Understanding the climate impacts on decadal vegetation change in northern Alaska

The Arctic is experiencing rapid climate change. This research documents tundra vegetation changes near Atqasuk and Utqiaġvik, Alaska. At each location, 30 plots were sampled annually from 2010 to 2019 using a point frame. For every encounter, we recorded the height and classified it into eight groupings (deciduous shrubs, evergreen shrubs, forbs, graminoids, bryophytes, lichens, litter and standing dead vegetation); for vascular plants we also identified the species. We found an increase in plant stature and cover over time consistent with regional warming. Graminoid cover and height increased at both sites, with a fivefold increase in cover in Atqasuk. At Atqasuk shrub and forb cover and height increased. Species diversity decreased at both sites. Year was generally the strongest predictor of vegetation change suggesting a cumulative change over time; however, soil moisture and soil temperature were also predictors of vegetation change. We anticipate plants in the region will continue to grow taller as the region warms, resulting in greater plant cover, especially graminoids and shrubs. The increase of plant cover and accumulation of litter may impact nonvascular plants negatively. Continued changes in community structure will impact energy balance and carbon cycling and may result in regional and global consequences.

Drought Variability over the Conterminous United States for the Past Century

We examine the drought variability over the Conterminous United States (CONUS) for 1915-2018 using the Noah-MP land-surface model. We examine different model options on drought reconstruction including optional representation of groundwater and dynamic vegetation phenology. Over our 104-year reconstruction period, we identify 12 great droughts that each covered at least 36% of CONUS and lasted for at least 5 months. The great droughts tend to have smaller areas when groundwater and/or dynamic vegetation are included in the model configuration. We detect a small decreasing trend in dry area coverage over CONUS in all configurations. We identify 45 major droughts in the baseline (with a dry area coverage greater than 23.6% of CONUS) that are, on average, somewhat less severe than great droughts. We find that representation of groundwater tends to increase drought duration for both great and major droughts, primarily by leading to earlier drought onset (some due to short-lived recovery from a previous drought) or later demise (groundwater anomalies lag precipitation anomalies). In contrast, representation of dynamic vegetation tends to shorten major droughts duration, primarily due to earlier drought demise ( closed stoma or dead vegetation reduces ET loss during droughts). On a regional basis, the U.S. Southwest (Southeast) has the longest (shortest) major drought durations. Consistent with earlier work, dry area coverage in all subregions except the Southwest has decreased. The effects of groundwater and dynamic vegetation vary regionally due to differences in groundwater depths (hence connectivity with the surface) and vegetation types.

Detecting drought effects on tree mortality in forests of Franconia (Germany)

<p>Central European forests face challenges with climate changing much faster than they can adapt. Extremely hot and dry summers like in 2018 deprive forests of soil moisture, leaving them with low ground water levels. While individuals with deep and well-established root systems survive, young individuals and shallow-rooted species perish.</p><p>In southern Germany, die-off of single trees or small groups got noticeable recently. Such effects of harsher conditions rarely occur over large areas, but more in a spotted, irregular manner. This makes the phenomenon difficult to detect and to estimate its extent. The share of trees lately deteriorated may be larger than expected and represent a considerable portion of forests. Therefore, we see the great need for monitoring. Remote sensing data is suitable to examine inaccessible areas at a large scale. To quantify mortality of individual trees among a majority of vital ones, sensor platforms and respective data have to fulfill certain criteria regarding spatial, temporal and spectral resolution. Dead trees can be distinguished from others due to discoloration and defoliation. This change in appearance affects the spectral response, even in pixels larger than the tree’s extent.</p><p>This study aims at recommending a suitable spatial scale for space-borne multispectral imagery products to achieve this task. We evaluate commercial and free remote sensing data products and their ability to estimate fractional cover of dead vegetation. Satellite data employed in this study comes from Landsat 8 (30 m), Sentinel-2 (10 m), RapidEye (6.5 m) and PlanetScope (3 m). Classification performance is tested against high-resolution multispectral aerial imagery (17 cm) acquired with a Micasense RedEdge-M camera.</p><p>High-resolution Micasense images are capable of detecting single dead trees, even after downgrading the resolution from 17 cm to 3 m. For all data products tested, fraction of dead trees per pixel did not differ significantly among land cover types (dead vegetation, vital vegetation, pavement, open soil). This indicates that individual dead trees may not be detectable in vital forest stands. The finding even seems to be valid for a resolution of 3 m (PlanetScope), which is identical to the downgraded Micasense data. In the near future the detection of this phenomenon might profit from technical developments towards even higher spatial detail of space-borne sensors. Alternatively, high resolution images from aerial campaigns, manned or unmanned, could bridge this gap when flight time and spatial coverage are increased significantly and facilitating policies are in place.</p>

