Sparse Vegetation Cover Research Articles

The Control of the Desert Locusts (Schistocerca Gregaria) in Somalia During the Upsurge Between 2019 and 2021

The desert "empty quarter" area of Saudi Arabia, together with Oman and, Yemen, was hit by a cyclone in May 2018, that resulted in vegetation flourishing and enabled the locust population present to increase 400-fold over the following six months. Following a second cyclone in October 2018, the population continued to increase an estimated 8000-fold. Swarms of these locusts in March 2019 flew east into southern Iran and thence to India and Pakistan. A change in wind direction enabled others to fly south into Yemen, where a civil war hindered any control operations. From Yemen, swarms moved to Ethiopia and Somalia in October 2019 and later into Kenya. In contrast to most countries, nomadic pastoralists in Somalia are nearly two-thirds of the population and occupy about two-thirds of the country. The ecology of nomadic pastoralism is an adaptation to an environment in which the availability of water and grass are critical factors. The life of pastoralists differs significantly from sedentary agriculturalists and is based on natural resource management that respects the limitations imposed on their environment, the necessity for mobility. To protect the pastoral system, the Government was concerned about the applying insecticides over vast areas due to the adverse impact on bees in the environment and on livestock that grazed on the pastures. Assisted by FAO, it was decided that locusts had to be controlled by the biopesticide, based on the fungus Metarhizium acridum, which produces a toxin that kills only locusts and related grasshoppers. The biopesticide, originally called "Green Muscle" was developed in the LUBILOSA Project organised with international funding and managed by CABI (Prior et al. 1992; Lomer et al. 2001). Developments in the production of the biopesticide had reduced costs and it is more effective to apply, although it takes longer to kill locusts, it can spread through a population and be particularly effective against the hopper bands of young locusts before they fly. Since 2004, the use of Metarhizium acridum had increased in China from only 5% to over 30% as over 100,000 hectares were treated. An advantage of the biopesticide is that birds are not killed and remain effective predators of the locusts. The programme confirmed the importance of using the biopesticide to protect the environment from the use of hazardous chemical insecticides. It also indicated that there were a number of Key challenges that had to be faced, namely: Difficulty in carry out field evaluations during intensive control operations; Remoteness of control areas where caged experiments could not be conducted; Limited number of government officers capable of carrying out field / laboratory assessments; Lack of suitable laboratory facilities to carry out subsequent observations on field collected samples; Narrow daily window for control operations with Metarhizium acridum operations are required as the air temperature quickly rises beyond 30°C as early as 11.00am; Sparse vegetation cover in semiarid areas affecting barrier treatment approach using aircraft.

UAV-based remote sensing using visible and multispectral indices for the estimation of vegetation cover in an oasis of a desert

Natural vegetation is an important indicator for the maintenance of symbiosis in an oasis in extremely arid zones. Unmanned aerial vehicles have advantages of high resolution and multiple wavebands to obtain details of sparse vegetation cover. So far, studies on the selection of machine learning methods are relatively limited and usually focus on only a few selected methods. In this study, the natural vegetation of the Dariyabui Oasis in the hinterland of the Taklamakan Desert in China was mapped using 2,550 samples of data and 14 visible and multispectral vegetation indices as model variables. Six machine learning methods were used to construct fractional vegetation cover (FVC) predictive regression models. Coefficient of determination (R2), root-mean-square error (RMSE), and mean-absolute error (MAE) were used to evaluate the models. The regression models were divided into four components: visible (RF: R2 = 0.65, RMSE = 0.59 %, MAE = 0.41 %), multispectral (RF: R2 = 0.71, RMSE = 0.54 %, MAE = 0.36 %), visible and multispectral (RF: R2 = 0.69, RMSE = 0.55 %, MAE = 0.37 %), and the product of visible and multispectral vegetation indices (RF: R2 = 0.68, RMSE = 0.57 %, MAE = 0.39 %). Besides, the visible vegetation index results were validated using different years and different aerial height data. The results show that these four regression models can effectively obtain the FVC of sparse vegetation of the desert. This study applied the Random Forest model, which was selected based on a comparison of other models, to predict the status of desert vegetation cover based on spectral data to provide information for its conservation and management.

