Areas Of Sparse Vegetation Research Articles

Impact of greenery and waterbody on the cooling of city’s environment: a case of Rajshahi City

ABSTRACT Both plants and bodies of water are crucial to the health of the urban landscape. The degree to which these factors contribute to cooling appears, however, to change depending on factors such as location, time of year, and the mix of land use and land cover. The purpose of this study was to investigate the potential role of trees and water in Rajshahi City Corporation in reducing urban heat island effect. As a first step, satellite images are processed to generate the normalized difference vegetation index (NDVI) and the modified normalized difference water index (MNDWI). The second step is to produce maps of vegetation and water features by using a decision tree (DT) algorithm fed with threshold values from the indices. After that, thermal bands from the Landsat 8 OLI and MODIS Terra Earth observation data are used to create a map of the land surface temperature (LST). Finally, this study assessed the effect of vegetation and water coverage on global surface temperatures by analyzing their spatial distributions. Different kinds of land cover can be found in and around urban centers. Therefore, this study chose two types of water bodies (big and small) and two types of vegetation (sparse and attached/compact) for experiment. Water bodies produce cooler temperatures than other land cover types, as shown by the data. It has been found, however, that cooling rates differ across large and small bodies of water. The surface temperatures of large bodies of water are typically lower than those of smaller bodies of water. Accordingly, it appears that expansive areas significantly impact the cooling of metropolitan environments. The findings also demonstrate a difference in surface temperature between areas of dense and sparse vegetation. The cooling impact of the sparse vegetation is much less than that of the dense and widespread vegetation. There was a significant relationship between surface warming and vegetation cover in this study (R2 = 0.81). Contrarily, the presence of water and vegetation is strongly correlated (R2 = 0.88) with surface warming. The findings also show that surface warming differs depending on land use. According to a study of four typical RCC locations, business districts and industrial zones have a far greater impact on local surface temperatures than do residential developments. The experiment demonstrates that the residential area with a high proportion of vegetation had a lower average temperature by nearly 2°C. The study’s findings highlight the value of maintaining urban parks and water features.

Impacts of land cover changes on dust emissions in northern China (2000–2020)

AbstractLand cover is a key factor affecting dust emissions. Substantial changes in land cover have occurred due to human activities and climate change in northern China. However, the extent to which these changes influence dust emissions is still controversial. Here, we explored the specific impact of land use type transformation on dust emissions between 2000 and 2020 by using the Weather Research and Forecasting model coupled with Chemistry (WRF‐Chem) from the perspective of land cover changes. Two scenarios were set up and compared to quantify this impact. One scenario was using the land cover data in 2000, and the other scenario was to use the land cover data in 2020. Both scenarios were driven by the initial meteorological conditions in 2020. Results indicated that the land cover changes between 2000 and 2020 reduced dust emissions in northern China, but the weakened intensity showed obvious spatial differences. Different land use conversion types had significant differences in their impact on dust emissions. Transforming bare areas to water bodies can reduce dust emissions by up to −3.62 g m−2 year−1. This is followed by the change from bare areas to sparse vegetation areas, which can decrease dust emissions by −2.9 g m−2 year−1, and then by the conversion from bare areas to cropland, with a reduction of −2.57 g m−2 year−1. These findings can offer methodologies and data support for the quantitative evaluation of the effects of land cover changes on dust emissions. They can also serve as a reference for environmental management when formulating land use policies.