Documentation of Acidic Mining Exploration Drill Cuttings at the Pebble Copper–Gold Mineral Prospect, Southwest Alaska

During exploration drilling of the Pebble copper–gold–molybdenum (Cu–Au–Mo) deposit, drilling wastes were disposed of directly on the landscape or passed through unlined sumps prior to disposal. The ore and host rock are rich in sulfides, which weather to sulfuric acid with consequent metal leaching. Oxidized cuttings were visually evident, and confirmed with laboratory and field testing to have a pH of 2.7–4.3. At these sites, Cu and Mo exceeded or were at the high end of the natural background. With one exception, Cu was in the range of 545 mg/kg to 4865 mg/kg. Dead vegetation was observed at all sites with drill cuttings on the surface. Dead vegetation was also observed on sump soil covers, unrelated to drilling waste. Sites where vegetation had not re-established were from four to thirteen years old. The potential impact to surface and groundwater was not determined. Understanding the source and extent of damage from cuttings could lead to better site management.

Synthesis of Vegetation Indices Using Genetic Programming for Soil Erosion Estimation

Vegetation Indices (VIs) represent a useful method for extracting vegetation information from satellite images. Erosion models like the Revised Universal Soil Loss Equation (RUSLE), employ VIs as an input to determine the RUSLE soil Cover factor (C). From the standpoint of soil conservation planning, the C factor is one of the most important RUSLE parameters because it measures the combined effect of all interrelated cover and management variables. Despite its importance, the results are generally incomplete because most indices recognize healthy or green vegetation, but not senescent, dry or dead vegetation, which can also be an important contributor to C. The aim of this research is to propose a novel approach for calculating new VIs that are better correlated with C, using field and satellite information. The approach followed by this research is to state the generation of new VIs in terms of a computer optimization problem and then applying a machine learning technique, named Genetic Programming (GP), which builds new indices by iteratively recombining a set of numerical operators and spectral channels until the best composite operator is found. Experimental results illustrate the efficiency and reliability of this approach to estimate the C factor and the erosion rates for two watersheds in Baja California, Mexico, and Zaragoza, Spain. The synthetic indices calculated using this methodology produce better approximation to the C factor from field data, when compared with state-of-the-art indices, like NDVI and EVI.

Predicting the occurrence of an endangered reptile based on habitat attributes

The endangered Blue Mountains water skink (Eulamprus leuraensis), a habitat specialist known from approximately 60 threatened highland peat swamps, is the sole endemic vertebrate of the Blue Mountains region, Australia. We quantified the species’ habitat associations by surveying 10 such swamps annually for three years. We scored habitat features and trapped skinks, comparing habitat attributes of trap sites where skinks were and were not captured. The distribution of E. leuraensis was non-random: skinks were found at sites with high values for some variables (soil moisture, live vegetation, surface water, understorey density and numbers of burrows) and low values for others (dead vegetation, logs, rocks, bare ground, canopy cover, sunlight penetration and numbers of invertebrates), and were mostly found in sites that were close to surface water and far from trees and logs. Eulamprus leuraensis is widely distributed within swamps, with weak associations between microhabitat variation and skink presence. Skink abundance and mean body size were highest within swamp centres, decreasing towards the margins; larger skinks were found closer to water, gravid female skinks were found at wetter sites and juveniles occupied marginal habitat. Skinks were rarely recaptured >10 m from their original site, with adult males travelling further than adult females and juveniles. We developed a quick field detection method for managers to assess the likely presence of E. leuraensis using two habitat attributes (soil moisture and burrow abundance). We mapped the species’ known and predicted habitat using GIS spatial layers, including locality records, associated vegetation communities and digital elevation models.

Anthropogenic or ecological trap: what is causing the population decline of the Lapwing Vanellus vanellus in Western Ukraine?

Ecological and anthropogenic traps exist and exert a negative effect on Lapwing populations. We believe that an anthropogenic trap is a partial or delayed manifestation of an ecological trap. In recent decades Lapwing communities have shown higher affiliation with urban landscapes, which negatively influences breeding success and the overall density of the species. It appears that the Lapwing has fallen into an anthropogenic trap, which in Ukraine is represented by agricultural landscapes. The decline in the Lapwing population is mainly caused by high intensity of agriculture, overgrazing, desolation of agricultural lands, changes in the water regime of rivers and lakes, global forestation, increasing disturbance by recreational activity and tourism, and an increase in the distribution and number of predatory mammals. Controlled burns of dead vegetation performed in late spring, household waste disposal, and construction work all contribute to the loss of breeding grounds. As a result the majority of local Lapwing populations declined during last decade, and some populations have gone completely extinct.