Changes in the rehabilitation status of irrigated lands in the Volgograd Oblast during 2001–2018

A comparative assessment of the rehabilitation status of irrigated lands, which are located in different natural areas of the Volgograd Oblast, is presented for 2001–2018. It was found that during the specified period there were recorded significant changes in the condition of irrigated lands: the total irrigation area has dramatically decreased since 2001 (-31%), especially at local runoff (-44.5%), the groundwater level has decreased (the level >5 m remains on 78% of the area). Also on this background, the areas of secondary salinized soils were also decreased (-3.9%). The main problems at the present are: the presence of fallow lands and rain-fed lands initially cultivated for irrigation, the lack of drainage on the most of irrigation systems and significant areas of saline soils requiring rehabilitation. Differences in natural conditions have largely determined the current rehabilitation state of irrigated soils and the intensity of their changes. Thus, the largest areas of secondary saline soils are preserved on irrigation systems located on the initially highly saline, poorly drained soils of the Khvalyn clay plain (Pallasovka, Svetloyarsk irrigation systems). Reconstruction of a number of irrigation systems with areas of secondary saline soils gave a positive result, after which secondary saline soils on reclaimed lands were not detected (Large Volgograd, Tyazhin irrigation systems). Significant areas of saline soils can be found on irrigated lands in the distribution areas of natural saline soils and chestnut, light chestnut saline soils – in the south of the Volga Upland, in the area of the Northern Yergeni and on the Khvalyn clay plain (Gorodishуsche, Generalovskoye, Pallasovka, etc. irrigation systems). Inherent research on the Volga-Don irrigation system with the involvement of remote sensing data has shown that this system reflects the general features of the modern rehabilitation condition of irrigated lands of the Volgograd Oblast – fallow lands, lack of drainage, and widespread saline soils are specific for this area. The use of high-resolution multispectral satellite images (Landsat-8, Sentinel-2) for the purposes of detection of fallow and irrigated lands in the current season demonstrates the possibility of clarifying information on this category of land. The determination of the distribution of salinized and solonetzic soils on irrigated lands of the Volgograd Oblast by remote methods can be carried out indirectly, i. e. according to the state of vegetation: when areas of sparse vegetation cover are being identified on images, targeted routes for soil surveys and soil sampling are selected.

Assessment of spatiotemporal dynamics of land and vegetation cover change detection in Maze National Park, Southwest Ethiopia.

Biodiversity conservation areas include National Parks which are integral parts of protected areas. In parks, the dynamics of land and vegetation covers are significantly affected by anthropogenic activities and natural factors. These resources are exposed to challenges due to unwise practices. Assessing and monitoring the status of park's natural resources enables us to understand the extent of land and vegetation resources. Few studies have been conducted in Maze National Park (MzNP) but none of them quantified the land and vegetation cover dynamics. The purpose of this study is, therefore, to quantify the extents spatiotemporal dynamics of land and vegetation cover of MzNP using GIS and RS technology considering three decades Landsat images acquired from USGS. GPS and GCPs were collected for the analysis of land cover classification and accuracy assessment, respectively. The trend analysis result revealed that scattered tree and grass land cover area have increased from 19.8% in 1987 to 51.6% in 2019. The area of sparse vegetation cover increased from 19.7% in 2000 to 78.4% in 2019. Highly dense and dense vegetation cover decreased remarkably. Accuracy assessment was 98% and computed Kappa coefficient was 0.97. The results suggested that the extent of MzNP's vegetation cover change was basically due to anthropogenic pressure. This study could be used to alarm responsible bodies to restructure and implement better environmental protection strategies for restoration of the rapidly deteriorating vegetation resource of MzNP.

