The political breakdown of the Soviet Union in 1991 provides a rare case of drastic changes in social and economic conditions, and as such a great opportunity to investigate the impacts of socioeconomic changes on the rates and patterns of forest harvest and regrowth. Our goal was to characterize forest-cover changes in the temperate zone of European Russia between 1985 and 2010 in 5-year increments using a strati?ed random sample of 12 Landsat footprints. We used Support Vector Machines and post-classi?cation comparison to monitor forest area, disturbance and reforestation. Where image availability was sub-optimal, we tested whether winter images help to improve classi?cation accuracy. Our approach yielded accurate mono-temporal maps (on average >95% overall accuracy), and change maps (on average 93.5%). The additional use of winter imagery improved classi?- cation accuracy by about 2%. Our results suggest that Russia's temperate forests underwent substantial changes during the observed period. Overall, forested areas increased by 4.5%, but the changes in forested area varied over time: a decline in forest area between 1990 and 1995 (?1%) was followed by an increase in overall forest area in recent years (+1.4%, 2005-2010), possibly caused in part by forest regrowth on abandoned farmlands. Disturbances varied greatly among administrative regions, suggesting that differences in socioeconomic conditions strongly in?uence disturbance rates. While portions of Russia's temperate forests experienced high disturbance rates, overall forest area is expanding. Our use of a strati?ed random sample of Landsat footprints, and of summer and winter images, allowed us to characterize forest dynamics across a large region over a long time period, emphasizing the value of winter imagery in the free Landsat archives, especially for study areas where data availability is limited.
File: Baumann-etal_2012_Using-the-landsat-record-to-detect-fcc-in-the-tempreate-zone-of-European-Russia_0.pdf
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Forest cover change is one of the most important land cover change processes globally, and old-growth forests continue to disappear despite many efforts to protect them. At the same time, many countries are on a trajectory of increasing forest cover, and secondary, plantation, and scrub forests are a growing proportion of global forest cover. Remote sensing is a crucial tool for understanding how forests change in response to forest protection strategies and economic development, but most forest monitoring with satellite imagery does not distinguish old-growth forest from other forest types. Our goal was to measure changes in forest types, and especially old-growth forests, in the biodiversity hotspot of northwest Yunnan in southwest China. Northwest Yunnan is one of the poorest regions in China, and since the 1990s, the Chinese government has legislated strong forest protection and fostered the growth of ecotourism-based economic development. We used Landsat TM/ETM+ and MSS images, Support Vector Machines, and a multi-temporal composite classi?cation technique to analyze change in forest types and the loss of old-growth forest in three distinct periods of forestry policy and ecotourism development from 1974 to 2009. Our analysis showed that logging rates decreased substantially from 1974 to 2009, and the proportion of forest cover increased from 62% in 1990 to 64% in 2009. However, clearing of high-diversity old-growth forest accelerated, from approximately 1100 hectares/year before the logging ban (1990 to 1999), to 1550 hectares/year after the logging ban (1999 to 2009). Paradoxically, old-growth forest clearing accelerated most rapidly where ecotourism was most prominent. Despite increasing overall forest cover, the proportion of old-growth forests declined from 26% in 1990, to 20% in 2009. The majority of forests cleared from 1974 to 1990 returned to either a nonforested land cover type (14%) or non-pine scrub forest (66%) in 2009, and our results suggest that most non-pine scrub forest was not on a successional trajectory towards high-diversity forest stands. That means that despite increasing forest cover, biodiversity likely continues to decline, a trend obscured by simple forest versus non-forest accounting. It also means that rapid development may pose inherent risks to biodiversity, since our study area arguably represents a best-case scenario for balancing development with maintenance of biodiversity, given strong forest protection policies and an emphasis on ecotourism development
File: brandt2012.pdf
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Conservation efforts should be based on habitat models that identify areas of high quality and that are built at spatial scales that are ecologically relevant. In this study, we developed habitat models for the Loggerhead Shrike (Lanius ludovicianus) in the Chihuahuan Desert of New Mexico to answer two questions: (1) are highly used habitats of high quality for shrikes in terms of individual fitness? and (2) what are the spatial scales of habitat associations relevant to this species? Our study area was Fort Bliss Army Reserve (New Mexico). Bird abundance was obtained from 10 min point counts conducted at forty-two 108 ha plots during a 3-year period. Measures of fitness were obtained by tracking a total of 73 nests over the 3 years. Habitat variables were measured at spatial scales ranging from broad to intermediate to local. We related habitat use and measures of fitness to habitat variables using Bayesian model averaging. We found a significant relationship between bird abundance and measures of fitness averaged across nesting birds in each plot (correlation up to 0.61). This suggests that measures of habitat use are indicative of habitat quality in the vicinity of Fort Bliss. Local- and intermediate-scale variables best explained shrike occurrence. Habitat variables were not related to any measures of fitness. A better understanding of the factors that limit individual bird fitness is therefore necessary to identify areas of high conservation value for this species.
