Comparison of Digital Image Processing Techniques for Classifying Arctic Tundra

Stine, Roy S.; Chaudhuri, Debasish; Ray, Peter M.; Pathak, Prasad; Hall-Brown, Mary

doi:10.2747/1548-1603.47.1.78

Cited by 9 publications

(5 citation statements)

References 22 publications

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“…To test the adequacy of the classification method utilizing grayscale imagery, the classification derived for the modern image of the spatially heterogeneous Barrow landscape was compared to classifications derived from the same satellite image using multiple spectral bands. The latter classification derived from the multispectral image has been shown to have a high level of accuracy compared to similar studies in the arctic (Muller et al 1999, Jorgenson et al 1994, Noyle 1999, Stine et al 2010, Chaudhuri 2008 with an overall user and producer accuracy of 74% and 88% respectively . When we compared the classification derived from the grayscale classification described above with the classification derived from the same but multispectral image, the grayscale classification had an overall accuracy of 98.58% and a Kappa coefficient of 0.97, suggesting it adequately represented the extant land cover of the landscape and that this is an acceptable method for classifying spatially heterogeneous tundra landscapes such as those in this study.…”

Section: Image Classificationmentioning

confidence: 77%

High spatial resolution decade-time scale land cover change at multiple locations in the Beringian Arctic (1948–2000s)

Lin

Johnson

Andresen

et al. 2012

Environ. Res. Lett.

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Analysis of time series imagery from satellite and aircraft platforms is useful for detecting land cover change at plot to regional scales. In this study, we created multi-temporal high spatial resolution land cover maps for seven locations in the Beringian Arctic and assessed the change in land cover over time. Land cover classifications were site specific and mostly aligned with a soil moisture gradient. Time series varied between 60 and 21 years. Four of the five landscapes studied in Alaska underwent an expansion of drier land cover classes while the two landscapes studies in Chukotka, Russia showed an expansion of wetter land cover types. While a range of land cover types was present across the landscapes studied, the extent of shrubs (in Chukotka) and open water (in Alaska) increased in all landscapes where these land cover types were present. The results support trends documented for regional change in NDVI (a measure of vegetation greenness and productivity) as well as a host of other long term, experimental and modeling studies. Using historic change trends for each land cover type at each landscape, we use a simple probabilistic vegetation model to establish hypotheses of future change trajectories for different land cover types at each of the landscapes investigated. This study is a contribution to the International Polar Year Back to the Future project (IPY-BTF).

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Section: Image Classificationmentioning

confidence: 77%

High spatial resolution decade-time scale land cover change at multiple locations in the Beringian Arctic (1948–2000s)

Lin

Johnson

Andresen

et al. 2012

Environ. Res. Lett.

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“…The extent of these misclassifications represented <1% of the study area, thus not compromising the use of the final map for identifying wildlife habitats and selecting monitoring sites. The confusion between shadowed areas and water is recurrent in classification studies performed at high resolution [7,48,142], as water shares similar spectral characteristics as shadows [142]. Even creating an additional class for shadow in the classification (instead of creating a shadow layer using a hillshade) did not solve the problem as there were several water areas classified as shadow and vice versa (results not shown).…”

Section: Classification Performancementioning

confidence: 99%

“…While some studies evaluate the final map visually to assess classification performance [1,7], others only rely on accuracy metrics [44,48]. We consider visual evaluation of the final classified map as a critical addition to accuracy metrics.…”

Section: Classification Validationmentioning

confidence: 99%

“…Supervised classification usually needs a sufficient number of reference points classified through extensive in situ surveys. The most accurate classifications of tundra vegetation were undertaken using supervised learning with ground-based plot surveys [46][47][48]. The Arctic land cover classes commonly include nonvegetated covers, such as snow, ice, human infrastructure, water, and bare ground (shaded areas are usually also included in nonvegetated covers), as well as vegetated covers that are stratified by community composition (e.g., [35,39,43,45,49]), plant functional types [6,20,40,50], and percent vegetation cover [51].…”