Soil Moisture Sensor Measurement and Vegetation-Soil-Water Related Indices – A Case Study in Mango Plantation, Nakhorn Ratchasima Province, Thailand

This study conducted the soil moisture estimation using remote sensing data and ground soil moisture sensors in the mango plantation. The applied methodology is the spatial and statistical analysis to determine the relationship between the measured soil moisture using ground sensors and the remote sensing indices generated from Sentinel-2A satellite images. The sensors measured the soil moisture ground data from Nov. 2019 to Feb. 2020. However, we used only the data on seven dates. This is because the cloud-free satellite images are available only on these dates to generate the remote sensing indices. The used indices are NDVI, Normalized Water Moisture Index (NDWI) for vegetation water content monitoring, Normalized Soil Moisture Index (NSMI) for data visualization and analysis. In the implementation, we first visualized the soil moisture trend compared with the remote sensing indices value at the image pixels of sensor location on each observation day. Next, we statistically analyzed the spatial data to establish the relationship between the soil moisture from all the ground sensors and the remote sensing indices. However, the output R2 is very low; then, it brings us to have an idea to apply in-depth analysis based on the ground sensor performance. This method shows an interesting result. We found that only the NDWI for monitoring vegetation water content has a similar trend with the soil moisture. Secondly, we performed the linear regression correlation between soil moisture and remote sensing indices values of each sensor as time-series analysis. The result show that the correlation between soil moisture and NDWI, NSMI and NDVI are classified into 3 groups, which are 0.7 < R2 < 0.9, 0.6 < R2 < 0.7, and R2 < 0.5, where their corresponding p-value ranges are 0.001 < p-value < 0.02, 0.01 < p-value < 0.03, and 0.08 < p-value < 0.9, respectively. Lastly, we investigated the reason that causes the very high correlation between the soil moisture value of the first group of sensors and NDWI and NSMI. The result shows that these sensors are in a sparse vegetation cover area, where NDVI ≤ 0.3. Therefore, according to this, we can conclude that remote sensing indices NDWI and NSMI can be applied for soil moisture estimation in a sparsely vegetated study area, where the NDVI value should be less than or equal to 0.3.

Dendrochronology-Based Normalized Difference Vegetation Index Reconstruction in the Qinling Mountains, North-Central China

Larix chinensis Beissn., as a native, dominant and climate-sensitive coniferous species at Mount Taibai timberline, Qinling mountains, is rarely disturbed by anthropogenic activities; thus, it is an ideal proxy for the investigation of climate change or vegetation evolution. In this study, we applied dendrochronological methods to the L. chinensis tree-ring series from Mt. Taibai and investigated the relationships between tree-ring widths and NDVI/climate factors using Pearson correlation analysis. On the basis of the remarkable positive correlations (r = 0.726, p < 0.01, n = 23) between local July normalized difference vegetation indices (NDVI) and tree-ring width indices, the regional 146-year annual maximum vegetation density was reconstructed using a regression model. The reconstructed NDVI series tracked the observed data well, as the trans-function accounted for 52.8% of observed NDVI variance during AD 1991–2013. After applying an 11-year moving average, five dense vegetation coverage periods and six sparse vegetation coverage periods were clearly presented. At a decadal scale, this reconstruction was reasonably and negatively correlated with a nearby historical-record-based dryness/wetness index (DWI), precisely verifying that local vegetation cover was principally controlled by hydrothermal variations. Spectral analysis unveiled the existence of 2–3-year, 2–4-year, 5–7-year and 7–11-year cycles, which may potentially reflect the connection between local NDVI evolution and larger-scale circulations, such as the El Niño–Southern Oscillation (ENSO) and solar activity. This study is of great significance for providing a long-term perspective on the dynamics of vegetation cover in the Qinling mountains, and could help to guide expectations of future forest variations.

