Search Results (17)

The hydrological series in the Loess Plateau region has exhibited shifts in trend, mean, and/or variance as the environmental conditions have changed, indicating a departure from the assumption of stationarity. As the variations accumulate, the compound effects caused by the driving variables on runoff variations grow complex and interactive, posing a substantial risk to water security and the promotion of high-quality development in regions or river basins. This study focuses on the Tuwei River Basin in the Loess Plateau, which experiences significant changes in vegetation coverage and minimal human disturbance, and examines the cross-driving relationship between the runoff change and its driving variables (including hydrometeorological and environmental variables). A quantitative statistical analysis method based on the GAMLSS is then developed to estimate the interacting effects of changes in the driving variables and their contribution to runoff changes. Finally, various anticipated scenarios are used to simulate the changes in driving variables and runoff disturbances. The findings indicate the following: (1) The developed GU, LO, and NO distribution-based GAMLSSs provide a notable advantage in effectively capturing the variations in groundwater storage variables, actual evapotranspiration, and underlying surface parameters, as well as accurately estimating the impacts of other relevant variables. (2) The precipitation and groundwater storage variables showed predominantly positive contributions to the runoff change, but actual evapotranspiration had an adverse effect. The changes in underlying surface parameters, particularly since 2000, increase actual evapotranspiration, while decreasing groundwater storage, resulting in a progressive decrease in runoff as their contribution grows. (3) The scenario simulation results reveal that alterations to the underlying surface have a substantial influence on the evolution of runoff in the Tuwei River Basin. Additionally, there are cross-effects between the impact of various driving variables on runoff, potentially compounding the complexity of inconsistent changes in runoff sequences. Full article

Soil erosion is a complex process involving material detachment, transportation (mainly by water, occasionally by wind), and eventual deposition when energy wanes. Human activities like tillage and construction can exacerbate soil erosion. Various forms of water erosion, such as sheet erosion, pedestal erosion, rills, piping, and gullying, are recognized. This pioneering study aims to comprehensively model water erosion across Taiwan at a 20 m spatial resolution, a departure from previous research focusing on smaller scales. Using the Revised Universal Soil Loss Equation (RUSLE) model, it seeks to examine the significant issue of soil erosion in Taiwan beyond agricultural areas and enable cross-regional comparisons. A large number of stations and the most recent data were used to establish the distributions of the rainfall runoff erosivity factor and the soil erodibility factor. In addition, we used the Google Earth Engine (GEE) to calculate the Normalized Difference Vegetation Index (NDVI) and a locally derived empirical equation to compute the cover-management factor. The topographic factor was determined using the System for Automated Geoscientific Analyses (SAGA). The support practice factor was analyzed using two different methods using datasets on World Urban Areas and Global Urban Boundaries from the literature. The analysis showed that despite the difference in the support practice factor, Taiwan’s resulting average yearly soil erosion rates are very similar (200.7 Mg ha⁻¹ year⁻¹ and 207.4 Mg ha⁻¹ year⁻¹). The amounts were validated against five watersheds that were instrumented with erosion pins. With prediction ratios ranging from 1.04 to 1.82 across four of the five watersheds, our findings provide empirical support for the alignment of our model with soil erosion pin measurements, especially within the Tsengwen reservoir watershed. However, it is noteworthy that these results also exhibit a tendency towards conservative estimations in the remaining watersheds. Our calculated estimate, falling within the range of 201–207 Mg ha⁻¹ year⁻¹, plausibly represents the upper limit of mean soil erosion in Taiwan. This assertion is predicated on the deliberate omission of local soil conservation measures from our study, a decision necessitated by the absence of comprehensive and detailed island-wide data. Despite this limitation, our results instill confidence in the robustness of our methodological approach, thereby suggesting that our estimation of soil erosion in Taiwan provides a reliable approximation. Full article