Section: Introductionmentioning

confidence: 99%

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Algorithms and Predictors for Land Cover Classification of Polar Deserts: A Case Study Highlighting Challenges and Recommendations for Future Applications

et al. 2023

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The use of remote sensing for developing land cover maps in the Arctic has grown considerably in the last two decades, especially for monitoring the effects of climate change. The main challenge is to link information extracted from satellite imagery to ground covers due to the fine-scale spatial heterogeneity of Arctic ecosystems. There is currently no commonly accepted methodological scheme for high-latitude land cover mapping, but the use of remote sensing in Arctic ecosystem mapping would benefit from a coordinated sharing of lessons learned and best practices. Here, we aimed to produce a highly accurate land cover map of the surroundings of the Canadian Forces Station Alert, a polar desert on the northeastern tip of Ellesmere Island (Nunavut, Canada) by testing different predictors and classifiers. To account for the effect of the bare soil background and water limitations that are omnipresent at these latitudes, we included as predictors soil-adjusted vegetation indices and several hydrological predictors related to waterbodies and snowbanks. We compared the results obtained from an ensemble classifier based on a majority voting algorithm to eight commonly used classifiers. The distance to the nearest snowbank and soil-adjusted indices were the top predictors allowing the discrimination of land cover classes in our study area. The overall accuracy of the classifiers ranged between 75 and 88%, with the ensemble classifier also yielding a high accuracy (85%) and producing less bias than the individual classifiers. Some challenges remained, such as shadows created by boulders and snow covered by soil material. We provide recommendations for further improving classification methodology in the High Arctic, which is important for the monitoring of Arctic ecosystems exposed to ongoing polar amplification.

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“…As field conditions described by Walker et al (1994), Snowbeds occur over gentle north facing slopes. The NDVI and spectral value ranges observed for Snowbed complexes in Stine et al (2010) were adopted and modified for this study. Using these criteria, rules were developed in the Knowledge Engineer available with ERDAS Imagine (Appendix B, Figure 5).…”

Section: Derivation Of Landscape Factorsmentioning

confidence: 99%

Using Geospatial Techniques to Analyze Landscape Factors Controlling Ionic Composition of Arctic Lakes, Toolik Lake Region, Alaska

Pathak

Whalen

2012

International Journal of Applied Geospatial Research

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The impacts of climate change on landscapes in arctic Alaska are evident in terms of permafrost melting, frequent thermokarst activity, and the occurrence of more broadleaf vegetation. These changes may alter natural biogeochemical cycles of ions along with major nutrients and affect ionic compositions of lakes, as they are connected with the landscapes. However, the nature of the connectivity between lakes and landscapes in this region is not yet explored. The authors propose that geospatial analysis of landscape properties along with observed lake ion concentrations will enable an understanding of the currently existing landscape controls over ion inputs into the lakes. For the watersheds of 41 lakes in the Arctic Foothills region of Alaska, spatial properties of natural vegetation communities expressed in terms of percentage, shape complexity, and patch density metrics were derived using satellite data. Regression analyses were performed for concentration of ions as well as conductivity in lake water where the spatial metrics along with lake physical properties, lake order, and glacial till age categories were used as predicting variables in the regression. Landscape metrics for major land covers i.e., Percentage of Moist Acidic Tundra (MAT) and Moist Non-acidic Tundra (MNT) were the major predicting variables for concentration of several ions.

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Comparison of Digital Image Processing Techniques for Classifying Arctic Tundra

Cited by 9 publications

References 22 publications

High spatial resolution decade-time scale land cover change at multiple locations in the Beringian Arctic (1948–2000s)

High spatial resolution decade-time scale land cover change at multiple locations in the Beringian Arctic (1948–2000s)

Algorithms and Predictors for Land Cover Classification of Polar Deserts: A Case Study Highlighting Challenges and Recommendations for Future Applications

Using Geospatial Techniques to Analyze Landscape Factors Controlling Ionic Composition of Arctic Lakes, Toolik Lake Region, Alaska

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