Satellite observed aboveground carbon dynamics in Africa during 2003–2021

Vegetation dynamics in the African continent play an important role in the global terrestrial carbon cycle. Above-ground biomass carbon (AGC) stocks in Africa are sensitive to drought, fires and anthropogenic disturbances, and can be increased from forest restoration and tree plantation. However, there are large uncertainties in estimating changes that have occurred in AGC stocks in Africa over the past decades. Here, we used a microwave remote sensing-based vegetation index named Vegetation Optical Depth produced from X-band observations by INRAE Bordeaux (IB X-VOD) to describe the AGC dynamics in Africa covering recent decades. From 2003 to 2021, African AGC showed a net increase at a rate of +0.06 [+0.04, +0.07] PgC·yr−1 (the range represents the minimum and maximum AGC changes estimated by four calibrations), resulting from a large carbon gain of +0.55 [+0.46, +0.60] PgC·yr−1 during the first decade of the twenty-first century (period 1: 2003–2010) and a much weaker increase of +0.05 [+0.04, +0.07] PgC·yr−1 over the recent decade (period 2: 2013–2021). AGC gains were mainly found in non-forest woody areas, which contributed the most to the AGC changes during 2003–2021. Rainforests showed a minor AGC loss of −0.02 [−0.03, −0.02] PgC·yr−1, which emphasizes the need for forest conservation in Africa. Relationships between the AGC changes and potential forcing climate or anthropogenic variables suggested that human-induced deforestation and water stress (especially the vapor pressure deficit (VPD)) are the most important variables explaining the spatial and temporal AGC variations, respectively. For areas of rainforests, we identified a strong relationship between AGC and VPD (negative), soil moisture (positive) and radiation (positive). For areas of sparse vegetation (mainly located in drylands), AGC changes are largely dominated by changes in the soil water conditions. This study presents a new dataset for monitoring AGC dynamics at a continental scale over recent decades being independent of optical observations, quantifying the impacts of anthropogenic pressure and water stress on aboveground biomass carbon changes.

Exploring the effect of landscape composition and agroecological practices on wild bees in horticultural farms

How farms and the surrounding landscape are managed locally substantially affects biodiversity, with consequent impacts on the supply of certain ecosystem services, such as pollination. Wild bees provide pollination services for small-scale horticultural farming, and are key to determining and improving farm production, as well as maintaining ecosystem-level diversity. Here, we investigated how landscape composition and agroecological practices affect wild bee community in small-scale horticultural farms. The study was conducted at 16 horticultural farms in the northern part of Madrid. The pan-trapping method was used to collect wild bees during the flowering period of horticultural plants. We interviewed farmers to identify which agroecological practices were primarily adopted to attain a resilient ecosystem. The most common practices adopted were weed control methods, natural fertilizer usage, pest control, and crop diversification. In total, 109 wild bee species were identified, and included individuals from all six bee families present on the Iberian Peninsula. One genus (Lasioglossum) was highly abundant, accounting for 68% of individuals, and is a known ground nester. Areas of sparse vegetation and bare soil and forested areas primarily enhanced the richness of bee species. On the other hand, abundance of wild bees is enhanced by pasture and forest areas. The presence of these habitats in areas surrounding farms might represent the potential nesting sites with important resources for wild bees. Small-scale horticulture production promotes landscape diversity, which strongly promotes the potential of different ecosystem services, including pollination and wild bees. Thus, implementing agroecological practices could transcend farms, and individual fields, to the landscape level, providing long-term sustainability of ecosystems.

Combining Multi-Dimensional SAR Parameters to Improve RVoG Model for Coniferous Forest Height Inversion Using ALOS-2 Data

This paper considers extinction coefficient changes with height caused by the inhomogeneous distribution of scatterers in heterogeneous forests and uses the InSAR phase center height histogram and Gaussian function to fit the normalized extinction coefficient curve so as to reflect the vertical structure of the heterogeneous forest. Combining polarization decomposition based on the physical model and the PolInSAR parameter inversion method, the ground and volume coherence matrices can be separated based on the polarization characteristics and interference coherence diversity. By combining the new abovementioned parameters, the semi-empirical improved RVoG inversion model can be used to both quantify the effects of temporal decorrelation on coherence and phase errors and avoid the effects of small vertical wavenumbers on the large temporal baseline of spaceborne data. The model provided robust inversion for the height of the coniferous forest and enhanced the parameter estimation of the forest structure. This study addressed the influence of vertical structure differences on the extinction coefficient, though the coherence of the ground and volume in sparse vegetation areas could not be accurately estimated, and the oversensitivity of temporal decorrelation caused by inappropriate vertical wavenumbers. According to this method we used spaceborne L-band ALOS-2 PALSAR data on the Saihanba forest in Hebei Province acquired in 2020 for the purpose of height inversion, with a temporal baseline range of 14–70 days and the vertical wavenumber range of 0.01–0.03 rad/m. The results are further validated using sample data, with R2 reaching 0.67.

