Showing papers on "Vegetation (pathology) published in 2021"

Observed increasing water constraint on vegetation growth over the last three decades.

Abstract: Despite the growing interest in predicting global and regional trends in vegetation productivity in response to a changing climate, changes in water constraint on vegetation productivity (i.e., water limitations on vegetation growth) remain poorly understood. Here we conduct a comprehensive evaluation of changes in water constraint on vegetation growth in the extratropical Northern Hemisphere between 1982 and 2015. We document a significant increase in vegetation water constraint over this period. Remarkably divergent trends were found with vegetation water deficit areas significantly expanding, and water surplus areas significantly shrinking. The increase in water constraints associated with water deficit was also consistent with a decreasing response time to water scarcity, suggesting a stronger susceptibility of vegetation to drought. We also observed shortened water surplus period for water surplus areas, suggesting a shortened exposure to water surplus associated with humid conditions. These observed changes were found to be attributable to trends in temperature, solar radiation, precipitation, and atmospheric CO2. Our findings highlight the need for a more explicit consideration of the influence of water constraints on regional and global vegetation under a warming climate.

A unified vegetation index for quantifying the terrestrial biosphere.

Abstract: Empirical vegetation indices derived from spectral reflectance data are widely used in remote sensing of the biosphere, as they represent robust proxies for canopy structure, leaf pigment content, and, subsequently, plant photosynthetic potential. Here, we generalize the broad family of commonly used vegetation indices by exploiting all higher-order relations between the spectral channels involved. This results in a higher sensitivity to vegetation biophysical and physiological parameters. The presented nonlinear generalization of the celebrated normalized difference vegetation index (NDVI) consistently improves accuracy in monitoring key parameters, such as leaf area index, gross primary productivity, and sun-induced chlorophyll fluorescence. Results suggest that the statistical approach maximally exploits the spectral information and addresses long-standing problems in satellite Earth Observation of the terrestrial biosphere. The nonlinear NDVI will allow more accurate measures of terrestrial carbon source/sink dynamics and potentials for stabilizing atmospheric CO2 and mitigating global climate change.

Vegetation uptake of mercury and impacts on global cycling

Abstract: Mercury (Hg) is a global pollutant that emits in large quantities to the atmosphere (>6,000–8,000 Mg Hg per year) through anthropogenic activities, biomass burning, geogenic degassing and legacy emissions from land and oceans. Up to two-thirds of terrestrial Hg emissions are deposited back onto land, predominantly through vegetation uptake of Hg. In this Review, we assemble a global database of over 35,000 Hg measurements taken across 440 sites and synthesize the sources, distributions and sinks of Hg in foliage and vegetated ecosystems. Lichen and mosses show higher Hg concentrations than vascular plants, and, whereas Hg in above-ground biomass is largely from atmospheric uptake, root Hg is from combined soil and atmospheric uptake. Vegetation Hg uptake from the atmosphere and transfer to soils is the major Hg source in all biomes, globally accounting for 60–90% of terrestrial Hg deposition and decreasing the global atmospheric Hg pool by approximately 660 Mg. Moreover, it reduces the Hg deposition to global oceans, which, in the absence of vegetation, might receive an additional Hg deposition of 960 Mg per year. Vegetation uptake mechanisms need to be better constrained to understand vegetation cycling, and model representation of vegetation Hg cycling should be improved to quantify global vegetation impacts. Mercury, a semi-volatile and globally abundant pollutant, is transported through the atmosphere and taken up by vegetation. This Review discusses the mechanisms of vegetation mercury uptake and the role of vegetation in the mercury cycle, highlighting its importance for redistribution in the terrestrial environment and influence on atmospheric mercury concentrations and deposition to oceans.