Vegetation and environmental changes since the Last Glacial Maximum inferred from a lake core from Saiyong Co, central Tibetan Plateau

Pollen and grain-size records from a small lake (Saiyong Co) in the source region of the Yangtze River, central Tibetan Plateau are presented and used to reveal vegetation and lake environmental history since the Last Glacial Maximum (LGM). Results indicate that the Saiyong Co basin was a marshy environment with sparse vegetation cover from 21.3 to 6.6 cal. ka BP, before becoming a lake. Highest water level is inferred for 3.9–3.4 cal. ka BP followed by a gradual drying up until 100 years ago. Since then, the lake level has begun to rise again. Pollen spectra are dominated by Artemisia, Poaceae and Cyperaceae, and vegetation changes are mainly reflected by changes in the proportion of these three taxa, especially Artemisia and Cyperaceae. Two major events occurred at 3.9 cal. ka BP and 1.2 cal. ka BP, in response to climate change. Our results also reveal that lake-level changes affect the interpretation of the pollen assemblages due to the relationship between lake size and the pollen source area. Local pollen became the dominant component in pollen assemblages at 0.6 cal. ka BP when the lake level fell to below a certain depth. A higher percentage of Cyperaceae marks the lake’s retreat and reflects the local vegetation around Saiyong Co during 0.6 to ~0.1 cal. ka BP. In the last 100 years, a high value of Cyperaceae is linked to a wetter climate with no obvious evidence for human activities. Our results have important implications for the interpretation of fossil pollen assemblages from small lakes as the size of the lake through its history also needs to be known to prevent erroneous interpretations of the pollen data.

Sediment yields variation and response to the controlling factors in the Wei River Basin, China

Assessing regional sediment yield variation and their responses to the potential controlling factors are critical to develop specific strategies of soil conservation measures to adapt to future climate change. This study attempted to investigate the spatial–temporal variation of sediment load in the Wei River basin in the midstream of the Yellow River during 1961–2015 at 15 hydrological stations. The results indicated that annual sediment load in the past six decades decreased significantly (P < 0.01) with the changing trends of −6.43 × 104, −3.86 × 104, −4.6 × 104 t/a at Xianyang, Zhangjiashan, and Zhuangtou stations, respectively. Annual sediment load exhibited abrupt changes in the mid-1990s, which were largely attributed to the strong effects of soil conservation measures in the study area. The spatial pattern of soil erosion were characterized by high sediment yield in the north with sparse vegetation cover and well-developed gullies, and low sediment yield in the south with flat plain and good vegetation cover in the Wei River basin. The results of the partial least squares-structural equation model (PLS-SEM) showed that vegetation changes and rainfall variability explained 62.3%, 47.3%, and 40.1% of the variation in runoff at Xianyang, Zhuangtou, and Zhangjiashan stations, respectively, whereas 59.4%, 17.6% and 48% of the variation in sediment load were explained by the combining effects of rainfall variability, changes of vegetation and runoff. This study provides a deep insight for understanding the effects of driving forces on sediment yield changes, and can be useful to regional soil conservation planning in the region.

Co-Seismic Surface Ruptures of the CE 1738 M 7.6 Dangjiang Earthquake Along the NW Continuation of the Xianshuihe Fault Zone and Tectonic Implications for the Central Tibetan Plateau

