Azad Henareh Khalyani

Grass Trumps Trees with Fire

Feedbacks involving rainfall, fire, and vegetation govern transitions between forests, savannas, and grasslands. Ecologists have long assumed that forests, savannas, and grasslands change gradually over space and time, with tree cover responding linearly to gradients in precipitation, aridity, fire disturbance, and grazing pressure. However, a growing body of evidence suggests that these biomes are self-reinforcing and that transitions between them can be nonlinear, governed by feedbacks at local and regional scales (1–3). Two reports in this issue, by Staver et al. on page 230 (4) and by Hirota et al. on page 232 (5), find evidence for these feedbacks and transitions at the global scale. These results suggest that global climate change will be substantially influenced by nonlinear behaviors and feedbacks between biophysical and human systems.

Classification of Landsat images based on spectral and topographic variables for land-cover change detection in Zagros forests

Detection of land-cover changes through time can be complicated because of sensor-specific differences in spatial and spectral resolutions; classified land-cover changes can be due to either real changes on the ground or a switch in sensors used to collect data. This study focused on two objectives: (1) selecting the best predictor variables for the classification of semi-arid Zagros forests given the characteristics of the study area and available data sets and (2) evaluating the application of the random forest (RF) algorithm as a unified technique for the classification of data sets acquired from different sensors. Three images of the same study area were acquired from the Landsat-5 Thematic Mapper (TM) sensor in 2009, the Landsat-7 Enhanced Thematic Mapper (ETM+) sensor with Scan Line Corrector (SLC) in 1999 and the Landsat-2 Multispectral Scanner (MSS) sensor in 1975. Following image preprocessing, the RF algorithm was applied for variable selection and classification. A test of equivalence was used to compare the overall accuracy of the classified maps from the three sensors. Slope, normalized difference vegetation index (NDVI) and elevation were determined to be the most important predictor variables for all three images. High overall classification accuracies were achieved for all three images (97.90% for MSS, 95.43% for TM and 95.29% for ETM). The ETM- and TM-derived maps had equivalent overall accuracy and even significantly higher overall accuracy was obtained for the MSS-derived map. The post-classification comparison showed an increase in agriculture and a decrease in forest cover. The selected predictor variables were consistent with ecological reality and showed more details on the changes of the land-cover classes across biophysical variables of the study area through time.

Mapping Tree Canopy Cover in Support of Proactive Prairie Grouse Conservation in Western North America

ABSTRACT Invasivewoody plant expansion is a primary threat driving fragmentation and loss of sagebrush (Artemisia spp.) and prairie habitats across the central andwestern United States. Expansion of native woody plants, including conifer (primarily Juniperus spp.) and mesquite (Prosopis spp.), over the past century is primarily attributable to wildfire suppression, historic periods of intensive livestock grazing, and changes in climate. To guide successful conservation programs aimed at reducing top-down stressors, we mapped invasive woody plants at regional scales to evaluate landscape level impacts, target restoration actions, and monitor restoration outcomes. Our overarching goal was to produce seamless regional products across sociopolitical boundaries with resolution fine enough to depict the spatial extent and degree of woody plant invasion relevant to greater sage-grouse (Centrocercus urophasianus) and lesser prairie-chicken (Tympanuchus pallidicinctus) conservation efforts. We mapped tree canopy cover at 1-m spatial resolution across an 11-state region (508 265 km2). Greater than 90% of occupied lesser prairie-chicken habitat was largely treeless for conifers (<1% canopy cover), whereas > 67% was treeless for mesquite. Conifers in the higher canopy cover classes (16–50% and >50% canopy cover) were scarce (<2% and 1% canopy cover), as was mesquite (<5% and 1% canopy cover). Occupied habitat by sagegrouse was more variable but also had a relatively large proportion of treeless areas ( =71, SE=5%). Lowto moderate levels of conifer cover (1–20%) were fewer ( = 23, SE = 5%) as were areas in the highest cover class (>50%; = 6, SE = 2%). Mapping indicated that a high proportion of invading woody plants are at a low to intermediate level. Canopy cover maps for conifer and mesquite resulting from this study provide the first and most geographically complete, high-resolution assessment of woody plant cover as a top-down threat to western sage-steppe and prairie ecosystems.

Deforestation and landscape structure changes related to socioeconomic dynamics and climate change in Zagros forests

The Zagros region of western Iran has been affected by the recent changes both in amount and in structure of forest cover. We evaluated the influence of several driving forces on forest cover and structure, including socioeconomic (urban and rural population and rural income) and climatic (mean annual rainfall and mean annual temperature) variables. We acquired all time series Landsat images of a study site from 1972 to 2009. The images were classified to produce a land cover map of each year. We calculated landscape and patch metrics and identified independent sets of metrics to describe forest change. The most influential driving force for forest loss was urban population and climatic variables to some extent. These challenges should be addressed at broader scales than those associated with the livelihoods and traditional management of local communities. Rural population had a significant association with landscape structure which should be considered in designing restoration projects.

Fire weather and likelihood: characterizing climate space for fire occurrence and extent in Puerto Rico

Assessing the relationships between weather patterns and the likelihood of fire occurrence in the Caribbean has not been as central to climate change research as in temperate regions, due in part to the smaller extent of individual fires. However, the cumulative effect of small frequent fires can shape large landscapes, and fire-prone ecosystems are abundant in the tropics. Climate change has the potential to greatly expand fire-prone areas to moist and wet tropical forests and grasslands that have been traditionally less fire-prone, and to extend and create more temporal variability in fire seasons. We built a machine learning random forest classifier to analyze the relationship between climatic, socio-economic, and fire history data with fire occurrence and extent for the years 2003–2011 in Puerto Rico, nearly 35,000 fires. Using classifiers based on climate measurements alone, we found that the climate space is a reliable associate, if not a predictor, of fire occurrence and extent in this environment. We found a strong relationship between occurrence and a change from average weather conditions, and between extent and severity of weather conditions. The probability that the random forest classifiers will rank a positive example higher than a negative example is 0.8–0.89 in the classifiers for deciding if a fire occurs, and 0.64–0.69 in the classifiers for deciding if the fire is greater than 5 ha. Future climate projections of extreme seasons indicate increased potential for fire occurrence with larger extents.

Water Flows Toward Power: Socioecological Degradation of Lake Urmia, Iran

Water is an invaluable resource, and equitable access to it is a fundamental human right. Disenfranchised groups often lose access to water resources because their interests are not well represented by decision makers. Excluding these groups from resource management policy often results in myopic decisions that contribute to further ecosystem damage. We describe the ecological degradation of Lake Urmia in Iran, which has recently experienced increased salinity and declining water quantity. The lake is a UNESCO Biosphere Reserve and Ramsar site, and supports unique biodiversity in the region. The lakes decline is driven by the destruction of Zagros forests and the governments water policies, which diverted water to more politically connected agricultural land users, increasing social inequity and prompting more deforestation. The most straightforward restoration solution is to discontinue the diversions and allow critical inflows to recharge Lake Urmia, preserving the lake and wetlands for migratory birds, tourists, and local communities.