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Highlights • Multi-seasonal optical imagery and ALS was compared for modelling forest attributes. • Optical and ALS intensity data convey distinct information about forest structure. • ALS data alone exhibited strong predictive power for stem density. • The synergy of both data sources led to more accurate model of BA and species mixture. • Sentinel-2 A imagery holds great potential for enhancing ALS-based forest inventories. Abstract Advanced forest resource inventory (FRI) information is of critical importance for sustainable forest management. FRIs are dependent on remote sensing data and processing methods, along with field calibration/validation to generate cost-effective options for modelling forest inventory and biophysical variables over large areas. The objective of this study was to examine the impact of combining multi-seasonal multispectral satellite imagery with airborne laser scanning (ALS) data for estimating basal area, species mixture and stem density for an uneven-aged tolerant hardwood forest in Ontario, Canada. Using random forest (RF) regression as a non-parametric diagnostic technique, three multispectral optical sensors (i.e., Landsat-5 TM, Sentinel-2 A and WorldView-2) were compared to examine the most cost-effective sensor configuration for modelling FRI variables. The contribution of spectral predictors derived from these optical sensors as well as ALS height and intensity metrics were evaluated using RF variable importance. As part of our variable selection framework, all predictor variables were grouped into relatively independent clusters using a hierarchical variable clustering technique, which revealed the distinctiveness between information contained in spectral predictors, height- and intensity-based metrics. This indicates that ALS intensity data carry unique information complementary to passive near-infrared data for forest characterization. ALS data alone did not result in accurate models for basal area and species mixture, but predictive accuracies were improved significantly with the addition of spectral predictors. Compared to single-date images, multi-seasonal imagery proved to be more accurate for modelling FRI variables, especially when combined with ALS data. Despite its limited spatial resolution, Sentinel-2 A was found to be the most cost-effective image source for enhancing ALS-based FRI models. Using variables identified by the variable selection procedure, best subsets regression outperformed the RF models developed for diagnostic analysis, resulting in a suite of accurate and parsimonious predictive models, with coefficients of determination of 0.73, 0.90 and 0.67, for basal area, species mixture, and stem density, respectively. [ABSTRACT FROM AUTHOR]

Arctic landscapes undergo seasonal and long-term changes as the active layer thaws and freezes, which can result in localized or irregular subsidence leading to the formation of thermokarst terrain. Differential Interferometric Synthetic Aperture Radar (DInSAR) is a technique capable of measuring ground surface displacements resulting from thawing permafrost at centimetre precision and is quickly gaining acceptance as a means of measuring ground displacement in permafrost regions. Using RADARSAT-2 stacked DInSAR data from 2013 and 2015 we determined the magnitude and patterns of land surface change in a continuous permafrost environment. At our study site situated in the Canadian High Arctic, DInSAR seasonal ground displacement patterns were consistent with field observations of permafrost degradation. As expected, many DInSAR values are close to the detection threshold (i.e., 1 cm) and therefore do not indicate significant change; however, DInSAR seasonal ground displacement patterns aligned well with climatological and soil conditions and offer geomorphological insight into subsurface processes in permafrost environments. While our dataset is limited to two years of data representing a three-year time period, the displacements derived from DInSAR provide insight into permafrost change in a High Arctic environment and demonstrate that DInSAR is an applicable tool for understanding environmental change in remote permafrost regions. [ABSTRACT FROM AUTHOR]

