The following review is the second part of a two part series on the use of remotely sensed data for quantifying wetland extent and inferring or measuring condition for monitoring drivers of change on wetland environments. In the first part, we introduce policy makers and non-users of remotely sensed data with an effective feasibility guide on how data can be used. In the current review, we explore the more technical aspects of remotely sensed data processing and analysis using case studies within the literature. Here we describe: (a) current technologies used for wetland assessment and monitoring; (b) the latest algorithmic developments for wetland assessment; (c) new technologies; and (d) a framework for wetland sampling in support of remotely sensed data collection. Results illustrate that high or fine spatial resolution pixels (≤10 m) are critical for identifying wetland boundaries and extent, and wetland class, form and type, but are not required for all wetland sizes. Average accuracies can be up to 11% better (on average) than medium resolution (11–30 m) data pixels when compared with field validation. Wetland size is also a critical factor such that large wetlands may be almost as accurately classified using medium-resolution data (average = 76% accuracy, stdev = 21%). Decision-tree and machine learning algorithms provide the most accurate wetland classification methods currently available, however, these also require sampling of all permutations of variability. Hydroperiod accuracy, which is dependent on instantaneous water extent for single time period datasets does not vary greatly with pixel resolution when compared with field data (average = 87%, 86%) for high and medium resolution pixels, respectively. The results of this review provide users with a guideline for optimal use of remotely sensed data and suggested field methods for boreal and global wetland studies.

A study was undertaken to examine the spectral properties of trees infected by mountain pine beetle (Dendrotonus ponderosae, Hopkins) in central British Columbia. A monoculture pine forest infected with mountain pine beetle was flown in early autumn (September), with a repeat flight the following summer (July). High spatial/spectral resolution VNIR-SWIR and discrete LiDAR data were collected. Three states were recognized: healthy, previsual green attack, and red attack. Spectra from the September flight were sampled from positions derived from LiDAR-modeled treetops. Analysis focused primarily on 3 absorption features: pigment feature (685 nm), and 2 water absorption features (970 nm and 1200 nm). Derivative- and depth-normalized continuum removed (CR) spectra were generated for all 3 classes. Results obtained suggest that consistent differences in the shape of the pigment absorption feature are observed among the healthy, green, and red attack samples. The results of the analyses of the water features were less conclusive. The 970 nm feature did not show any separation, whereas the 1200 nm showed significant separation with the derivative and CR curves. These results indicate that, although subtle, reflectance differences between healthy and green attack crowns might provide a means for early detection of mountain pine beetle attack. Résumé. Une étude a été entreprise pour examiner les propriétés spectrales des arbres infectés par le dendroctone du pin ponderosa (Dendrotonus ponderosae, Hopkins) dans le centre de la Colombie-Britannique. Un vol a été effectué en début d’automne (septembre) avec un deuxième vol l’été suivant (juillet) au dessus d’une forêt de pins monospécifique infectée par le dendroctone du pin ponderosa. Des données VNIR-SWIR avec une haute résolution spatiale/spectrale et des données lidars discrètes ont été recueillies. Trois états ont été reconnus : sain, attaque verte prévisuelle et attaque rouge. Les spectres du vol de septembre ont été échantillonnés à partir de positions dérivées des cimes des arbres modélisées par lidar. L’analyse a été axée principalement sur 3 caractéristiques d’absorption : une caractéristique pigmentaire (685 nm) et 2 caractéristiques d’absorption de l’eau (970 nm et 1200 nm). Des spectres dérivés et des spectres à continuum enlevé «continuum removed» (CR) normalisés à la profondeur ont été générés pour les 3 classes. Les résultats obtenus suggèrent que des différences cohérentes dans la forme de la caractéristique d’absorption des pigments sont observées entre les échantillons sains, verts et attaques rouges. Les résultats de les analyses des caractéristiques de l’eau sont moins concluants. La caractéristique à 970 nm n’a pas montré de séparation, tandis que celle à 1200 nm a montré une séparation significative avec les courbes dérivées et CR. Ces résultats indiquent que, bien que subtiles, les différences de réflectance entre les cimes saines et les cimes avec des attaques vertes peuvent fournir un moyen pour la détection précoce du dendroctone du pin ponderosa.

Wetlands have and continue to undergo rapid environmental and anthropogenic modification and change to their extent, condition, and therefore, ecosystem services. In this first part of a two-part review, we provide decision-makers with an overview on the use of remote sensing technologies for the ‘wise use of wetlands’, following Ramsar Convention protocols. The objectives of this review are to provide: (1) a synthesis of the history of remote sensing of wetlands, (2) a feasibility study to quantify the accuracy of remotely sensed data products when compared with field data based on 286 comparisons found in the literature from 209 articles, (3) recommendations for best approaches based on case studies, and (4) a decision tree to assist users and policymakers at numerous governmental levels and industrial agencies to identify optimal remote sensing approaches based on needs, feasibility, and cost. We argue that in order for remote sensing approaches to be adopted by wetland scientists, land-use managers, and policymakers, there is a need for greater understanding of the use of remote sensing for wetland inventory, condition, and underlying processes at scales relevant for management and policy decisions. The literature review focuses on boreal wetlands primarily from a Canadian perspective, but the results are broadly applicable to policymakers and wetland scientists globally, providing knowledge on how to best incorporate remotely sensed data into their monitoring and measurement procedures. This is the first review quantifying the accuracy and feasibility of remotely sensed data and data combinations needed for monitoring and assessment. These include, baseline classification for wetland inventory, monitoring through time, and prediction of ecosystem processes from individual wetlands to a national scale.

A project was constructed to integrate remotely sensed data from multiple sensors and platforms to characterize range of ecosystem characteristics in the Peace–Athabasca Delta in Northern Alberta, Canada. The objective of this project was to provide a framework for the processing of multisensor data to extract ecosystem information describing complex deltaic wetland environments. The data used in this study was based on a passive satellite-based earth observation multispectral sensor (Sentinel-2) and airborne discrete light detection and ranging (LiDAR). The data processing strategy adopted here allowed us to employ a data mining approach to grouping of the input variables into ecologically meaningful clusters. Using this approach, we described not only the reflective characteristics of the cover, but also ascribe vertical and horizontal structure, thereby differentiating spectrally similar, but ecologically distinct, ground features. This methodology provides a framework for assessing the impact of ecosystems on radiance, as measured by Earth observing systems, where it forms the basis for sampling and analysis. This final point will be the focus of future work.

This study examines the response of a cold-regions deltaic wetland ecosystem in northwestern Canada to two separate and differing seasonal wetting cycles. The goal of this paper was to examine the nature of reflected electromagnetic energy measured by earth observation (EO) satellites, and to assess whether seasonal wetland hydroperiod and episodic flooding events impact the information retrieved by the Sentinel-2 sensors. The year 2018 represents a year characterized by a large spring freshet and ice-jam flooding, while 2019 represents a year characterized more by summer open-water flooding. We applied the Modified Normalized Difference Wetness Index (MNDWI) to address the effects of the wetting cycles. The response of the vegetative cover was tracked using the fraction of the absorbed photosynthetically active radiation (fAPAR) and the Leaf Area Index (LAI). All three indices were viewed through the lens of cover classes as derived through a previously published study by the authors. The study provides a framework for designing longer-term studies where multiple intra- and inter-annual hydrological cycles can be accessed via EO data. Future studies will enable the examination of lag times inherent in the response to the various water sources applied to spectral response and incorporate this EO approach into a monitoring framework.