Modelling land use and land cover changes in the Lower Neretva Region

It has been shown that simulation models are reliable tools for predicting land changes, which contributes to better understanding and management of human impact on the environment. Land use and land cover changes in the Lower Neretva Region between 1990 and 2035 have been analysed and modelled in this study. The final simulation model of future changes was created based on cellular automata and artificial neural networks, implemented in the MOLUSCE plugin for QGIS. In addition, a test simulation model for 2020 was created, which showed high accuracy. Input variables for the final simulation model included a digital elevation model (DEM), slope, distance from water bodies, distance from built-up areas, and population density by settlement in 2011 and 2021. According to the results, forests and grasslands will expand and occupy almost 45% of the area. A slight increase in built-up and agricultural areas is expected, while swamps, water bodies, and sparse vegetation areas will decrease.

Картографирование структуры пространственных точечных образов в региональном масштабе

To identify geographical regularities in changing the structure of spatial point patterns, we proposed the use of cartographic visualization. Approaches to gathering initial data, mechanisms for calculating the characteristics of the spatial structure and ways of displaying them on the map are described. The process of mapping combines elements of geoinformatics, spatial statistics and cartography. The elements of the methodology were integrated using the R programming language and the spatstat package. A script was written in the R that enables automating the calculation of the spatial structure’s quantitative characteristics. Thirteen indicators were identified that can be used for cartographic visualization of the spatial point images structure features. The described approach was tested on the example of sparse woody vegetation areas found in the ravine-gully network of the Central Chernozem region.

Evaluation of IMERG and ERA5 precipitation products over the Mongolian Plateau

Precipitation is an important component of the hydrological cycle and has significant impact on ecological environment and social development, especially in arid areas where water resources are scarce. As a typical arid and semi-arid region, the Mongolian Plateau is ecologically fragile and highly sensitive to climate change. Reliable global precipitation data is urgently needed for the sustainable development over this gauge-deficient region. With high-quality estimates, fine spatiotemporal resolutions, and wide coverage, the state-of-the-art Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) and European Center for Medium-range Weather Forecasts Reanalysis 5 (ERA5) have great potential for regional climatic, hydrological, and ecological applications. However, how they perform has not been well investigated on the Mongolian Plateau. Therefore, this study evaluated the performance of three IMERG V06 datasets (ER, LR and FR), two ERA5 products (ERA5-HRES and ERA5-Land), and their predecessors (TMPA-3B42 and ERA-Interim) over the region across 2001–2018. The results showed that all products broadly characterized seasonal precipitation cycles and spatial patterns, but only the three reanalysis products, IMERG FR and TMPA-3B42 could capture interannual and decadal variability. When describing daily precipitation, dataset performances ranked ERA5-Land > ERA5-HRES > ERA-Interim > IMERG FR > IMERG LR > IMERG ER > TMPA-3B42. All products showed deficiencies in overestimating weak precipitation and underestimating high-intensity precipitation. Besides, products performed best in agricultural lands and forests along the northern and south-eastern edges, followed by urban areas and grasslands closer to the center, and worst in the sparse vegetation and bare areas of the south-west. Due to a negative effect of topographic complexity, IMERG showed poor detection capabilities in forests. Accordingly, this research currently supports the applicability of reanalysis ERA5 data over the arid, topographically complex Mongolian Plateau, which can inform regional applications with different requirements.

Leaf wax biomarkers of a high-mountain lake area in western iberia—Implications for environmental reconstructions

Leaf wax n-alkane biomarkers are widely used to infer past vegetation dynamics and hydroclimate changes. The use of these compounds strongly relies on the characterization of modern plants. However, few studies have explored leaf waxes of modern plants and their application to reconstructing climate and environmental changes in the Iberian Peninsula, a region known for its high vulnerability to climate change. In this study, we characterize the distributions and compound-specific isotopic compositions of the leaf waxes of dominant plants in the vegetation cover, soil, and surface sediment of the Lake Peixão area, a high-mountain glacial lake in Serra da Estrela (central Portugal). Our results show that the modern oro-Mediterranean (subalpine) vegetation of the study area is dominated by C3 grasses/herbs and shrubs that preferentially produce long-chain leaf waxes (≥C27). The C31n-alkane display the overall highest concentration, produced by some grasses and shrubs, but especially Erica sp (heather), which is highlighted as a major source for the total n-alkane pool in the lake sediments. C29 is the second-most abundant and the most equally produced n-alkane of the vegetation cover; C25 and C27 homologs are mainly associated with aquatic-related grasses/herbs, while C33 and C35 are particularly linked to cold-drought tolerant Juniperus sp. shrubs. Shrubs show higher but proportional values than grasses/herbs in the isotopic space, suggesting a directly proportional physiological adaptation of the two ecological forms to the prevailing climatic and environmental factors of the study area. C29 is pointed as the most representative (or less plant-biased) leaf wax n-alkane in the lake sediments. Thus, δD of C29n-alkane is interpreted as a robust terrestrial hydrological indicator (δDterr), which signal is believed to be strongly influenced by the mean air temperature and/or precipitation amount. Despite the sparse vegetation and small catchment area, the apparent hydrogen fractionation factor, determined from δDterr of the lake surface sediment, is in line with the modeled global mean values for the latitude of the study area. The different molecular and compound-specific signatures of the studied oro-Mediterranean species have the potential to support future interpretations of leaf wax biomarkers in the Iberian Peninsula.

