Efforts are increasingly being made to classify the world’s wetland resources, an important ecosystem and habitat that is diminishing in abundance. There are multiple remote sensing classification methods, including a suite of nonparametric classifiers such as decision-tree (DT), rule-based (RB), and random forest (RF). High-resolution satellite imagery can provide more specificity to the classified end product, and ancillary data layers such as the Normalized Difference Vegetation Index, and hydrogeomorphic layers such as distance-to-a-stream can be coupled to improve overall accuracy (OA) in wetland studies. In this paper, we contrast three nonparametric machine-learning algorithms (DT, RB, and RF) using a large field-based dataset (n = 228) from the Selenga River Delta of Lake Baikal, Russia. We also explore the use of ancillary data layers selected to improve OA, with a goal of providing end users with a recommended classifier to use and the most parsimonious suite of input parameters for classifying wetland-dominated landscapes. Though all classifiers appeared suitable, the RF classification outperformed both the DT and RB methods, achieving OA >81%. Including a texture metric (homogeneity) substantially improved the classification OA. However, including vegetation/soil/water metrics (based on WorldView-2 band combinations), hydrogeomorphic data layers, and elevation data layers to increase the descriptive content of the input parameters surprisingly did not markedly improve the OA. We conclude that, in most cases, RF should be the classifier of choice. The potential exception to this recommendation is under the circumstance where the end user requires narrative rules to best manage his or her resource. Though not useful in this study, continuously increasing satellite imagery resolution and band availability suggests the inclusion of ancillary contextual data layers such as soil metrics or elevation data, the granularity of which may define its utility in subsequent wetland classifications.

Aims: Eurasian forest‐steppes are among the most complex non‐tropical terrestrial ecosystems. Despite their considerable scientific, ecological and economic importance, knowledge of forest‐steppes is limited, particularly at the continental scale. Here we provide an overview of Eurasian forest‐steppes across the entire zone: (a) we propose an up‐to‐date definition of forest‐steppes, (b) give a short physiogeographic outline, (c) delineate and briefly characterize the main forest‐steppe regions, (d) explore forest‐steppe biodiversity and conservation status, and (e) outline forest‐steppe prospects under predicted climate change.Location: Eurasia (29°–56°N, 16°–139°E).Results and Conclusions: Forest‐steppes are natural or near‐natural vegetation complexes of arboreal and herbaceous components (typically distributed in a mosaic pattern) in the temperate zone, where the co‐existence of forest and grassland is enabled primarily by the semi‐humid to semi‐arid climate, complemented by complex interactions of biotic and abiotic factors operating at multiple scales. This new definition includes lowland forest–grassland macromosaics (e.g. in Eastern Europe), exposure‐related mountain forest‐steppes (e.g. in Inner Asia), fine‐scale forest–grassland mosaics (e.g. in the Carpathian Basin) and open woodlands (e.g. in the Middle East). Using criteria of flora, physiognomy, relief and climate, nine main forest‐steppe regions are identified and characterized. Forest‐steppes are not simple two‐phase systems, as they show a high level of habitat diversity, with forest and grassland patches of varying types and sizes, connected by a network of differently oriented edges. Species diversity and functional diversity may also be exceptionally high in forest‐steppes. Regarding conservation, we conclude that major knowledge gaps exist in determining priorities at the continental, regional, national and local levels, and in identifying clear target states and optimal management strategies. When combined with other threats, climate change may be particularly dangerous to forest‐steppe survival, possibly resulting in compositional changes, rearrangement of the landscape mosaic or even the latitudinal or altitudinal shift of forest‐steppes.

Although remote sensing technology has long been used in wetland inventory and monitoring, the accuracy and detail level of wetland maps derived with moderate resolution imagery and traditional techniques have been limited and often unsatisfactory. We explored and evaluated the utility of a newly launched high-resolution, eight-band satellite system (Worldview-2; WV2) for identifying and classifying freshwater deltaic wetland vegetation and aquatic habitats in the Selenga River Delta of Lake Baikal, Russia, using a hybrid approach and a novel application of Indicator Species Analysis (ISA). We achieved an overall classification accuracy of 86.5% (Kappa coefficient: 0.85) for 22 classes of aquatic and wetland habitats and found that additional metrics, such as the Normalized Difference Vegetation Index and image texture, were valuable for improving the overall classification accuracy and particularly for discriminating among certain habitat classes. Our analysis demonstrated that including WV2’s four spectral bands from parts of the spectrum less commonly used in remote sensing analyses, along with the more traditional bandwidths, contributed to the increase in the overall classification accuracy by ~4% overall, but with considerable increases in our ability to discriminate certain communities. The coastal band improved differentiating open water and aquatic (i.e., vegetated) habitats, and the yellow, red-edge, and near-infrared 2 bands improved discrimination among different vegetated aquatic and terrestrial habitats. The use of ISA provided statistical rigor in developing associations between spectral classes and field-based data. Our analyses demonstrated the utility of a hybrid approach and the benefit of additional bands and metrics in providing the first spatially explicit mapping of a large and heterogeneous wetland system.