Integrating Traditional Ecological Knowledge and Remote Sensing for Monitoring Rangeland Dynamics in the Altai Mountain Region.

Integrating traditional ecological knowledge (TEK) with remote sensing capabilities to monitor rangeland dynamics could lead to more acceptable, efficient, and beneficial rangeland management schemes for stakeholders of grazing systems. We contrasted pastoralists' perception of summer pasture quality in the Altai Mountains of Central Asia with normalized difference vegetation index (NDVI) metrics obtained from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor. The spatial relationship between satellite-based assessment of the grassland quality and on-the-ground evaluation by local herders was first assessed for a single year using 49, 1 × 1 km grassland blocks sampled in July 2013. Herder-derived forage value was positively and strongly (63% of variance explained) related to satellite-derived NDVI values (MODIS 1 km monthly data, MOD13A3) as well as field estimates of % vegetation cover (62% explained) and to a lesser degree to vegetation height (28% explained). Herders' multi-year perception (i.e., recall ability) was also contrasted with satellite observations of their herding areas over the period of 2006-2016 during which NDVI temporal anomaly explained >11% of variance in estimates of pasture quality recalled. Few herders in Kazakhstan could recall pasture conditions, most herders in Russia and China could but inconsistently (4 and 7% variation explained, respectively), whereas most herders in Mongolia could recall pasture conditions in strong agreement with NDVI anomaly (30% variation explained), patterns reflecting herders' regional dependence on herding as a livelihood. Corroboration of herder-derived estimates and satellite-derived vegetation indices creates opportunity for re-expression of satellite data in map form as TEK-derived indices more compatible with herder perceptions.

Glacial lake inventory and lake outburst potential in Uzbekistan.

Climate change has been shown to increase the number of mountain lakes across various mountain ranges in the World. In Central Asia, and in particular on the territory of Uzbekistan, a detailed assessment of glacier lakes and their evolution over time is, however lacking. For this reason we created the first detailed inventory of mountain lakes of Uzbekistan based on recent (2002-2014) satellite observations using WorldView-2, SPOT5, and IKONOS imagery with a spatial resolution from 2 to 10m. This record was complemented with data from field studies of the last 50years. The previous data were mostly in the form of inventories of lakes, available in Soviet archives, and primarily included localized in-situ data. The inventory of mountain lakes presented here, by contrast, includes an overview of all lakes of the territory of Uzbekistan. Lakes were considered if they were located at altitudes above 1500m and if lakes had an area exceeding 100m2. As in other mountain regions of the World, the ongoing increase of air temperatures has led to an increase in lake number and area. Moreover, the frequency and overall number of lake outburst events have been on the rise as well. Therefore, we also present the first outburst assessment with an updated version of well-known approaches considering local climate features and event histories. As a result, out of the 242 lakes identified on the territory of Uzbekistan, 15% are considered prone to outburst, 10% of these lakes have been assigned low outburst potential and the remainder of the lakes have an average level of outburst potential. We conclude that the distribution of lakes by elevation shows a significant influence on lake area and hazard potential. No significant differences, by contrast, exist between the distribution of lake area, outburst potential, and lake location with respect to glaciers by regions.

Forest dynamics in the U.S. indicate disproportionate attrition in western forests, rural areas and public lands.

Forests are experiencing significant changes; studying geographic patterns in forests is critical in understanding the impact of forest dynamics to biodiversity, soil erosion, water chemistry and climate. Few studies have examined forest geographic pattern changes other than fragmentation; however, other spatial processes of forest dynamics are of equal importance. Here, we study forest attrition, the complete removal of forest patches, that can result in complete habitat loss, severe decline of population sizes and species richness, and shifts of local and regional environmental conditions. We aim to develop a simple yet insightful proximity-based spatial indicator capturing forest attrition that is independent of spatial scale and boundaries with worldwide application potential. Using this proximity indicator, we evaluate forest attrition across ecoregions, land ownership and urbanization stratifications across continental United States of America. Nationally, the total forest cover loss was approximately 90,400 km2, roughly the size of the state of Maine, constituting a decline of 2.96%. Examining the spatial arrangement of this change the average FAD was 3674m in 1992 and increased by 514m or 14.0% in 2001. Simulations of forest cover loss indicate only a 10m FAD increase suggesting that the observed FAD increase was more than an order of magnitude higher than expected. Furthermore, forest attrition is considerably higher in the western United States, in rural areas and in public lands. Our mathematical model (R2 = 0.93) supports estimation of attrition for a given forest cover. The FAD metric quantifies forest attrition across spatial scales and geographic boundaries and assesses unambiguously changes over time. The metric is applicable to any landscape and offers a new complementary insight on forest landscape patterns from local to global scales, improving future exploration of drivers and repercussions of forest cover changes and supporting more informative management of forest carbon, changing climate and species biodiversity.

Accuracy assessment of land cover/land use classifiers in dry and humid areas of Iran.