Spatiotemporal Analysis of Fire Foci and Environmental Degradation in the Biomes of Northeastern Brazil

Forest fires destroy productive land throughout the world. In Brazil, mainly the Northeast of Brazil (NEB) is strongly affected by forest fires and bush fires. Similarly, there is no adequate study of long-term data from ground and satellite-based estimation of fire foci in NEB. The objectives of this study are: (i) to evaluate the spatiotemporal estimation of fires in NEB biomes via environmental satellites during the long term over 1998–2018, and (ii) to characterize the environmental degradation in the NEB biomes via orbital products during 1998–2018, obtained from the Burn Database (BDQueimadas) for 1794 municipalities. The spatiotemporal variation is estimated statistically (descriptive, exploratory and multivariate statistics) from the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Standardized Precipitation Index (SPI) through the Climate Hazards Group InfraRed Precipitation Station (CHIRPS). Moreover, we identify 10 homogeneous groups of fire foci (G1–G10) with a total variance of 76.5%. The G1 group is the most extended group, along with the G2 group, the exception being the G3 group. Similarly, the G4–G10 groups have a high percentage of hotspots, with more values in the municipality of Grajaú, which belongs to the agricultural consortium. The gradient of fire foci from the coast to the interior of the NEB is directly associated with land use/land cover (LULC) changes, where the sparse vegetation category and areas without vegetation are mainly involved. The Caatinga and Cerrado biomes lose vegetation, unlike the Amazon and Atlantic Forest biomes. The fires detected in the Cerrado and Atlantic Forest biomes are the result of agricultural consortia. Additionally, the two periods 2003–2006 and 2013–2018 show periods of severe and prolonged drought due to the action of El Niño.

Mapping of Major Land Use Land Cover Dynamics and Its Driving Factors: A Case Study of Nepalgunj Sub-Metropolitan City, Banke, Nepal

Understanding changes in Land Use Land Cover (LULC) is essential for managing and monitoring natural resources and development, particularly where urbanization is expanding. So, this study aimed to assess the level of changes in LULC of Nepalgunj Sub-metropolitan city using temporal Landsat satellite imageries of 1996, 2008, and 2020 AD, and the key drivers of LULC change were observed through a purposive household survey (N=140) with a sampling intensity of 0.5%. LULC maps were generated using initial unsupervised and later supervised classification. LULC changes were computed using the post-change detection classification technique. LULC map of 1996 AD, 2008 AD, and 2020 AD showed accuracy of 84.44 %, 85.45%, and 83.64% with a kappa value of 0.8381, 0.8497, and 0.829 respectively. Bareland, Human buildup, and grassland were found to have increased by 13.34%, 5.07%, and 29.62% respectively while sparse vegetation, dense vegetation, and water bodies were found to have decreased by 44.10%, 17.82%, and 13.34% respectively between 1996 and 2008. Likewise, there was decrease in grassland area (-26%), dense vegetation area (-9.48%), sparse vegetation area (-5%), water bodies (-0.12%), and increase in Bareland (+20%) and Human buildup (+20.6%) in between 2008 to 2020. Eight key drivers of LULC, development of infrastructure, government policy, plans, and land market, forest encroachment, forest, and its products, political condition, economic opportunities, and hotel and tourism activities, were recognized in the study area. Further research is required to determine the specific ramifications of the aforementioned LULC change drivers, as well as the area's long-term viability.