Taking as its point of departure the place of the vegetal realm within Jane Campion’s filmmaking, this article attends to both living and artificial plants, homing in on the exquisitely crafted paper flowers of The Power of the Dog to explore their entanglement with human power relations. Manmade flowers are clearly distinct from the flowers of the garden or the prairie, but in this Western, they form part of a broader floral aesthetic with their living kin. Drawing upon thought that stems from actual plants (Deleuze and Guattari’s arboreal-rhizomatic thinking) and vegetal philosophy (Marder, Coccia), as well as parallel botany’s attention to the artificial (Lionni), I follow the fate of one paper flower as it intersects with the gendered history of artificial flower making and floral sexual symbolism. Thinking with this paper flower, I engage with theories that variously question binary power relations (Cixous, Barthes, Steinbock), reading these alongside scholarship on sex, gender, and masculinity in the Western (Neale, Mulvey, Bruzzi), and broaching the hierarchies of settler colonialism. The film’s floral aesthetic, I argue, challenges the either/or logic of male or female, masculine or feminine, and even though it cannot fully break away from the binaries it critiques, it is indebted to registering the importance of the nuance (Barthes) in the unthreading of power. Full article

Frequent droughts induced by climate warming have caused increasing impacts on the vegetation of the Loess Plateau (LP). However, the effects of drought on vegetation are highly dependent on when the drought occurs and how long it lasts during the growing season. Unfortunately, most of the existing drought indices ignore the differences in the drought effects on different vegetation growth stages. In this study, we first established a phenology-based vegetation condition index, namely weighted vegetation condition index (WVCI), which accounts for the differences in vegetation sensitivity to drought by assigning specific weights to different phenological stages of vegetation. Then, we used the WVCI to reveal the temporal and spatial variations in vegetative drought from 2001 to 2019 over the LP from the aspects of drought frequency, trend and relative deviation. The results showed that (1) the LP experienced frequent droughts during the study period, but mainly mild and moderate droughts. The drought frequencies decreased from southeast to northwest, and extreme droughts rarely occurred in mountainous areas and plains. (2) The droughts in most areas of the LP tended to ease, and only a few areas in the Hetao Plain, Ningxia Plain and Fenwei Plain showed an increasing trend of drought. (3) After 2012, the departure percentage of WVCI in most areas of the LP was positive, indicating above-average vegetation conditions. (4) Compared with the well-established vegetation condition index, the WVCI proved to have the ability to monitor and assess vegetative drought on an annual scale in the LP. As a result, our research could help develop and implement drought-resistance and disaster-prevention measures on the LP. Full article

Exploring the spatiotemporal characteristics of park visitors and the “push and pull” factors that shape this mobility is critical to designing and managing urban parks to meet the demands of rapid urbanization. In this paper, 56 parks in Shenzhen were studied in 2019. First, cell phone signaling data were used to extract information on visitors’ departure locations and destination parks. Second, the bivariate Moran’s I and bivariate local Moran’s I (BiLISA) methods were used to identify the statistical correlation between the factors of the built environment and the park recreation trips. Finally, linear regression models were constructed to quantify the factors influencing the attractiveness of the park. Our study showed the following: (1) Recreation visitors at large parks varied significantly among population subgroups. Compared with younger adults, teenagers and older adults traveled lower distances and made fewer trips, and in particular, older adults of different genders differed significantly in park participation. (2) Recreational trips in large parks were related to the functional layout of the built environment around their residence. In areas with rich urban functions (e.g., southern Shenzhen), trips to large parks for leisure are more aggregated. (3) The findings reinforce the evidence that remote sensing data for urban vegetation can be an effective factor in characterizing park attractiveness, but the explanatory power of different vegetation data varies widely. Our study integrated the complementary human activity and remote sensing data to provide a more comprehensive understanding of urban park use and preferences. This will be important for future park planning. Full article

