Regional Assessment of Aspen Change and Spatial Variability on Decadal Time Scales

Sankey, Temuulen Tsagaan

doi:10.3390/rs1040896

Cited by 12 publications

(15 citation statements)

References 36 publications

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“…Some results have indicated positive and negative cover change within the same landscape (Kulakowski et al. ; Sankey ), lending further support to the concept of varying aspen functional types (Rogers et al. ).…”

Section: Introductionmentioning

confidence: 78%

Herbivory strains resilience in drought‐prone aspen landscapes of the western United States

Rogers

Mittanck²

2013

J Vegetation Science

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Question Aspen forests in the northern hemisphere provide richer biodiversity compared to surrounding vegetation types. In both North America and Europe, however, aspen stands are threatened by a variety of human impacts: clear felling, land development, water diversion, fire suppression and both wild and domestic ungulate herbivory. We conducted a landscape assessment of quaking aspen (Populus tremuloides) for the purpose of identifying key components of resilience. Specifically, we tested novel measures linking plant–animal interactions, compared crucial functional differences in aspen types and made restorative recommendations based on the outcome of these assessments. Location Book Cliffs region, eastern Utah and western Colorado, USA. Methods Seventy‐seven 1‐ha plots were sampled for forest structure, composition, regeneration and recruitment, landscape elements, browse level and herbivore use. Use was determined by counting the number of pellet groups from ungulate species at each sample location. We tested the efficacy of a visual stand condition rating system when compared to objective metrics. A series of non‐parametric analyses were used to compare functional aspen types and stand condition groups by key variables. Non‐metric multidimensional scaling (NMS) allowed us to explore all our data to find the most critical measures of aspen stand conditions for the purpose of better informing future aspen monitoring. Results Plots differed significantly in seral or stable aspen functional types, stand condition rating and browse species use. Ordination analysis revealed that regeneration level and herbivore use were the strongest objective indicators of aspen stand conditions, while stand condition rating proved a valuable subjective index of forest status. While ungulate herbivory of aspen is problematic internationally, our results show acute impacts where moderate slopes, relatively low water availability and intense browsing predominate. Conclusions Appropriate measures for aspen communities, informed by crucial functional divisions, allowed us to gain a clear understanding of conditions across this large landscape. Overall, aspen in our study landscape is highly vulnerable to collapse due to narrow physiographic and climate limitations and browse levels. Without herbivory reduction, future conservation in such areas will be strained and widespread system failure may occur.

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Section: Introductionmentioning

confidence: 78%

Herbivory strains resilience in drought‐prone aspen landscapes of the western United States

Rogers

Mittanck²

2013

J Vegetation Science

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“…The area of the city and its suburbs is approximately 350 square miles and, according to census data of the National Historical Geographic Information System (NHGIS), approximately 1,000,000 people used to live in this area in 2000. Landsat images are frequently used for land cover change detection (e.g., [14,15] For each of the two classifiers two classification maps were produced using the before and after Katrina Landsat imagery. Tables 1 to 4 show the error matrices for the four classified maps.…”

Section: Study Area and Datasetsmentioning

confidence: 99%

Implications of Classification of Methodological Decisions in Flooding Analysis from Hurricane Katrina

Khatami

Mountrakis

2012

Remote Sensing

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Recent climatic patterns indicate that extreme weather events will increase in frequency and magnitude. Remote sensing offers unique advantages for large-scale monitoring. In this research, Landsat 5 remotely sensed imagery was used to assess flooding caused by Hurricane Katrina, one of the worst natural disasters in the US over the past decades. The objective of our work is to assess whether decisions associated with the classification process, such as location of reference data and algorithm choice, affected flooding results and subsequent analysis using census data. Maximum Likelihood (ML) and Back Propagation Neural Network (NN) were the tested algorithms, the former reflecting a simple and popular classifier, and the latter an advanced but complex method. Flooding estimations were almost identical within the reference sample area, 124.4 km 2 for the ML classifier and 123.7 km 2 for the NN classifier. However, large discrepancies were found outside the reference sample area with the ML predicting 462.5 km 2 and the NN identifying 797.2 km 2 as flooded, almost twice the amount. Further investigation took place to evaluate the influence of the classification method to a social study, namely the racial characteristics of flooded areas. Using Census 2000 data, our study area was segmented in census tracts. Results indicated a strong positive correlation between concentration of African Americans and proportional residential flooding. Pairwise T-Tests also verified that flooding among different African American concentrations was statistically different. There were no significant differences between the ML and NN methods in the results interpretation, which is mostly attributed to the significant geographic overlap between reference sample area and the examined census tracts. This study suggests that emergency responders should exercise significant caution in their decision making when using classification products from undersampled geographic areas

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“…There is, however, no automated approach to combining the two bands. A user-defined approach is required to produce a final map of the target cover type Glenn, 2009a and2009b;Sankey, 2009). Furthermore, the two bands are cumbersome to use even in a regression-based approach.…”

Section: Comparison Of Spectral Unmixing Techniquesmentioning

confidence: 99%

Landsat-5 TM and Lidar Fusion for Sub-pixel Juniper Tree Cover Estimates in a Western Rangeland

Sankey¹,

Glenn²

2011

photogramm eng remote sensing

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Pinyon-juniper woodlands comprise the third most common land-cover type in the United States and have been documented to have drastically increased both in density and extent in recent decades. We explored Landsat-5 TM and Light Detection and Ranging (lidar) data, individually and fused together, for estimating sub-pixel juniper cover. Linear spectral unmixing (LSU), Constrained Energy Minimization (CEM), and Mixture Tuned Matched Filtering (MTMF) techniques were compared along with spectral-lidar fusion approaches. None of the Landsat-5 TM-derived estimates were significantly correlated with field-measured juniper cover (n = 100), while lidar-derived estimates were strongly correlated (R 2 = 0.74, p-value Ͻ0.001). Fusion of these estimates produced superior results to both classifications individually (R 2 = 0.80, p-value Ͻ0.001). The MTMF technique performed best, while a multiple regression-based fusion was the best approach to combining the two data sources. Future studies can use the best sub-pixel classification and fusion approach to quantify changes in associated ecosystem properties such as carbon.

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Regional Assessment of Aspen Change and Spatial Variability on Decadal Time Scales

Cited by 12 publications

References 36 publications

Herbivory strains resilience in drought‐prone aspen landscapes of the western United States

Herbivory strains resilience in drought‐prone aspen landscapes of the western United States

Implications of Classification of Methodological Decisions in Flooding Analysis from Hurricane Katrina

Landsat-5 TM and Lidar Fusion for Sub-pixel Juniper Tree Cover Estimates in a Western Rangeland

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