maps of predictor variables, and available predictor variables not being able to differentiate between certain vegetation units, particularly at the plant community level. Of these factors, poor resolution of maps was seen as the most critical. One type of model could not be recommended over another; however the choice of model will be largely dependent on the nature of the data set and the type of map coverage required.     

An Untidy Cover: Invasion of Bracken Fern in the Shifting Cultivation Systems of Southern Yucatán, Mexico

In land change science studies, a cover type is defined by land surface attributes, specifically including the types of vegetation, topography and human structures, which makes it difficult to characterize land cover as discrete classes. One of the challenges in characterizing a land-cover type is to distinguish variability within the class from actual land-cover transformation. The spread of plant invasions in tropical systems is affected by seasonal variations and disturbances such as agricultural activities and fires, making it difficult to determine the spread through thematic classifications. In this paper, we estimate the changes in spatial extent and seasonal variation of bracken fern invasion in Southern Yucatán from 1989 to 2005 by using a linear mixture model (LMM), a widely used method in the classification of remotely sensed data. The results show an increase in areas affected by bracken from 40 km² in 1989 to almost 80 km² in 2000. Lower estimates of the invasion resulted from data acquired at the end of the dry season (March–May), when bracken mixes with secondary vegetation or is removed by fires. The accuracy of the maps is estimated through the use of sketch maps of farmer's parcels and field data collected from 2000 to 2001. Understanding the spatial distribution and annual variability of bracken fern cover in the region is critical to determining the relation between disturbances such as fire and forest recovery. Using LMM may enhance this understanding by giving a more accurate picture of the extent and distribution of bracken fern invasion.
Abstract in Spanish is available at http://www.blackwell-synergy.com/loi/btp     

Predictive performance of random forest on the identification of mangrove species in arid environments

Machine learning methods are the most popular approaches for carrying out classification in remote sensing studies. Of the methods available, random forest (RF) is the one most often used due to its high predictive performance. The objective of this study was to assess the predictive performance of RF in identifying (classifying) mangrove species in an arid environment using two cameras: one conventional (visible part of the light, RGB), the other specialized (Green, Red, Near-infrared, GRN). The RGB and GRN bands were used with derived vegetation indexes (for each camera), the canopy height model (derived from photogrammetry), and distance to water (derived from raster analysis) to classify the study area in eight classes (including three mangrove species) using RF. Results suggest only slight differences in predictive performance (validation) between the products derived from the GRN and RGB cameras, the accuracy values ranged from 0.58 to 0.77 and from 0.53 to 0.72 for RGB and GRN, respectively. The most important variables were the distance to water and canopy height model for both cameras, followed by specific bands and vegetation indices. The study concludes that conventional cameras mounted in commercial drones can be used efficiently to identify mangrove species in arid environments when the classification model uses physical variables of the species (tree height) and the system (distance to water). Results of this study can be applied to describe spatial distributions by species in small or large patches of mangroves in arid environments, thus improving our ecological knowledge of this ecosystem.