Ensemble Machine Learning Outperforms Empirical Equations for the Ground Heat Flux Estimation with Remote Sensing Data

Estimating evapotranspiration at the field scale is a major component of sustainable water management. Due to the difficulty to assess some major unknowns of the water cycle at that scale, including irrigation amounts, evapotranspiration is often computed as the residual of the instantaneous surface energy budget. One of the Surface Energy Balance components with the largest uncertainties in their quantification over bare soils and sparse vegetation areas is the ground heat flux (G). Over the last decades, the estimation of G with remote sensing (RS) data has been mainly achieved with empirical equations, on the basis of the G and net radiation (Rn) ratio, G/Rn. The G/Rn empirical equations generally require vegetation data (Type I empirical equations), in combination with surface temperature (Ts) and albedo (Type II empirical equations). In this article, we aim to evaluate the estimation of G with RS data. Here, we compared eight G/Rn empirical equations against two types of machine learning (ML) methods: an ensemble ML type, the Random Forest (RF), and the Neural Networks (NN). The comparison of each method was evaluated using a wide range of climate and land cover datasets, including data from Eddy-Covariance towers that extend along the mid-latitude areas that encompass the European and African continents. Our results have shown evidence that the driver of G in bare soils and sparse vegetation areas (Fraction of Vegetation, Fv ≤ 0.25) is Ts, instead of vegetation greenness indexes. On the other hand, the accuracy in the estimation of G with Rn, Ts or Fv decreases in densely vegetated areas (Fv ≥ 0.50). There are no significant differences between the most accurate Type I and II empirical equations. For bare soils and sparse vegetation areas the empirical equation which combines the Leaf Area Index (LAI) and Ts (E7) estimates G best. In densely vegetated areas, an exponential empirical equation based on Fv (E4), shows the best performance. However, ML better estimates G than the empirical equations, independently of the Fv ranges. An RF model with Rn, LAI and Ts as predictor variables shows the best accuracy and performance metrics, outperforming the NN model.

Dynamic Forecast of Desert Locust Presence Using Machine Learning with a Multivariate Time Lag Sliding Window Technique

Desert locust plagues can easily cause a regional food crisis and thus affect social stability. Preventive control of the disaster highlights the early detection of hopper gregarization before they form devastating swarms. However, the response of hopper band emergence to environmental fluctuation exhibits a time lag. To realize the dynamic forecast of band occurrence with optimal temporal predictors, we proposed an SVM-based model with a temporal sliding window technique by coupling multisource time-series imagery with historical locust ground survey observations from between 2000–2020. The sliding window method was based on a lagging variable importance ranking used to analyze the temporal organization of environmental indicators in band-forming sequences and eventually facilitate the early prediction of band emergence. Statistical results show that hopper bands are more likely to occur within 41–64 days after increased rainfall; soil moisture dynamics increasing by approximately 0.05 m³/m³ then decreasing may enhance the chance of observing bands after 73–80 days. While sparse vegetation areas with NDVI increasing from 0.18 to 0.25 tend to witness bands after 17–40 days. The forecast model combining the optimal time lags of these dynamic indicators with other static indicators allows for a 16-day extended outlook of band presence in Somalia, Ethiopia, and Kenya. Monthly predictions from February to December 2020 display an overall accuracy of 77.46%, with an average ROC-AUC of 0.767 and a mean F-score close to 0.772. The multivariate forecast framework based on the lagging effect can realize the early warning of band presence in different spatiotemporal scenarios, supporting early decisions and response strategies for desert locust preventive management.