The role of large-scale strike-slip faults in high-elevation areas in absorbing the strain resulting from plate convergence has yet to be scientifically understood. The Dang Jiang Fault (DJF), as the NW continuation of the Xianshuihe Fault Zone (XFZ) in the central Qinghai-Tibetan Plateau, may provide an excellent testing ground for this question, given its high slip rate, sparse vegetative cover, minimal modification, and possible relationship with the CE 1738 Dangjiang destructive earthquake. However, co-seismic surface ruptures and seismotectonics remain in dispute because of inconvenient transportation and lack of oxygen at high altitudes. Thus, field investigations are conducted here to determine co-seismic surface ruptures. The newly synthesized data from geologic observations, historical record reviews, geomorphic mapping, trench logging, and sample dating indicate that the CE 1738 Dangjiang earthquake produced an ∼100 km-long surface rupture that includes offsets of gullies, linear scarps and troughs, sag ponds,en echelonfractures, and pressure ridges. The magnitude is re-estimated asM7.6, with average and maximum strike-slip displacements of ∼2.1 ± 0.1 m and ∼3.5 ± 0.1 m, respectively. The DJF has undergone multiple seismic faulting events, and the linear fitting surface displacement rate in the Holocene is ∼6.3 ± 1.9 mm/yr with a 95% confidence interval. This study implies that the seismic hazard of the DJF cannot be underestimated given that its elapsed time is close to or beyond the recurrence interval of major earthquakes and that the oblique convergence of the Qiangtang Block might be accommodated by the clockwise rotation of the block through repeated left-lateral strike-slip movements along the southern boundary of the Bayan Har Block.

Responsive strategies of three sympatric small rodents to the altitudinal effects on microhabitats

Abstract This study evaluated the effects of altitudinal gradients on small-rodent populations and microhabitat conditions. We selected three altitudinal bands: lowland, mid-land, and highland. We captured three small-rodent species, the striped field mouse (Apodemus agrarius), the Korean field mouse (A. peninsulae), and the red-backed vole (Myodes regulus). A. agrarius preferred microhabitats with dense ground vegetation and sparse mid-story vegetation, basal area, downed trees, and stone coverage. A. peninsulae utilized ground vegetation, overstory vegetation, and downed trees. M. regulus occupied microhabitats with abundant stone coverage and little ground vegetation coverage. Ground vegetation coverage was higher in the mid-land. The lowland was characterized by high understory vegetation, bulky downed trees, and high stone coverage. A. agrarius mostly occupied the mid-land, whereas A. peninsulae and M. regulus mainly inhabited the lowland. Our results show that each small-rodent species had its own strategy for utilizing the key habitat factors in altitudinally affected microhabitats and for reducing competition among the populations. This finding contributes to improving knowledge of the ecological altitudinal features of microhabitat conditions, as well as the altitudinal distributions and abundances of three small-rodent species.

The role of fringing vegetation in supporting avian access to arid zone waterholes

ABSTRACT Access to drinking water is essential for many avian species in arid landscapes, especially in hot and dry periods when metabolic requirements for water increase. The role of fringing vegetation in facilitating surface water access by arid zone bird communities was investigated over a 14-month period during which water demand increased. Bird visitation to six long-lasting waterholes in the MacDonnell Ranges Bioregion in central Australia was recorded over two summers and one winter using camera traps. Species were assigned to functional classes based on their size and preferred foraging substrate. Generalised linear mixed models were used to test relationships between fringing vegetation variables and the independent trapping events for each functional class. Fringing vegetation was critical for small and intermediate-sized canopy foragers to access waterhole sites. Their activity declined to almost zero in areas where the nearest tree or shrub cover was greater than 10 m. The strength of this relationship was consistent as weather conditions became drier and hotter. However, activity of small and intermediate-sized canopy foragers was negatively related to the percent canopy cover of the nearest tree or shrub, potentially because sparse vegetative cover offers greater visibility when approaching the water’s edge. In contrast, ground forager and raptor activity at waterhole sites was unrelated to surrounding vegetation, and these groups frequently accessed water from open areas. Under future warming scenarios, small and intermediate canopy foragers may be vulnerable to predation if they are forced to access water at sites away from nearby fringing vegetation.