Assessment of ecosystem change often focuses on the degree of conversion and representation in networks of protected areas. While essential, these factors alone do not provide a holistic index of ecosystem conditions. Metrics that compare the current state of ecosystems to a meaningful reference condition can help identify “hidden” risks, lost functions, and provide conservation and management-relevant insights. Here we review a departure metric that can be used to measure ecosystem conditions and its implementation for all lands in the United States by the LANDFIRE Program. We then use two case studies to demonstrate how manually calculating the departure metric is used to explore under- and over-representation of structural stages. Finally, we document the assumptions, interpretation, and limitations of the departure metric, and discuss its current and possible future applications. Full article

In 2021, the Biden administration signed an executive order to protect 30% of American lands by 2030. Accomplishing this ambitious goal in the U.S. requires understanding the relative contribution of public and private lands toward supporting biodiversity. New approaches are needed because existing approaches focus on quantity of habitat without incorporating quality. To fill this need, we developed a 30 m resolution national habitat condition index (HCI) that integrates quality and quantity measures of habitat. We hypothesized that including an evaluation of the quality of habitat at landscape scales, both in conservation-focused preserves and working lands would provide a better assessment of the value of geographies for conservation. We divided the conterminous U.S. by major land cover type and into natural and cultivated lands and then spatially mapped multiple anthropogenic stressors, proximity to aquatic habitat, and vegetation departure from expected natural disturbance regimes. Each map layer was then scored for site impact and distance decay and combined into a final national index. Field observations providing scored relative ecological conditions were used for HCI calibration and validation at both CONUS and regional scales. Finally, we evaluate lands by management (conservation versus working lands) and ownership (public versus private) testing the value of these lands for conservation. While we found regional differences across CONUS, functional habitat was largely independent of protection status: working lands provide clear habitat and other values. These results are relevant for guiding strategies to achieve the U.S. 30 by 30 goals. Where similar data exist in other countries, analogous modeling could be used to meet their national conservation commitments. Full article

Eggplant is the fifth economically most important vegetable in the Solanaceae family after tomato, potato, chili, and tobacco. Apart from the well-cultivated brinjal or aubergine eggplant (Solanum melongena L.), two other underutilized eggplant species, the African eggplant (S. macrocarpon L.) and the scarlet eggplant (S. aethiopicum L.), were also cultivated with local importance where the leaves and fruits are used for food and medicinal purposes. The major objectives of the eggplant breeding program are to improve fruit quality, increase yield performance through heterosis breeding, and introduce pest and disease resistances from wild relatives. Europe and Asia hold a wide collection of germplasm resources with significant potential for genetic improvement. While cultivated eggplant is susceptible to several fungi and bacteria, many wild relatives offer potential resistance to these pathogens. In this paper, we review the genetic resources and diversity of cultivated eggplant and its wild relatives. As a point of departure, we examine the economic importance, domestication, taxonomy characterization, and relationships of the crop and its wild relatives. The importance of evaluating and safeguarding wild relatives is highlighted, as crop wild relatives are highly underrepresented. A key section in this study is an overview dedicated to genetic resources, resistance to biotic and abiotic stresses, pre-breeding, and breeding for sustainable eggplant production. Full article

Managing vast federal public lands governed by multiple land use policies creates challenges when demographic data on at-risk species are lacking. The U.S. Bureau of Land Management Cedar City Field Office used this project in the Black Mountains (Utah) to inform vegetation management supporting at-risk greater sage-grouse and Utah prairie dog planning. Ecological systems were mapped from satellite remote sensing imagery and used to model species habitat suitability under two levels of management activity (custodial, preferred) and climate scenarios for historic and two global circulation models. Spatial state-and-transition models of ecological systems were simulated for all six scenarios up to 60 years while coupled with expert-developed habitat suitability indices. All ecological systems are at least moderately departed from reference conditions in 2012, whereas habitat suitability was 50.5% and 48.4% for sage-grouse and prairie dog, respectively. Management actions replaced non-native annual grasslands with perennial grasses, removed conifers, and controlled exotic forbs. The drier climate most affected ecological departure and prairie dog habitat suitability at 30 years only. Different climates influenced spatial patterns of sage-grouse habitat suitability, but nonspatial values were unchanged. Climate impacts on fire, vegetation succession, and restoration explain many results. Front-loading restoration is predicted to benefit under future drier climate. Full article