基于SNIC-CNN-SVM模型的京津风沙源二期工程区土地利用/土地覆盖遥感识别研究

PDF HTML阅读 XML下载 导出引用 引用提醒 基于SNIC-CNN-SVM模型的京津风沙源二期工程区土地利用/土地覆盖遥感识别研究 DOI: 10.5846/stxb202109262707 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 中国林业科学研究院院基金重点项目（CAFYBB2019ZB004）；国家高分重大科技专项（21-Y30B02-9001-19/22-3） Remote sensing recognition of land use/land cover in the Beijing-Tianjin sandstorm source region based on SNIC-CNN-SVM model Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:稀疏植被覆盖（草地、沙地、戈壁）演变能够直接表征区域生态环境和人类活动的动态影响变化。但由于大尺度稀疏植被区一般都具有地理跨度大，景观结构复杂多样，破碎化程度高，现有地表覆盖分类产品针对性不足等问题，使得该区域内林草沙的遥感提取难度较大，精度普遍偏低，直接制约生态效应评价模型的应用效果。因此，以典型大尺度稀疏植被区--京津风沙源治理二期工程区为研究区，研建了SNIC-CNN-SVM （SCS）模型，实现了大尺度稀疏植被区林草沙典型要素的信息自动提取和主要土地利用/土地覆盖类型识别。研究结果表明：1）引入惩罚性机制优化后的SNIC分割算法，有效提升了稀疏植被区与沙地区的边界区分度，有助于分类精度的提升；2）基于改进SNIC-CNN-SVM模型方案的研究区总体分类精度达89.41%，较优化前提高了11.17%，特别是乔、灌、草、沙地和戈壁的分类识别精度显著提升，表明该优化方案在以研究区为代表的稀疏植被区域分类中具有较好的应用效果和推广价值；3）分类结果显示，2020年工程区草地面积最大，占到了一半以上（51.52%），沙地占比11.96%，稀疏植被覆盖（草地、沙地、戈壁）区域占比68.68%，表明工程区处在林地-稀疏植被-沙地的过渡地带，生态环境保护压力与防沙治沙形势依然严峻；4）近20年来，乔灌草等植被增加面积约占工程区20.64%，主要由沙化土地转化，沙化土地减少面积约占工程区的4.58%，表明研究区植被状况不断改善，实施的各项生态工程作用显著，能够更有效地服务于多维度生态系统服务功能评价。该研究以期能够为京津风沙源二期工程区的生态系统演变规律研究及生态工程评价等工作提供重要科学支撑。 Abstract:The evolution of sparse vegetation cover (grassland, sandy land, Gobi) can directly characterize the dynamic changes of the regional ecological environment and human activities. However, large-scale sparse vegetation areas generally have large geographic spans, complex and diverse landscape structures, high fragmentation, and insufficient pertinence of existing land cover classification products. These problems make it difficult to extract the forest, grass, and sand in this area by remote sensing, and the accuracy is generally low, which directly restrict the application results of the ecological effect evaluation model. Therefore, this study took the typical large-scale sparse vegetation area, the Beijing-Tianjin sandstorm source region (BTSSR) as the study area, and the SNIC-CNN-SVM (SCS) model is proposed. The forest, grass, sand element information is automatically extracted and the land use/land cover types are identified in the study area. The results show that:1) the SNIC segmentation algorithm was optimized by the penalty mechanism and can effectively increase the boundary discrimination between the sparse vegetation area and the sandy area, which is helpful to improve the classification accuracy; 2) the overall classification accuracy of the optimized model reached 89.41%, which is 11.17% higher than that before optimization, especially the classification and recognition accuracy of trees, shrubs, grasses, sandy land and Gobi is significantly improved, indicating that the optimization model has good application effect and promotion value in the classification of sparse vegetation areas represented by the study area; 3) in 2020, the grassland area in the study area was the largest, accounting for more than half (51.52%), sandy land accounted for 11.96%, and sparse vegetation coverage (grassland, sandy land, Gobi) accounted for 68.68%, indicating that the project area is in the transition zone of woodland-sparse vegetation-sandy land, and the pressure on ecological environment protection and the situation of desertification prevention and control are still severe; 4) in the past 20 years, the increased area of vegetation such as arbor, shrub and grass accounted for about 20.64% of the project, which is mainly derived from desertified land. The reduced area of desertified land accounted for about 4.58% of the project area, indicating that the vegetation conditions in the study area are continuously improving, and the implemented ecological projects have a significant effect. The method in this paper can be more effectively used for multi-dimensional ecosystem service function evaluation. This study is expected to provide important scientific support for the research on the evolution law of the ecosystem and the evaluation of ecological engineering in the BTSSR. 参考文献 相似文献 引证文献

Projected Changes in Terrestrial Vegetation and Carbon Fluxes under 1.5 °C and 2.0 °C Global Warming