File: StLouis-etAl-LandscapeEcology-2010.pdf
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The majority of landscape pattern studies are based on the patch-mosaic paradigm, in which habitat patches are the basic unit of the analysis. While many patch-based landscape indices successfully relate spatial patterns to ecological processes, it is also desirable to use finer grained analyses for understanding species presence, abundance, and movement patterns across the landscape and to describe spatial context by measuring habitat change across scales. Here, we introduce two multi-scale pixel-based approaches for spatial pattern analysis, which quantify the spatial context of each pixel in the landscape. Both approaches summarize the proportion of habitat at increasing window sizes around each pixel in a scalogram. In the first regressionbased approach, a third-order polynomial is fitted to the scalogram of each pixel, and the four polynomial coefficients are used as descriptors of spatial context of each pixel within the landscape mosaic. In the second shape-based approach, the scalogram mean and standard deviation, and the mean slope between forest cover at the smallest window size and each of the larger window sizes are calculated. The values emerging from these two approaches are assigned to each focal pixel and can be used as predictive variables, for example, in species presence and abundance studies. We tested the performance of these approaches on 18 random landscapes and nine actual landscapes with varying forest habitat cover. Results show that both methods were able to differentiate between several spatial contexts. We thus suggest that these approaches could serve as a complement or an alternative to existing methods for landscape pattern analysis and possibly add further insight into pattern-species relations.
File: BarMassada_LE_2010.pdf
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Fire is an important natural disturbance process in many ecosystems, but humans can irrevocably change natural fire regimes. Quantifying long-term change in fire regimes is important to understand the driving forces of changes in fire dynamics, and the implications of fire regime changes for ecosystem ecology. However, assessing fire regime changes is challenging, especially in grasslands because of high intra- and inter-annual variation of the vegetation and temporally sparse satellite data in many regions of the world. The breakdown of the Soviet Union in 1991 caused substantial socioeconomic changes and a decrease in grazing pressure in Russia's arid grasslands, but how this affected grassland fires is unknown. Our research goal was to assess annual burned area in the grasslands of southern Russia before and after the breakdown. Our study area covers 19,000 km2 in the Republic of Kalmykia in southern Russia in the arid grasslands of the Caspian plains. We estimated annual burned area from 1985 to 2007 by classifying AVHRR data using decision tree algorithm, and validated the results with RESURS, Landsat and MODIS data. Our results showed a substantial increase in burned area, from almost none in the 1980s to more than 20% of the total study area burned in both 2006 and 2007. Burned area started to increase around 1998 and has continued to increase, albeit with high fluctuations among years. We suggest that it took several years after livestock numbers decreased in the beginning of the 1990s for vegetation to recover, to build up enough fuel, and to reach a threshold of connectivity that could sustain large fires. Our burned area detection algorithm was effective, and captured burned areas even with incomplete annual AVHRR data. Validation results showed 68% producer's and 56% user's accuracy. Lack of frequent AVHRR data is a common problem and our burned area detection approach may also be suitable in other parts of the world with comparable ecosystems and similar AVHRR data limitations. In our case, AVHRR data were the only satellite imagery available far enough back in time to reveal marked increases in fire regimes in southern Russia before and after the breakdown of the Soviet Union.
File: Dubinin_etal_RSE_2010.pdf
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Light detection and ranging (LiDAR) is increasingly used to map terrain and vegetation. Data collection is expensive, but costs are reduced when multiple products are derived from each mission. We examined how well low-density leaf-off LiDAR, originally flown for terrain mapping, quantified hardwood forest structure. We measured tree density, dbh, basal area, mean tree height, Lorey's mean tree height, and sawtimber and pulpwood volume at 114 field plots. Using univariate and multivariate linear regression models, we related field data to LiDAR return heights. We compared models using all LiDAR returns and only first returns. First-return univariate models explained more variability than all-return models; however, the differences were small for multivariate models. Multiple regression models had R2 values of 65% for sawtimber and pulpwood volume, 63% for Lorey's mean tree height, 55% for mean tree height, 48% for mean dbh, 46% for basal area, and 13% for tree density. However, the standard error of the mean for predictions ranged between 1 and 4%, and this level of error is well within levels needed for broad-scale forest assessments. Our results suggest that low-density LiDAR intended for terrain mapping is valuable for broad-scale hardwood forest inventories.