Vascular plant colonisation, distribution and vegetation development on Surtsey during 1965–2015

Since Surtsey was formed in 1963–1967, colonisation of vascular plants has been recorded by locating the f irst colonists of each species within a 1 ha (100x100 m) quadrat grid system of the island. The abundance of individual vascular plant species was further recorded within the grid in 1996-1997, 2005-2006, and 2014-2015 using a three graded abundance scale from rare to common. During 1965–2015, a total of 74 vascular plant species were found on Surtsey. The colonisation was considerable between 1965–1979 followed by a stagnation period. After the establishment of a dense seagull colony on the southern part of the island in 1986, colonisation increased greatly and peaked between 1992–1995. The colonisation differed greatly between surface types, being highest on sandy lava and barren lava, relatively low on eolian sand and coastal sediments, and none on palagonite tuff. By classification and ordination six main vegetations types were identified over the period 1996–2015, forming a gradient from scattered, species poor pioneer vegetation to a dense, lush vegetation developed under by high nutrient input from breeding seagulls. Over this period major vegetation changes occurred. Areas with pioneer vegetation have greatly decreased, while areas with Honckenya and gravel flat vegetation, and forb rich grassland have increased. The number of species per ha increased on average from 4.8 to 7.2 and 10.4 in the years 19961997, 2005-2006, 2014-2015 respectively. Despite the great influence of seabirds within their breeding colony, most of the island still has sparse vegetation cover. The colonising species differed greatly in their rate of dispersal. Species with the greatest rate were Honckenya peploides, Poa annua and Silene uniflora, followed by Sagina procumbens, Puccinellia coarctata, Cerastium fontanum, Arabidopsis petraea, Leymus arenarius and Rumex acetosella. They are either pioneers and/or ruderals and are common on the mainland of Iceland, where they grow in sparsely covered, rocky or sandy areas or on land that has been disturbed.

Vascular plant colonisation, distribution and vegetation development on Surtsey during 1965–2015

Since Surtsey was formed in 1963–1967, colonisation of vascular plants has been recorded by locating the f irst colonists of each species within a 1 ha (100x100 m) quadrat grid system of the island. The abundance of individual vascular plant species was further recorded within the grid in 1996-1997, 2005-2006, and 2014-2015 using a three graded abundance scale from rare to common. During 1965–2015, a total of 74 vascular plant species were found on Surtsey. The colonisation was considerable between 1965–1979 followed by a stagnation period. After the establishment of a dense seagull colony on the southern part of the island in 1986, colonisation increased greatly and peaked between 1992–1995. The colonisation differed greatly between surface types, being highest on sandy lava and barren lava, relatively low on eolian sand and coastal sediments, and none on palagonite tuff. By classification and ordination six main vegetations types were identified over the period 1996–2015, forming a gradient from scattered, species poor pioneer vegetation to a dense, lush vegetation developed under by high nutrient input from breeding seagulls. Over this period major vegetation changes occurred. Areas with pioneer vegetation have greatly decreased, while areas with Honckenya and gravel flat vegetation, and forb rich grassland have increased. The number of species per ha increased on average from 4.8 to 7.2 and 10.4 in the years 19961997, 2005-2006, 2014-2015 respectively. Despite the great influence of seabirds within their breeding colony, most of the island still has sparse vegetation cover. The colonising species differed greatly in their rate of dispersal. Species with the greatest rate were Honckenya peploides, Poa annua and Silene uniflora, followed by Sagina procumbens, Puccinellia coarctata, Cerastium fontanum, Arabidopsis petraea, Leymus arenarius and Rumex acetosella. They are either pioneers and/or ruderals and are common on the mainland of Iceland, where they grow in sparsely covered, rocky or sandy areas or on land that has been disturbed.

Terpenoids of plants in arid environments

Abstract Arid and desert environments are characterized by the sparse and discontinuous vegetation cover. Species that have been able to survive difficult bioclimatic conditions and adapt from generation to generation in these areas had to develop physiological and biochemical mechanisms of tolerance and/or resistance. The use of secondary metabolites, specifically terpenoids, is predominant in most of the biotic and abiotic interactions in which these plants are involved. Studies have shown their roles in the prevention of oxidative stress by intervening in thermo-tolerance, water stress, and salt stress generalized in a model of "the protective role of volatile compounds" explained by a single biochemical mechanism. Other studies have proven the functions of terpenoids in direct and indirect defenses against natural enemies, herbivores, and pathogenic microorganisms, in the attraction of pollinators, in competition and facilitation and other interactions between plants. This review mainly summarizes the recent research progress on the adaptation mechanisms of plants in arid environments and the biological and ecological roles of terpenoids in the various biotic and abiotic interactions.