The terrestrial ecosystem plays a vital role in regulating the exchange of carbon between land and atmosphere. This study investigates how terrestrial vegetation coverage and carbon fluxes change in a world stabilizing at 1.5 °C and 2 °C warmer than pre-industrial level. Model results derived from 20 Earth System Models (ESMs) under low, middle, and high greenhouse emission scenarios from CMIP5 and CMIP6 are employed to supply the projected results. Although the ESMs show a large spread of uncertainties, the ensemble means of global LAI are projected to increase by 0.04 ± 0.02 and 0.08 ± 0.04 in the 1.5 and 2.0 °C warming worlds, respectively. Vegetation density is projected to decrease only in the Brazilian Highlands due to the decrease of precipitation there. The high latitudes in Eurasia are projected to have stronger increase of LAI in the 2.0 °C warming world compared to that in 1.5 °C warming level caused by the increase of tree coverage. The largest zonal LAI is projected around 70° N while the largest zonal NPP is projected around 60° N and equator. The zonally inhomogeneous increase of vegetation density and productivity relates to the zonally inhomogeneous increase of temperature, which in turn could amplify the latitudinal gradient of temperature with additional warming. Most of the ESMs show uniform increases of global averaged NPP by 10.68 ± 8.60 and 15.42 ± 10.90 PgC year−1 under 1.5 °C and 2.0 °C warming levels, respectively, except in some sparse vegetation areas. The ensemble averaged NEE is projected to increase by 3.80 ± 7.72 and 4.83 ± 10.13 PgC year−1 in the two warming worlds. The terrestrial ecosystem over most of the world could be a stronger carbon sink than at present. However, some dry areas in Amazon and Central Africa may convert to carbon sources in a world with additional 0.5 °C warming. The start of the growing season in the northern high latitudes is projected to advance by less than one month earlier. Five out of 10 CMIP6 ESMs, which use the Land Use Harmonization Project (LUH2) dataset or a prescribed potential vegetation distribution to constrain the future change of vegetation types, do not reduce the model uncertainties in projected LAI and terrestrial carbon fluxes. This may suggest the challenge in optimizing the carbon fluxes modeling in the future.

Land use and land cover changes in the Lower Neretva Region from 1990 to 2020

Changes in land use and land cover are the result of complex interactions between humans and their environment. This study examines land use and land cover changes in the Lower Neretva Region between 1990 and 2020. Political and economic changes in the early 1990s resulted in changes in the landscape, both directly and indirectly. Multispectral image processing was used to create thematic maps of land use and land cover for 1990, 2005, and 2020. Satellite images from Landsat 5, Landsat 7 and Landsat 8 were the main source of data. Land use and land cover structure was assessed using a hybrid approach, combining unsupervised and manual (visual) classification methods. An assessment of classification accuracy was carried out using a confusion matrix and kappa coefficient. According to the results of the study, the percentage of built-up areas increased by almost 33%. Agricultural land and forests and grasslands also increased, while the proportion of swamps and sparse vegetation areas decreased.

Schoolyard Affordances for Physical Activity: A Pilot Study in 6 Nordic–Baltic Countries

Environmental settings influence children’s and adolescents’ physical activity (PA) in neighborhoods and schoolyards. This study aimed to explore the main characteristics of schoolyards in six Nordic–Baltic countries, to document how those facilities provide affordances for PA in 7–18 year–old schoolchildren, and how the schoolyard meets children’s preferences. One schoolyard was studied in each included country: Iceland, Norway, Finland, Lithuania, Latvia, and Estonia. The affordances, facilities, and equipment for PA in schoolyards were identified through orthophoto maps and standard registration forms. Children’s preferences were collected through group interviews at each participating school. A common design of schoolyards across countries indicated mostly flat topography with sparse vegetation and green areas dominated by large traditional sport arenas such as a football field, areas suitable for ball games, and track and field activities. Green spaces and varied topography were more prominent in Nordic countries. Across nationalities, the responses from pupils regarding the schoolyard were similar: they liked it though they wished for more variety of activities to do during recess. National regulations/recommendations for schoolyard design differed across the countries, being more restricted to sport fields and sport-related activities in Latvia and Lithuania, while in Nordic countries, the recommendations focused more on versatile schoolyard design.