Mangrove forests grow in intertidal zones in tropical and subtropical regions and have suffered a dramatic decline globally over the past few decades. Remote sensing data, collected at various spatial resolutions, provide an effective way to map the spatial distribution of mangrove forests over time. However, the spectral signatures of mangrove forests are significantly affected by tide levels. Therefore, mangrove forests may not be accurately mapped with remote sensing data collected during a single-tidal event, especially if not acquired at low tide. This research reports how a decision-tree −based procedure was developed to map mangrove forests using multi-tidal Landsat 5 Thematic Mapper (TM) data and a Digital Elevation Model (DEM). Three indices, including the Normalized Difference Moisture Index (NDMI), the Normalized Difference Vegetation Index (NDVI) and NDVI L ·NDMI H (the multiplication of NDVI L by NDMI H , L: low tide level, H: high tide level) were used in this algorithm to differentiate mangrove forests from other land-cover and land-use types in Fangchenggang City, China. Additionally, the recent Landsat 8 OLI (Operational Land Imager) data were selected to validate the results and compare if the methodology is reliable. The results demonstrate that short-term multi-tidal remotely-sensed data better represent the unique nearshore coastal wetland habitats of mangrove forests than single-tidal data. Furthermore, multi-tidal remotely-sensed data has led to improved accuracies using two classification approaches: i.e. decision trees and the maximum likelihood classification (MLC). Since mangrove forests are typically found at low elevations, the inclusion of elevation data in the two classification procedures was tested. Given the decision-tree method does not assume strict data distribution parameters, it was able to optimize the application of multi-tidal and elevation data, resulting in higher classification accuracies of mangrove forests. When using multi-source data of differing types and distributions to map mangrove forests, a decision-tree method appears to be superior to traditional statistical classifiers. [ABSTRACT FROM AUTHOR]

In this study, digital images collected at a study site in the Canadian High Arctic were processed and classified to examine the spatial-temporal patterns of percent vegetation cover (PVC). To obtain the PVC of different plant functional groups (i.e., forbs, graminoids/sedges and mosses), field near infrared-green-blue (NGB) digital images were classified using an object-based image analysis (OBIA) approach. The PVC analyses comparing different vegetation types confirmed: (i) the polar semi-desert exhibited the lowest PVC with a large proportion of bare soil/rock cover; (ii) the mesic tundra cover consisted of approximately 60% mosses; and (iii) the wet sedge consisted almost exclusively of graminoids and sedges. As expected, the PVC and green normalized difference vegetation index (GNDVI; (R NIR − R Green )/(R NIR + R Green )), derived from field NGB digital images, increased during the summer growing season for each vegetation type: i.e., ∼5% (0.01) for polar semi-desert; ∼10% (0.04) for mesic tundra; and ∼12% (0.03) for wet sedge respectively. PVC derived from field images was found to be strongly correlated with WorldView-2 derived normalized difference spectral indices (NDSI; (R x − R y )/(R x + R y )), where R x is the reflectance of the red edge (724.1 nm) or near infrared (832.9 nm and 949.3 nm) bands; R y is the reflectance of the yellow (607.7 nm) or red (658.8 nm) bands with R 2 ’s ranging from 0.74 to 0.81. NDSIs that incorporated the yellow band (607.7 nm) performed slightly better than the NDSIs without, indicating that this band may be more useful for investigating Arctic vegetation that often includes large proportions of senescent vegetation throughout the growing season. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) data are often used to determine the physical properties of the soil surface, such as soil moisture and surface roughness. Although these analyses are commonly applied in agricultural environments, there has been limited application in more natural environments, particularly at high latitudes. For the research reported here, an artificial neural network (ANN) is developed to model surface roughness in the Canadian High Arctic. This research represents the first phase of the overall goal of developing an operational methodology for estimating surface roughness, vegetation cover and soil moisture using SAR and limited field measurements. Multiple incidence angle data and fully polarimetric data from RADARSAT-2 are combined with long and short profile in situ surface roughness measurements from 134 sample locations located across two distinct High Arctic study sites. Multiple ANN models were developed using various backscatter, textural, and polarimetric variables. The ANN models exhibited a moderate to strong agreement to field-measured surface roughness. This study demonstrates that operational surface roughness modeling in the Canadian High Arctic is feasible with RADARSAT-2 polarimetric data. [ABSTRACT FROM AUTHOR]