Fire is an ecological process that also has socio-economic effects. To learn more about fire occurrence, I examined relationships between land classes and about 12,000 spatially delineated large wildfires (defined here as uncontrolled fires ≥200 ha, although definitions vary) during 1999 to 2017 in the conterminous United States. Using random forests, extreme gradient boosting, and c5.0 classifiers, I modeled all fires, first years (1999 to 2002), last years (2014 to 2017), the eastern, central, and western United States and seven ecoregions. The three classifiers performed well (true positive rates 0.82 to 0.94) at modeling all fires and fires by year, region, and ecoregion. The random forests classifier did not predict to other time intervals or regions as well as other classifiers and models were not constant in time and space. For example, the eastern region overpredicted fires in the western region and models for the western region underpredicted fires in the eastern region. Overall, greater abundance of herbaceous grasslands, or herbaceous wetlands in the eastern region, and evergreen forest and low abundance of crops and pasture characterized most large fires, even with regional differences. The 14 states in the northeastern United States with no or few large fires contained limited herbaceous area and abundant crops or developed lands. Herbaceous vegetation was the most important variable for fire occurrences in the western region. Lack of crops was most important for fires in the central region and a lack of pasture, crops, and developed open space was most important for fires in the eastern region. A combination of wildlands vegetation was most influential for most ecoregions, although herbaceous vegetation alone and lack of pasture, crops, and developed open space also were influential. Despite departure from historical fire regimes, these models demonstrated that herbaceous vegetation remains necessary for fires and that evergreen forests in particular are fire-prone, while reduction of vegetation surrounding housing developments will help provide a buffer to reduce large fires. Full article

Airborne lidar point clouds of vegetation capture the 3-D distribution of its scattering elements, including leaves, branches, and ground features. Assessing the contribution from vegetation to the lidar point clouds requires an understanding of the physical interactions between the emitted laser pulses and their targets. Most of the current methods to estimate the gap probability ( $P_{\mathrm{gap}}$ ) or leaf area index (LAI) from small-footprint airborne laser scan (ALS) point clouds rely on either point-number-based (PNB) or intensity-based (IB) approaches, with additional empirical correlations with field measurements. However, site-specific parameterizations can limit the application of certain methods to other landscapes. The universality evaluation of these methods requires a physically based radiative transfer model that accounts for various lidar instrument specifications and environmental conditions. We conducted an extensive study to compare these approaches for various 3-D forest scenes using a point-cloud simulator developed for the latest version of the discrete anisotropic radiative transfer (DART) model. We investigated a range of variables for possible lidar point intensity, including radiometric quantities derived from Gaussian Decomposition (GD), such as the peak amplitude, standard deviation, integral of Gaussian profiles, and reflectance. The results disclosed that the PNB methods fail to capture the exact $P_{\mathrm{gap}}$ as footprint size increases. By contrast, we verified that physical methods using lidar point intensity defined by either the distance-weighted integral of Gaussian profiles or reflectance can estimate $P_{\mathrm{gap}}$ and LAI with higher accuracy and reliability. Additionally, the removal of certain additional empirical correlation coefficients is feasible. Routine use of small-footprint point-cloud radiometric measures to estimate $P_{\mathrm{gap}}$ and the LAI potentially confirms a departure from previous empirical studies, but this depends on additional parameters from lidar instrument vendors. Full article

In this paper, we develop a systems dynamics model of a coupled human and natural fire-prone system to evaluate changes in wildfire response policy. A primary motivation is exploring the implications of expanding the pace and scale of using wildfires as a forest restoration tool. We implement a model of a forested system composed of multiple successional classes, each with different structural characteristics and propensities for burning at high severity. We then simulate a range of alternative wildfire response policies, which are defined as the combination of a target burn rate (or inversely, the mean fire return interval) and a predefined transition period to reach the target return interval. We quantify time paths of forest successional stage distributions, burn severity, and ecological departure, and use departure thresholds to calculate how long it would take various policies to restore forest conditions. Furthermore, we explore policy resistance where excessive rates of high burn severity in the policy transition period lead to a reversion to fire exclusion policies. Establishing higher burn rate targets shifted vegetation structural and successional classes towards reference conditions and suggests that it may be possible to expand the application of wildfires as a restoration tool. The results also suggest that managers may be best served by adopting strategies that define aggressive burn rate targets but by implementing policy changes slowly over time. Full article