Quantitative assessment of the effect of mining subsidence on the health of native floras using remote sensing techniques

Remote sensing technique has been used in this paper to study the effect of underground coal mining subsidence on the health condition and growth pattern of the native vegetation. The study site was an underground coal mining area of Singareni Collieries Company Limited (SCCL), India. Mining was performed in 2001, and subsidence occurred in 2001–2002. Satellite imagery of the undamaged forest before the mining subsidence was compared with the affected forest after the mining subsidence. The changes in vegetation covered areas were analyzed based on digital image classification and vegetation index. The evaluation of vegetation changes was performed for the years 2000–2005 (period 1), 2005–2010 (period 2), 2010–2018 (period 3), and the entire study period of 2000–2018 (period 4), separately. It was observed that the dense vegetation area was reduced by 16.91% during period 1 (after 3–4 years of the occurrence of subsidence), while during the consecutive later periods of 2 and 3, it increased by 24.27% and 6.59%, respectively. During the entire period 4 of the study, dense vegetation was increased by 13.95%. This would be because of natural recovery and gradual stabilization of the native soil due to the absence of human interference in the long term of time. The sparse vegetation and non-vegetated area were changed by +14.22% and +2.68% during period 1, while they were changed by -15.36%, -7.91%, and -8.91%, +1.32%, during periods 2 and 3, respectively.

Digital Examination of Vegetation Changes in River Floodplain Wetlands Based on Remote Sensing Images: A Case Study Based on the Downstream Section of Hailar River

The Hailar River is an important river in the Inner Mongolia Autonomous Region, China. It plays an extremely important role in maintaining the ecological balance of the region. However, in recent decades, the Hailar River and its surrounding areas have been developed at a high rate and its wetland resources have faced various threats. In this study, vegetation changes in the Hailar River wetlands were analyzed using remote sensing data from the Landsat TM (1987, 2001, and 2010) and Landsat OLI-TIRS (2019) satellites. A vegetation change model was developed using Matlab software to assess vegetation changes in the area. There were significant changes in the wetland vegetation of the lower Hailar River study site between 1987 and 2019. There was an increase in open sand habitat with a sparse vegetation area of 1.08 km2, a decrease in grassland area of 13.17 km2, and an increase in the forest area of 15.91 km2. The spatial distribution of the normalized difference vegetation index (NDVI) varied across the study site and was high overall. The vegetation types varied with distance from the river. There are two possible explanations for positive and negative vegetation change trends. In areas where the water supply is sufficient and relatively stable, the cover of forest vegetation was gradually increasing and the herbaceous plant community is gradually evolving into a scrub woodland plant community. In areas where the water supply is lacking, there are changes in the sense of a decrease of forest vegetation and an increase of open sand habitat with sparse vegetation. Therefore, this study suggests that the existing wetlands should be protected, used wisely, and developed rationally to provide sustainable resources for the next generation.

Estimation of Evapotranspiration in Sparse Vegetation Areas by Applying an Optimized Two-Source Model

Evapotranspiration (ET) is an important part of the water, carbon, and energy cycles in ecosystems, especially in the drylands. However, due to the particularity of sparse vegetation, the estimation accuracy of ET has been relatively low in the drylands. Therefore, based on the dry climate and sparse vegetation distribution characteristics of the drylands, this study optimized the core algorithms (canopy boundary resistance, aerodynamic resistance, and sparse vegetation coverage) and explored an ET estimation method in the Shuttleworth–Wallace two-layer model (SW model). Then, the Beijing–Tianjin sandstorm source region (BTSSR) was used as the study area to evaluate the applicability of the improved model in the drylands. Results show that: (1) The R2 value of the improved model results was increased by 1.4 and the RMSE was reduced by 1.9 mm, especially in extreme value regions of ET (maximum or minimum). (2) Regardless of the spatial distribution and seasonal changes of the ET (63–790 mm), the improved ET estimation model could accurately capture the differences. Furtherly, the different vegetation regions could stand for the different climate regions to a certain extent. The accuracy of the optimized model was higher in the semi-arid region (R2 = 0.92 and 0.93), while the improved model had the best improvement effect in the arid region, with R2 increasing by 0.12. (3) Precipitation was the decisive factor affecting vegetation transpiration and ET, with R2 value for both exceeding 0.9. The effect of vegetation coverage (VC) was less. This method is expected to provide a more accurate and adaptable model for the estimation of ET in the drylands.