A Long-Term Spatiotemporal Analysis of Vegetation Greenness over the Himalayan Region Using Google Earth Engine

Abstract: The Himalayas constitute one of the richest and most diverse ecosystems in the Indian sub-continent. Vegetation greenness driven by climate in the Himalayan region is often overlooked as field-based studies are challenging due to high altitude and complex topography. Although the basic information about vegetation cover and its interactions with different hydroclimatic factors is vital, limited attention has been given to understanding the response of vegetation to different climatic factors. The main aim of the present study is to analyse the relationship between the spatiotemporal variability of vegetation greenness and associated climatic and hydrological drivers within the Upper Khoh River (UKR) Basin of the Himalayas at annual and seasonal scales. We analysed two vegetation indices, namely, normalised difference vegetation index (NDVI) and enhanced vegetation index (EVI) time-series data, for the last 20 years (2001–2020) using Google Earth Engine. We found that both the NDVI and EVI showed increasing trends in the vegetation greening during the period under consideration, with the NDVI being consistently higher than the EVI. The mean NDVI and EVI increased from 0.54 and 0.31 (2001), respectively, to 0.65 and 0.36 (2020). Further, the EVI tends to correlate better with the different hydroclimatic factors in comparison to the NDVI. The EVI is strongly correlated with ET with r2 = 0.73 whereas the NDVI showed satisfactory performance with r2 = 0.45. On the other hand, the relationship between the EVI and precipitation yielded r2 = 0.34, whereas there was no relationship was observed between the NDVI and precipitation. These findings show that there exists a strong correlation between the EVI and hydroclimatic factors, which shows that changes in vegetation phenology can be better captured using the EVI than the NDVI.

Assessing of Urban Vegetation Biomass in Combination with LiDAR and High-resolution Remote Sensing Images

Abstract: The urban vegetation ecosystem is a vegetation ecosystem that is deeply influenced by human beings. The rapid urbanization process brings a great influence on the growth environment of urban vegeta...

Urban Vegetation Mapping from Aerial Imagery Using Explainable AI (XAI).

Abstract: Urban vegetation mapping is critical in many applications, i.e., preserving biodiversity, maintaining ecological balance, and minimizing the urban heat island effect. It is still challenging to extract accurate vegetation covers from aerial imagery using traditional classification approaches, because urban vegetation categories have complex spatial structures and similar spectral properties. Deep neural networks (DNNs) have shown a significant improvement in remote sensing image classification outcomes during the last few years. These methods are promising in this domain, yet unreliable for various reasons, such as the use of irrelevant descriptor features in the building of the models and lack of quality in the labeled image. Explainable AI (XAI) can help us gain insight into these limits and, as a result, adjust the training dataset and model as needed. Thus, in this work, we explain how an explanation model called Shapley additive explanations (SHAP) can be utilized for interpreting the output of the DNN model that is designed for classifying vegetation covers. We want to not only produce high-quality vegetation maps, but also rank the input parameters and select appropriate features for classification. Therefore, we test our method on vegetation mapping from aerial imagery based on spectral and textural features. Texture features can help overcome the limitations of poor spectral resolution in aerial imagery for vegetation mapping. The model was capable of obtaining an overall accuracy (OA) of 94.44% for vegetation cover mapping. The conclusions derived from SHAP plots demonstrate the high contribution of features, such as Hue, Brightness, GLCM_Dissimilarity, GLCM_Homogeneity, and GLCM_Mean to the output of the proposed model for vegetation mapping. Therefore, the study indicates that existing vegetation mapping strategies based only on spectral characteristics are insufficient to appropriately classify vegetation covers.