File: Hawbaker_etal_Lidar_ForestScience_2010.pdf
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Measures of image texture derived from remotely sensed imagery have proven useful in many applications. However, when using multitemporal imagery or multiple images to cover a large study area, it is important to understand how image texture measures are affected by surface phenology. Our goal was to characterize the robustness to phenological variation of common first- and second-order texture measures of satellite imagery. Three North American study sites were chosen to represent different biomes. At each site, a suite of image textures were calculated for three to four dates across the growing season. Texture measures were compared among dates to quantify their stability, and the stability of measures was also compared between biomes. Interseasonal variability of texture measures was high overall indicating that care must be taken when using measures of texture at different phenological stages. Certain texture measures, such as first-order mean and entropy, as well as second-order homogeneity, entropy, and dissimilarity, were more robust to phenological change than other measures
File: Culbert_etal_IEEE_JSTARS_2010_0.pdf
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Eastern Europe provides unique opportunities to study changes in land use patterns, because much farmland became parcelized in the post-socialist period (i.e. large fields were broken up into smaller ones). Classification-based remote sensing approaches, however, do not capture such land cover modifications and new approaches based on continuous indicators are needed. Our goal is to develop a novel method to map farmland field size based on image texture.We fitted linear regression models to relate field size to Landsat-based image texture for a study area in the border region of Poland, Slovakia and Ukraine. Texture explained up to 93% of the variability in field size. Our field size map revealed marked differences among countries and these differences appear to be related to socialist land-ownership patterns and post-socialist land reform strategies. Image texture has great potential for mapping land use patterns and may contribute to a better understanding of land cover modifications in Eastern Europe and elsewhere.
File: Kuemmerle-etal_2009_JLUS_2.pdf
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Habitat transformations and climate change are among the most important drivers of biodiversity loss. Understanding the factors responsible for the unequal distribution of species richness is a major challenge in ecology. Using data from the North American Breeding Bird Survey to measure species richness and a change metric extracted from the MODerate resolution Imaging Spectroradiometer (MODIS), we examined the influence of energy variability on the geographic distribution of avian richness across the conterminous U.S. and in the different ecoregions, while controlling for energy availability. The analysis compared three groups of birds: all species, Neotropical migrants, and permanent residents. We found that interannual variability in available energy explained more than half of the observed variation in bird richness in some ecoregions. In particular, energy variability is an important factor in explaining the patterns of overall bird richness and of permanent residents, in addition to energy availability. Our results showed a decrease in species richness with increasing energy variability and decreasing energy availability, suggesting that more species are found in more stable and more productive environments. However, not all ecoregions followed this pattern. The exceptions might reflect other biological factors and environmental conditions. With more ecoclimatic variability predicted for the future, this study provides insight into how energy variability influences the geographical patterns of species richness.
File: Rowhani-Ecosystems-2008.pdf
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Biodiversity and habitat face increasing pressures due to human and natural influences that alter vegetation structure. Because of the inherent difficulty of measuring forested vegetation three-dimensional (3-D) structure on the ground, this important component of biodiversity and habitat has been, until recently, largely restricted to local measurements, or at larger scales to generalizations. New lidar and radar remote sensing instruments such as those proposed for spaceborne missions will provide the capability to fill this gap. This paper reviews the state of the art for incorporating information on vegetation 3-D structure into biodiversity and habitat science and management approaches, with emphasis on use of lidar and radar data. First we review relationships between vegetation 3-D structure, biodiversity and habitat, and metrics commonly used to describe those relationships. Next, we review the technical capabilities of new lidar and radar sensors and their application to biodiversity and habitat studies to date. We then define variables that have been identified as both useful and feasible to retrieve from spaceborne lidar and radar observations and provide their accuracy and precision requirements. We conclude with a brief discussion of implications for spaceborne missions and research programs. The possibility to derive vegetation 3-D measurements from spaceborne active sensors and to integrate them into science and management comes at a critical juncture for global biodiversity conservation and opens new possibilities for advanced scientific analysis of habitat and biodiversity.
File: Bergen_etal_JGR_2010_0.pdf
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