Knowledge of the spatial distribution of vegetation cover and phytomass in the High Arctic is becoming increasingly important due to the changing climate of this region. Vegetation is very limited in arctic environments, especially in the High Arctic bioclimatic zones [1
]. The limited vegetation that is present, however, can have significant effects on the terrestrial carbon balance [3
] and methane fluxes [5
]. Further, knowledge of vegetation density is important for monitoring forage quality and quantity for ungulates such as Peary Caribou (listed as Endangered in the Canadian Species At Risk Act) and muskoxen [6
In addition to the obvious limitations of air and soil temperature, there are a number of factors that serve as controls on vegetation growth in the high arctic, including soil moisture [8
], available nutrients [10
], topography [12
], microtopography [8
], and soil type [14
]. The variation and distribution of these different environmental controls results in a very heterogeneous vegetation cover, with very different vegetation ecosystems sometimes in close proximity to one other. Remote sensing is therefore the best tool available to accurately map the spatial distribution of above-ground phytomass at the fine scales necessary to distinguish between these vegetation community types. This fine-scale mapping is crucial for accurate carbon budgets and phytomass estimation at the local scale [15
], and also for accurately scaling up these variables to larger regional scales [16
]. Vegetation cover can also complicate the retrieval of other important biophysical parameters, such as soil moisture, when using Synthetic Aperture Radar (SAR) [18
]; however, if vegetation cover is known, soil moisture retrieval accuracy can be increased [19
SAR has been used in vegetation/biomass studies at sub-arctic latitudes [21
], but is very rarely used to study arctic vegetation [23
], and has not been used at all in the context of the High Arctic. There are considerable benefits to using SAR when compared to optical data for arctic research. Acquiring cloud-free optical images is often problematic in the arctic, and low solar zenith angles at high latitudes can also cause illumination issues with optical data [19
]. There are also challenges with using SAR to model vegetation in the High Arctic, however, such as the extremely limited amounts of phytomass in all but the wettest areas. Even so, there are a number of approaches to modeling backscatter from vegetation canopies
(see review in [19
] and [24
]). These approaches, however, only apply when the vegetation is dense enough to form some sort of canopy, i.e.
, the above-ground phytomass must be of a sufficient height to have a noticeable effect on backscatter, which may not hold true in many parts of the arctic. Agricultural studies have demonstrated that even relatively short vegetation can produce an appreciable amount of HV backscatter due to depolarization [21
] of the backscatter, as well as marked differences in HH and VV polarizations [22
], though high arctic vegetation levels are generally even lower than short-crop agriculture. Low density grasses and sedges, which make up much of the vegetation cover in the high arctic, can also be difficult to distinguish from bare ground [26
]. Regardless, polarimetric data could be key to this analysis if this depolarization holds true for very low levels of vegetation. Multiple incidence angle data may be important for similar reasons, with greater incidence angle backscatter having greater interaction (and therefore backscatter) with short vegetation than smaller incidence angle data [21
] (similar to how surface roughness affects backscatter), though stopping short of full volumetric scatter.
The Radar Vegetation Index (RVI), which has been used to estimate biomass from SAR data [20
] characterizes vegetation scattering by dividing the cross-polarization scattering by the total scattering; effectively measuring the degree of depolarization. RVI may not be applicable if there is little to no volume scattering present, as would be the case below a certain threshold level of vegetation indicated by a lower HV backscatter contribution. If the vegetation cover is sparse or very dry, the surface roughness may be the most significant contributor to total backscatter [29
Vegetation can be modeled using not only relations of SAR to above-ground phytomass (as described above), but also using other controlling factors as input. As mentioned previously, vegetation in the high arctic is closely related to topographic and moisture gradients across the landscape, so variables derived from a Digital Elevation Model (DEM) have the potential to be useful for vegetation modeling. Vegetation biomass can be a good proxy for soil moisture in the arctic, due to the vegetation being dependent on the spatial distribution of soil moisture (i.e.
, it only grows in areas with consistently high soil moisture, with drier areas being completely barren) [31
]. Once vegetation is modeled, it can therefore be used to help model soil moisture using SAR over the same areas [19
], or vice versa. The inclusion of multiple data types from different sources into the model suggests that the use of Artificial Neural Networks (ANNs) would be a practical way to model the vegetation.
ANNs are commonly used to invert surface parameters from SAR data [33
], and show great promise in both simplifying the modeling process and increasing the accuracy of the results. ANNs have the capacity to “learn” complex, nonlinear patterns, and generalize these patterns in noisy environments. This capacity to generalize means that ANNs can be effective in situ
ations where data may be missing or imprecise. ANNs are also able to incorporate prior knowledge and physical constraints into the analysis, while making no assumptions about the statistical nature of the input data [35
]. This allows for the incorporation of disparate data from many remote sensing and ancillary sources, and can include variables such as terrain height, slope, aspect, soil texture and land cover. ANNs are superior at generalizing (or extending) results for application to new areas than a strictly empirical model, and do not have the same parameterization problems and assumption difficulties as physical models. The target variable for the ANN model needs to be a spatially explicit measure of vegetation phytomass, so a vegetation index from high resolution multi-spectral data is ideal.