Feedbacks between vegetation restoration and local precipitation over the Loess Plateau in China

Abstract: The implementation of large-scale vegetation restoration over the Chinese Loess Plateau has achieved clear improvements in vegetation fraction, as evidenced by large areas of slopes and plains being restored to grassland or forest. However, such large-scale vegetation restoration has altered land-atmosphere exchanges of water and energy, as the land surface characteristics have changed. These variations could affect regional climate, especially local precipitation. Quantitatively evaluating this feedback is an important scientific question in hydrometeorology. This study constructs a coupled land-atmosphere model incorporating vegetation dynamics, and analyzes the spatio-temporal changes of different land use types and land surface parameters over the Loess Plateau. By considering the impacts of vegetation restoration on the water-energy cycle and on land-atmosphere interactions, we quantified the feedback effect of vegetation restoration on local precipitation across the Loess Plateau, and discussed the important underlying processes. To achieve a quantitative evaluation, we designed two simulation experiments, comprising a real scenario with vegetation restoration and a hypothetical scenario without vegetation restoration. These enabled a comparison and analysis of the net impact of vegetation restoration on local precipitation. The results show that vegetation restoration had a positive effect on local precipitation over the Loess Plateau. Observations show that precipitation on the Loess Plateau increased significantly, at a rate of 7.84 mm yr−2, from 2000 to 2015. The simulations show that the contribution of large-scale vegetation restoration to the precipitation increase was about 37.4%, while external atmospheric circulation changes beyond the Loess Plateau contributed the other 62.6%. The average annual precipitation under the vegetation restoration scenario over the Loess Plateau was 12.4% higher than that under the scenario without vegetation restoration. The above research results have important theoretical and practical significance for the ecological protection and optimal development of the Loess Plateau, as well as the sustainable management of vegetation restoration.

NDVI Indicates Long-Term Dynamics of Vegetation and Its Driving Forces from Climatic and Anthropogenic Factors in Mongolian Plateau

Abstract: In recent years, global warming and intense human activity have been responsible for significantly altering vegetation dynamics on the Mongolian Plateau. Understanding the long-term vegetation dynamics in this region is important to assess the impact of these changes on the local ecosystem. Long-term (1982–2015), satellite-derived normalized difference vegetation index (NDVI) datasets were used to analyse the spatio-temporal patterns of vegetation activities using linear regression and the breaks for additive season and trend methods. The links between these patterns and changes in temperature, precipitation (PRE), soil moisture (SM), and anthropogenic activity were determined using partial correlation analysis, the residual trends method, and a stepwise multiple regression model. The most significant results indicated that air temperature and potential evapotranspiration increased significantly, while the SM and PRE had markedly decreased over the past 34 years. The NDVI dataset included 71.16% of pixels showing an increase in temperature and evaporation during the growing season, particularly in eastern Mongolia and the southern border of the Inner Mongolia Autonomous region, China. The proportion indicating the breakpoint of vegetation dynamics was 71.34% of pixels, and the trend breakpoints mainly occurred in 1993, 2003, and 2010. The cumulative effects of PRE and SM in the middle period, coupled with the short-term effects of temperature and potential evapotranspiration, have had positive effects on vegetation greening. Anthropogenic factors appear to have positively impacted vegetation dynamics, as shown in 81.21% of pixels. We consider rapid economic growth, PRE, and SM to be the main driving factors in Inner Mongolia. PRE was the main climatic factor, and combined human and livestock populations were the primary anthropogenic factors influencing vegetation dynamics in Mongolia. This study is important in promoting the continued use of green projects to address environmental change in the Mongolian Plateau.

Flow Resistance in Open Channel Due to Vegetation at Reach Scale: A Review

Abstract: Vegetation on the banks and flooding areas of watercourses significantly affects energy losses. To take the latter into account, computational models make use of resistance coefficients based on the evaluation of bed and walls roughness besides the resistance to flow offered by vegetation. This paper, after summarizing the classical approaches based on descriptions and pictures, considers the recent advancements related to the analytical methods relative both to rigid and flexible vegetation. In particular, emergent rigid vegetation is first analyzed by focusing on the methods for determining the drag coefficient, then submerged rigid vegetation is analyzed, highlighting briefly the principles on which the different models are based and recalling the comparisons made in the literature. Then, the models used in the case of both emergent and submerged rigid vegetation are highlighted. As to flexible vegetation, the paper reminds first the flow conditions that cause the vegetation to lay on the channel bed, and then the classical resistance laws that were developed for the design of irrigation canals. The most recent developments in the case of submerged and emergent flexible vegetation are then presented. Since turbulence studies should be considered as the basis of flow resistance, even though the path toward practical use is still long, the new developments in the field of 3D numerical methods are briefly reviewed, presently used to assess the characteristics of turbulence and the transport of sediments and pollutants. The use of remote sensing to map riparian vegetation and estimating biomechanical parameters is briefly analyzed. Finally, some applications are presented, aimed at highlighting, in real cases, the influence exerted by vegetation on water depth and maintenance interventions.