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

Spatiotemporal variations of surface ozone and its influencing factors across Tibet: A Geodetector-based study

Reasons regarding surface ozone formation and distribution in remote regions is limited. Tibet is an important remote area on Earth, with various climates and extremely high elevation (average ~ 4000 m), which makes it a good place to study the spatiotemporal distribution of surface ozone and explore the causes. Based on ground monitoring data from 18 stations on Tibet between 2015 and 2019, the annual, seasonal, monthly, and diurnal variations of surface ozone were analyzed. The annual mean values (60.7–72.5 μg/m3) presented an increasing trend during the past five years, with seasonal concentrations of surface ozone higher in spring than in winter. Spatially, both the ground observations and high-resolution remote sensing data indicated that the surface ozone was relatively high in the southwest regions of Tibet, and low in the southeast and northeast areas. Geodetector analysis found that relative humidity (RH), normalized difference vegetation index (NDVI), and solar radiation (SR) were the top three individual factors affecting surface ozone distribution, while NO2, PM10, and PM2.5 showed less influence. All influencing factors showed an improvement through the two-factor interaction. The associations of RH∩PM10 (q = 0.77), RH∩NDVI (q = 0.72), and NDVI∩SR (q = 0.73) exhibited a strong impact on surface ozone distribution, suggesting that places with sparse vegetation cover, dry climate and strong SR would usually cause high atmospheric ozone burden. This could also explain why concentrations of surface ozone continue to increase in some remote areas worldwide with ecological deterioration and desertification.

Information system of analysis of geodata for tracking changes of vegetation

The rapid growth of geo-information technology capabilities in the field of spatial data processing and analysis has led to a significant growth of the role of geo-information systems in different areas of human activity. Application of approaches to spatial information processing from satellites new for more effective and efficient assessment of the state of plant cover is caused by growing tendency of availability to data of Earth remote sensing. The article offers an information system that allows to quickly and conveniently track changes in the vegetation. The analysis was carried out on the example of the Chornobyl Area between 2000 and 2020. The Chornobyl Disaster coincides with the period of intensive vegetative plant development. During that period, they are most sensitive to radiation. It has been established that for defining the quantitative state of biomass the NDVI index at different time intervals is most often used. But this index becomes ineffective during periods of weakening of active phase of vegetation. This is therefore of practical interest to assess the possibility of using the K-means clustering for the analysis of space images of vegetation cover at different phases of vegetation. As a result of the research, water surface, land with and without vegetation has been correctly interpreted, thus determining the land with a sparse vegetation and dense vegetation cover. The maps of the vegetation cover according to the normalized vegetative index using the K-medium method were constructed, the method by which changes in vegetation over 20 years can be clearly observed. The accuracy results were verified with the Common Method Bias. According to the calculations, despite all natural cataclysms (temperature increase, drought, winter anomalies of precipitations and temperatures, storms, forest fires), as well as human activity (sanitary clear cuttings, illegal logging), vegetation in the Chornobyl zone continues to grow and its areas will increase, although not so quickly.

Desertification Extraction Based on a Microwave Backscattering Contribution Decomposition Model at the Dry Bottom of the Aral Sea