Land cover/land use (LCLU) maps are essential inputs for environmental analysis. Remote sensing provides an opportunity to construct LCLU maps of large geographic areas in a timely fashion. Knowing the most accurate classification method to produce LCLU maps based on site characteristics is necessary for the environment managers. The aim of this research is to examine the performance of various classification algorithms for LCLU mapping in dry and humid climates (from June to August). Testing is performed in three case studies from each of the two climates in Iran. The reference dataset of each image was randomly selected from the entire images and was randomly divided into training and validation set. Training sets included 400 pixels, and validation sets included 200 pixels of each LCLU. Results indicate that the support vector machine (SVM) and neural network methods can achieve higher overall accuracy (86.7 and 86.6%) than other examined algorithms, with a slight advantage for the SVM. Dry areas exhibit higher classification difficulty as man-made features often have overlapping spectral responses to soil. A further observation is that spatial segregation and lower mixture of LCLU classes can increase classification overall accuracy.

Sustainable development under population pressure: lessons from developed land consumption in the conterminous U.S.

Population growth will result in a significant anthropogenic environmental change worldwide through increases in developed land (DL) consumption. DL consumption is an important environmental and socioeconomic process affecting humans and ecosystems. Attention has been given to DL modeling inside highly populated cities. However, modeling DL consumption should expand to non-metropolitan areas where arguably the environmental consequences are more significant. Here, we study all counties within the conterminous U.S. and based on satellite-derived product (National Land Cover Dataset 2001) we calculate the associated DL for each county. By using county population data from the 2000 census we present a comparative study on DL consumption and we propose a model linking population with expected DL consumption. Results indicate distinct geographic patterns of comparatively low and high consuming counties moving from east to west. We also demonstrate that the relationship of DL consumption with population is mostly linear, altering the notion that expected population growth will have lower DL consumption if added in counties with larger population. Added DL consumption is independent of a county's starting population and only dependent on whether the county belongs to a Metropolitan Statistical Area (MSA). In the overlapping MSA and non-MSA population range there is also a constant DL efficiency gain of approximately 20 km2 for a given population for MSA counties which suggests that transitioning from rural to urban counties has significantly higher benefits in lower populations. In addition, we analyze the socioeconomic composition of counties with extremely high or low DL consumption. High DL consumption counties have statistically lower Black/African American population, higher poverty rate and lower income per capita than average in both NMSA and MSA counties. Our analysis offers a baseline to investigate further land consumption strategies in anticipation of growing population pressures.

Integrating local and global error statistics for multi-scale RBF network training: an assessment on remote sensing data.

BACKGROUND: This study discusses the theoretical underpinnings of a novel multi-scale radial basis function (MSRBF) neural network along with its application to classification and regression tasks in remote sensing. The novelty of the proposed MSRBF network relies on the integration of both local and global error statistics in the node selection process. METHODOLOGY AND PRINCIPAL FINDINGS: The method was tested on a binary classification task, detection of impervious surfaces using a Landsat satellite image, and a regression problem, simulation of waveform LiDAR data. In the classification scenario, results indicate that the MSRBF is superior to existing radial basis function and back propagation neural networks in terms of obtained classification accuracy and training-testing consistency, especially for smaller datasets. The latter is especially important as reference data acquisition is always an issue in remote sensing applications. In the regression case, MSRBF provided improved accuracy and consistency when contrasted with a multi kernel RBF network. CONCLUSION AND SIGNIFICANCE: Results highlight the potential of a novel training methodology that is not restricted to a specific algorithmic type, therefore significantly advancing machine learning algorithms for classification and regression tasks. The MSRBF is expected to find numerous applications within and outside the remote sensing field.

Spatial wildlife-vehicle collision models: a review of current work and its application to transportation mitigation projects.

In addition to posing a serious risk to motorist safety, vehicle collisions with wildlife are a significant threat for many species. Previous spatial modeling has concluded that wildlife-vehicle collisions (WVCs) exhibit clustering on roads, which is attributed to specific landscape and road-related factors. We reviewed twenty-four published manuscripts that used generalized linear models to statistically determine the influence that numerous explanatory predictors have on the location of WVCs. Our motivation was to summarize empirical WVC findings to facilitate application of this knowledge to planning, and design of mitigation strategies on roads. In addition, commonalities between studies were discussed and recommendations for future model design were made. We summarized the type and measurement of each significant predictor and whether they potentially increased or decreased the occurrence of collisions with ungulates, carnivores, small-medium vertebrates, birds, and amphibians and reptiles. WVCs commonly occurred when roads bisect favorable cover, foraging, or breeding habitat for specific species or groups of species. WVCs were generally highest on road sections with high traffic volumes, or low motorist visibility, and when roads cut through drainage movement corridors, or level terrain. Ungulates, birds, small-medium vertebrates, and carnivore collision locations were associated with road-side vegetation and other features such as salt pools. In several cases, results were spurious due to confounding and interacting predictors within the same model. For example, WVCs were less likely to occur when a road bisected steep slopes; however, steep slopes may be located along specific road-types and habitat that also influence the occurrence of WVCs. In conclusion, this review showed that much of the current literature has gleaned the obvious, broad-scale relationships between WVCs and predictors from available data sets, and localized studies can provide unique and novel results. Future research requires specific modeling for each target species on a road-by-road basis, and measuring the predictive power of model results within similar landscapes. In addition, research that builds on the current literature by investigating rare anomalies and interacting variables will assist in providing sound comprehensive guidelines for wildlife mitigation planning on roads.