Raynolds et al.
] established that maximum annual Normalized Difference Vegetation Index (NDVI) values from coarse spatial resolution imagery are closely related to peak summer above-ground phytomass across a range of sites in the arctic along a latitudinal gradient. Previous research at the Cape Bounty study location used in this analysis, using high spatial resolution imagery to generate NDVI, demonstrates a weaker relationship [9
], possibly due to the differences in the range of phytomass levels sampled (i.e.
, Raynolds sampled across five bioclimatic zones), spatial resolutions, or vegetation characteristics unique to Cape Bounty. Other vegetation variables were more closely correlated to high spatial resolution NDVI, such as Percent Vegetation Cover (PVC) [9
], vegetation volume [38
], and Leaf Area Index [4
]. A large proportion of the phytomass is made up of bryophytes in the high arctic [37
], with Cape Bounty being no exception, and this is thought to be a confounding factor in the relationships of NDVI to these other vegetation variables, due to the differing NDVI reflectance characteristics of bryophytes and vascular plants [40
]. It is not clear as to the effects of bryophytes on SAR backscatter in this environment, although their ability to absorb and retain moisture likely influences the dielectric properties at the surface. Watanabe et al.
], in a study using L-band SAR, discovered that moss layers in permafrost environments have a significant impact on co-pol backscattering (though not cross-pol backscatter), so the effects on C-band SAR, as used in this study, are likely to be noticeable, especially if the bryophytes are holding water.
Previous studies in the High Arctic have noted the fine-scale topographic and moisture controls of the ice-wedge polygon and frost crack dominated landscape [8
]. Vegetation in polar semi-desert areas is often limited to the margins of the polygons, where wind speed is reduced and sufficient moisture and nutrients are present to allow growth; these features therefore leave large patches of bare ground. This high proportion of bare ground can dominate the NDVI signal, causing an underestimation of above-ground phytomass for the area. Previous research has suggested that using the Soil Adjusted Vegetation Index (SAVI) [44
], which takes into account larger proportions of bare ground in the signal, could ameliorate the NDVI underestimation of above-ground phytomass [8
]. Optical data will therefore be used to generate SAVI values across the study area to be used as a baseline for the vegetation modeling.
The purposes of this research are two-fold: (i) to determine the effects that high arctic vegetation has on SAR backscatter, including polarimetric effects; and (ii) to model vegetation phytomass using an Artificial Neural Network (ANN). The results of the ANN will then be related to above-ground phytomass levels. Due to the low levels of vegetation phytomass in the study area, it is not expected that the vegetation itself will have strong interactions with the SAR signals, but it is likely that changes in soil moisture and surface roughness that would be strongly associated with areas of higher phytomass could be detected more easily.
High resolution optical data were used to facilitate the modeling of above-ground phytomass using RADARSAT-2 Synthetic Aperture Radar (SAR) data. Three different SAR parameters, along with a topographic position index derived from a high-resolution digital elevation model, were used to create Artificial Neural Network (ANN) models that estimated values of the Soil Adjusted Vegetation Index (SAVI) across different sets of image objects. Models for individual ecological classes were found to outperform a single model that included all classes (r2 = 0.49, normalized root mean square error (N_RMSE) = 9%) when the output from the separate models were combined and compared to the optical-derived SAVI values (r2 = 0.60, N_RMSE = 8%). The models were applied to larger image objects, with acceptable results (r2 = 0.72, N_RMSE = 6%), showing the potential of the models to be applied at multiple spatial scales without sacrificing accuracy. The output of the ANNs was also used to create another model that estimates above-ground phytomass across the landscape, and resulted in a strong relationship with ground-sampled phytomass values (r2 = 0.87, N_RMSE = 11%). This relationship demonstrates the utility of SAR data, compared to using optical data alone, when attempting to model above-ground phytomass in a high arctic environment with relatively low levels of vegetation. The phytomass levels in this environment are low enough that much of the relationship between field-measured phytomass and the SAR variables could be due to backscatter sensitivity to soil moisture, and, to a smaller degree, surface roughness. However, the close coupling of moisture and phytomass in this environment means that this relationship increases rather than decreases the effectiveness of the modeling effort.
Polarimetric variables were not found to be correlated to SAVI, and were therefore not used as inputs to the ANN models. This lack of correlation is not surprising, as the RADARSAT-2 dataset was initially acquired for soil moisture estimation, where steep incidence angles are preferred to reduce the surface roughness dependency and vegetation interaction. In the near future, it would be worthwhile to acquire polarimetric SAR data at shallower incidence angles to maximize vegetation interaction and determine the potential degree of polarimetric C-band SAR correlation to high arctic vegetation. The use of shorter wavelength SAR sensors, such as TerraSAR-X, would also likely increase the effective influence of vegetation on backscatter, due to the very low levels of vegetation in high arctic environments, and is an avenue worth further exploration.