Evaluation of Vegetation Indices and Phenological Metrics Using Time-Series MODIS Data for Monitoring Vegetation Change in Punjab, Pakistan

Abstract: In arid and semi-arid regions, it is essential to monitor the spatiotemporal variability and dynamics of vegetation. Among other provinces of Pakistan, Punjab has produced a significant number of crops. Recently, Punjab, Pakistan, has been described as a global hotspot for extremes of climate change. In this study, the soil adjusted vegetation index (SAVI), normalized vegetation difference index (NDVI), and enhanced vegetation index (EVI) were comprehensively evaluated to monitor vegetation change in Punjab, Pakistan. The time-series MODIS (Moderate Resolution Imaging Spectroradiometer) data of different periods were used. The mean annual variability of the above vegetation indices (VIs) from 2000 to 2019 was evaluated and analyzed. For each type of vegetation, two phenological metrics (i.e., for the start of the season and end of the season) were calculated and compared. The spatio-temporal image analysis of the mean annual vegetation indices revealed similar patterns and varying vegetation conditions. In the forests and vegetation areas with sparse vegetation, the EVI showed high uncertainty. The phenological metrics of all vegetation indices were consistent for most types of vegetation. However, the NDVI result had the greatest variance between the start and end of season. The lowest annual VI variability was mainly observed in the southern part of the study area (less than 10% of the study area) based on the statistical analysis of spatial variability. The mean annual spatial variability of NDVI was <20%, SAVI was 30%, and EVI ranged between 10–20%. More than 40% of the variability was observed in the NDVI and SAVI vegetation indices.

Revisiting global vegetation controls using multi-layer soil moisture

Abstract: The productivity of terrestrial vegetation is determined by a multitude of drivers between the land surface and atmosphere. Water availability is critical for vegetation productivity, but the verti...

Spatio-Temporal Changes and Driving Forces of Vegetation Coverage on the Loess Plateau of Northern Shaanxi

Abstract: As an important indicator of terrestrial ecosystems, vegetation plays an important role in the study of global or regional ecological environmental changes Northern Shaanxi is located in the ecologically fragile area of the Loess Plateau, which is affected by interactions between natural and human factors Here, we used the Normalized Difference Vegetation Index (NDVI) as an indicator to study the temporal and spatial variations of vegetation in Northern Shaanxi from 2000 to 2018 Based on the geographic detector method which can detect spatial differentiation, we analyzed the spatial differentiation characteristics and driving forces of vegetation in Northern Shaanxi, and revealed the most appropriate range or type of influencing factors for promoting vegetation growth The results showed that the overall vegetation coverage improved in the study area, and NDVI showed an increasing trend with a growth rate of 010/10 years from 2000 to 2018 Natural and human factors are crucial driving forces of NDVI change, among which gross domestic product, land-use type, slope, and temperature have the greatest influence The interaction between natural and human factors on NDVI was dominated by nonlinear and mutual enhancement effects, and the influence of interactions among all factors was significantly higher than that of a single factor The range or types of factors suitable for vegetation growth were analyzed in the study area, and the joint action of natural and human factors had a more significant impact on vegetation These findings provide a scientific basis for local governments to intervene in vegetation changes and ecological restoration through natural and human factors within the favorable scope