El Niño/Southern Oscillation (ENSO) teleconnections present a hemispheric dipole pattern in both rainfall and vegetation between eastern and southern Africa. We analyze precipitation and normalized difference vegetation index (NDVI) departures during the 2015–2017 ENSO cycle; with one of the strongest warm events (El Niño) on record followed by a short and weak cold event (La Niña). Typically, southern (eastern) Africa is associated with dry (wet) conditions during El Niño, and wet (dry) conditions during La Niña. In general, the temporal and spatial evolution of vegetation responses show the expected dipole pattern during the 2015–2016 El Niño and following 2016–2017 La Niña. However, in 2015–2016 the eastern African impacts were displaced to the west and south of the canonical pattern. Composites of seasonal vegetation anomalies highlight the magnitude and position of impacts. Further investigation through empirical orthogonal teleconnections and spatial correlation analysis confirms the similar, but opposite, teleconnection impacts in eastern and southern Africa. The diametrically opposed patterns have particular implications for agricultural production and the availability of fodder and forage, especially in the pastoral communities of the two regions. Full article

The terrestrial condition assessment (TCA) evaluates effects of uncharacteristic stressors and disturbance agents on land-type associations (LTAs) to identify restoration opportunities on national forest system (NFS) lands in the United States. A team of agency scientists and managers, representing a broad array of natural resource disciplines, developed a logic structure for the TCA to identify appropriate data sources to support analyses. Primary national data sources included observed insect- and pathogen-induced mortality, key critical loads for soil and the atmosphere, long term seasonal departures in temperature and precipitation, road densities, uncharacteristic wildfires, historical fire regime departure, wildfire potential, insect and pathogen risk, and vegetation departure from natural range of variability. The TCA was implemented with the ecosystem management decision support (EMDS) system, a spatial decision support system for landscape analysis and planning. EMDS uses logic models to interpret data, synthesizes information over successive layers of logic topics, and draws inferences about the ecological integrity of LTAs as an initial step to identifying high priority LTAs for landscape restoration on NFS lands. Results from the analysis showed that about 74 percent of NFS lands had moderate or better overall ecological integrity. Major impacts to ecological integrity included risk of mortality due to insects and disease, extent of current mortality, extent of areas with high and very high wildfire hazard potential, uncharacteristically severe wildfire, and elevated temperatures. In the discussion, we consider implications for agency performance reporting on restoration activities, and subsequent possible steps, including strategic and tactical planning for restoration. The objective of the paper is to describe the TCA framework with results from a national scale application on NFS lands. Full article

The development of low-cost and relatively simple tools to identify emerging land degradation across complex regions is fundamental to plan monitoring and intervention strategies. We propose a procedure that integrates multi-spectral satellite observations and air temperature data to detect areas where the current status of local vegetation and climate shows evident departures from the mean conditions of the investigated region. Our procedure was tested in Basilicata (Italy), which is a typical bio-geographic example of vulnerable Mediterranean landscape. We grouped Landsat TM/ETM+ NDVI and air temperature (T) data by vegetation cover type to estimate the statistical distributions of the departures of NDVI and T from the respective land cover class means. The pixels characterized by contextual left tail NDVI values and right tail T values that persisted in time (2002–2006) were classified as critical to land degradation. According to our results, most of the critical areas (88.6%) corresponded to forests affected by erosion and to riparian buffers that are shaped by fragmentation, as confirmed by aerial and in-situ surveys. Our procedure enables cost-effective screenings of complex areas able to identify raising hotspots that require urgent and deeper investigations. Full article