The fine particles produced during the desertification process provide a rich material source for sand and dust activities. Accurately locating the desertified areas is a prerequisite for human intervention in sand and dust activities. In arid and semi-arid regions, due to very sparse vegetation coverage, the microwave surface scattering model is very suitable for describing the variation of topsoil property during the process of desertification. However, the microwave backscattering coefficient (MBC) trend of the soil during the desertification process is still unclear now. Moreover, the MBC of a resolution unit usually involves the contribution of soil and vegetation. These problems seriously limit the application of microwave remote sensing technology in desertification identification. In this paper, we studied the soil MBC change trend during the desertification process and proposed a microwave backscattering contribution decomposition (MBCD) model to estimate the soil MBC of a resolution unit. Furthermore, a simple microwave backscattering threshold (SMSBT) model was established to describe the severity of desertification. The MBCD and SMSBT models were verified qualitatively through landscape photos of sampling points from a field survey in November 2018. The results showed that the MBC would gradually decline with the deepening degree of desertification. The MBCD model and the corresponding least squares method can be used to estimate the soil MBC accurately, and the SMSBT model can accurately distinguish different degrees of desertification. The results of desertification classification showed that more than 68% of the dry bottom of the Aral Sea is suffering from different degrees of desertification.

A New Application of the Disturbance Index for Fire Severity in Coastal Dunes

Fires are a disturbance that can lead to short term dune destabilisation and have been suggested to be an initiation mechanism of a transgressive dune phase when paired with changing climatic conditions. Fire severity is one potential factor that could explain subsequent coastal dune destabilisations, but contemporary evidence of destabilisation following fire is lacking. In addition, the suitability of conventional satellite Earth Observation methods to detect the impacts of fire and the relative fire severity in coastal dune environments is in question. Widely applied satellite-derived burn indices (Normalised Burn Index and Normalised Difference Vegetation Index) have been suggested to underestimate the effects of fire in heterogenous landscapes or areas with sparse vegetation cover. This work assesses burn severity from high resolution aerial and Sentinel 2 satellite imagery following the 2019/2020 Black Summer fires on Kangaroo Island in South Australia, to assess the efficacy of commonly used satellite indices, and validate a new method for assessing fire severity in coastal dune systems. The results presented here show that the widely applied burn indices derived from NBR differentially assess vegetation loss and fire severity when compared in discrete soil groups across a landscape that experienced a very high severity fire. A new application of the Tasselled Cap Transformation (TCT) and Disturbance Index (DI) is presented. The differenced Disturbance Index (dDI) improves the estimation of burn severity, relative vegetation loss, and minimises the effects of differing soil conditions in the highly heterogenous landscape of Kangaroo Island. Results suggest that this new application of TCT is better suited to diverse environments like Mediterranean and semi-arid coastal regions than existing indices and can be used to better assess the effects of fire and potential remobilisation of coastal dune systems.

Digital mapping of soil carbon sequestration potential with enhanced vegetation cover over New South Wales, Australia

AbstractDigital soil maps of soil organic carbon (SOC) sequestration potential resulting from a hypothetical 10% relative increase in long‐term vegetation cover are presented at 100‐m resolution across the state of New South Wales (NSW) in southeast Australia. This land management outcome is considered realistically achievable for many land managers, using strategies such as revegetation, grazing management or crop residue management. A mean state‐wide potential increase of 5.4 Mg ha−1 over the 0‐ to 30‐cm depth interval was derived. Assuming a 20‐year period of re‐equilibration, this equates to an average SOC increase of 0.27 Mg ha−1 year−1. Sequestration potential is systematically influenced by a combination of climate, soil parent material and current vegetation cover, for example only 1.6 Mg ha−1 SOC under dry conditions in sandy, infertile soil material with sparse vegetation cover, compared with 15.9 Mg ha−1 under wet conditions in clay‐rich, fertile soil material with moderate–high vegetation cover. The outputs could be used to identify locations of highest sequestration potential and thereby help prioritize areas and inform decisions on sequestration programmes. Future application of the method at field scale with high levels of accuracy, together with strategic sampling, may provide statistically reliable estimates of carbon sequestration, for application in carbon trading schemes such as Australia's Emissions Reduction Fund. The modelling involved a conceptually transparent ‘space‐for‐time substitution’ process. Multiple linear regression (MLR) and random forest (RF) modelling techniques were applied, but only MLR gave consistently meaningful results. The apparent failing of RF in this application warrants further examination.