Abstract
Rising global temperatures tied to increases in greenhouse gas emissions are impacting high latitude regions, leading to changes in vegetation composition and feedbacks to climate through increased methane (CH4) emissions. In subarctic peatlands, permafrost collapse has led to shifts in vegetation species on landscape scales with high spatial heterogeneity. Our goal was to provide a baseline for vegetation distribution related to permafrost collapse and changes in biogeochemical processes. We collected unmanned aerial system (UAS) imagery at Stordalen Mire, Abisko, Sweden to classify vegetation cover types. A series of digital image processing routines were used to generate texture attributes within the image for the purpose of characterizing vegetative cover types. An artificial neural network (ANN) was developed to classify the image. The ANN used all texture variables and color bands (three spectral bands and six metrics) to generate a probability map for each of the eight cover classes. We used the highest probability for a class at each pixel to designate the cover type in the final map. Our overall misclassification rate was 32%, while omission and commission error by class ranged from 0% to 50%. We found that within our area of interest, cover classes most indicative of underlying permafrost (hummock and tall shrub) comprised 43.9% percent of the landscape. Our effort showed the capability of an ANN applied to UAS high-resolution imagery to develop a classification that focuses on vegetation types associated with permafrost status and therefore potentially changes in greenhouse gas exchange. We also used a method to examine the multiple probabilities representing cover class prediction at the pixel level to examine model confusion. UAS image collection can be inexpensive and a repeatable avenue to determine vegetation change at high latitudes, which can further be used to estimate and scale corresponding changes in CH4 emissions.
Highlights
Subarctic regions are experiencing warming trends that result in permafrost thaw and collapse, which leads to large changes in the vegetative landscape [1]
Because functional cover types have been linked to CH4 emissions in high latitudes, mapping vegetation using broad cover types that are related to CH4 emissions is useful for understanding landscape change and provides context and evidence for changes in fluxes related to climate change as well as ties to field observations [3,42,66]
We suggest that texture features are useful indicators of these classes and could be used singularly if only those cover classes were necessary to quantify in the landscape
Summary
Subarctic regions are experiencing warming trends that result in permafrost thaw and collapse, which leads to large changes in the vegetative landscape [1]. The collapse of permafrost in peatlands often results in a transition from dry palsa and shrub communities to partially thawed, Sphagnum-dominated bogs and fully thawed, sedge-dominated fens [2] These changes in vegetation composition can result in large increases in methane (CH4) emissions [3,4,5], driven by changes in peat chemistry that support increased CH4 production rates [6] as well as more efficient transport through sedges [7]. Changes in plant functional types and hydrology associated with thaw correspond with changes in microbial communities including a change in the dominant methanogenic production pathway, which results in a shift in the isotopic composition of CH4 emissions [8] This changing vegetative and hydrologic landscape causes thaw ponds and open water to provide additional anoxic conditions that further drives methane release [2]. Though the approach had success, even this method had limitations due to the inability to effectively capture at spatial scales less than one meter, when fine scale changes in topography drive vegetation composition [21]
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