Does high vegetation coverage equal high giant panda density?

高原型河谷城市具有特殊的自然地理与气候特征，生态环境脆弱，城市化引起的生态环境问题日益突出。植被作为其生态系统的载体，响应更加敏感。深入研究高原型河谷城市的植被变化及其影响因素，对促进西部大开发及城市化健康发展，建立良好的城市人居、生态环境具有重要的现实意义。西宁市作为典型的高原型河谷城市，植被覆盖在城市化与退耕还林（草）政策共同作用下变化明显。基于植被-不透水表面-土壤（V-I-S）模型，以西宁市城市规划区1995年与2009年两期landsat TM影像为数据源，利用线性光谱混合模型进行混合像元分解，获取研究区植被覆盖度的空间分布。通过整体分析、转移矩阵分析，格网分析等技术手段，研究植被时空变化特征并分别探讨川道与丘陵植被变化的影响因素。结果表明：研究期内，西宁城市规划区平均植被覆盖度维持在30%左右。2009年与1995年相比植被覆盖度出现下降，植被覆盖空间差异略有减小。在数量上，基本无覆盖、中覆盖、高覆盖等级呈增加趋势，低覆盖、全覆盖呈下降趋势。研究区西北部及西南部丘陵区植被覆盖整体趋于好转，主要由中低丰度植被等级变化而来，原因在于2000-2005年湟中县累计退耕还林（草）54.91km²，累计造林247.98km²，使研究区西北部植被覆盖等级提高，表明西宁市退耕还林（草）工程对于改善植被覆盖效果明显。同时丘陵区植被变化与气候影响趋势相同，表明其植被变化可能也受气候变化影响。城市扩展方向及强度对其周边植被覆盖的影响突出。市区快速扩张及农业退化使川道内中高丰度植被覆盖整体退化趋势明显。主要原因在于2000年后西宁进入快速发展期，城市用地规模迅速增大，川道内城市周边大量中高覆盖等级植被转变为基本无覆盖等级，造成植被退化。川道内城市区域植被变化与气候影响趋势相反，表明本文研究结果可能低估了城市化对川道内植被变化的影响幅度，相比气候影响，人为活动的影响更加强烈。研究区内植被覆盖等级的变化趋势为植被覆盖较差的等级（基本无覆盖和低覆盖）向高一级别发展，得益于退耕还林（草）工程；中等级别以上的覆盖等级出现一定程度的退化，尤其是位于川道中受到城市化干扰的区域植被退化问题尤为突出，需对这些区域采取植被保育措施，避免植被覆盖高等级区域受到城市化影响造成不可逆转的退化。;The geographical and climatic characteristics of Plateau Valley-City in the Western China are very typical and fragile. The urban ecosystems and environment have been damaged by urbanization recently years. Vegetation coverage plays a key role in the ecosystem of Plateau Valley-City, and it is very sensitive to urbanization. As a typical plateau Valley-City, Xining experienced rapid urbanization and implemented the policy of Converting Cultivated Land into Forest/Grass at the same time. Study on pattern dynamics of vegetation coverage of Xining urban plan zone and its causative factors is of great significance for promoting Western Development and urbanization, and contributes to creating a pleasant urban eco-environment in the Western China. Landsat TM data are the most economical and temporal continuous remote sensing images, however, vegetation pattern analysis on urban scale needs high resolution images. To resolve the problems and provide a more accurate result, Landsat TM data of 1995 and 2009 were unmixed by Linear Spectral Mixing Model (LSMM) in Xining plan zone to calculate proportion of vegetation cover, based on Vegetation-Impervious surface-Soil (V-I-S) model. Linear Spectral Mixture Model comprises five main processes: Minimum Noise Fraction (MNF), Pixel Purity Index (PPI), end-members collection by n-D visualizer, linear spectral unmixing and accuracy test. End-members include vegetation, high albedo surface, low albedo surface and soil. By using statistical analysis, transfer matrix and grid analysis, we evaluated the pattern dynamic of vegetation coverage, and discussed the effect of urbanization, Converting Cultivated Land into Forest/Grass and climate change to vegetation changes in each Valley terrace area and hills area. The results indicate that: at overall level, the average vegetation coverage kept about 30% and showed a downtrend in the study period, meanwhile the regional differences had a little decrease. The areas of no vegetation coverage, moderate and high abundance vegetation coverage showed increasing trend, while the areas of low and full abundance vegetation coverage showed decreasing trend. The vegetation coverage in northwestern and southwestern area emerged in an increasing trend and it was mainly from low and moderate vegetation coverage. Huangzhong County is the main area affected by the policy, and finished Converting Cultivated Land into Forest/Grass and afforestation of 54.91km² and 247.98km² from 2000 to 2005. It suggested that the policy was very effective. Vegetation dynamics of hills areas may be affected by climate change because that they had same trend to the vegetation dynamics effects of climate change in Tibetan Plateau. Vegetation coverage around urban built-up area changed obviously along with urbanization direction and intensity. Areas of moderate and high abundance vegetation coverage presented a degenerate trend, especially in the urban built-up area influenced by urbanization. The main reason is that urban construction land rapid increased after 2000 and occupied moderate and high vegetation coverage. Effects of human activities on vegetation coverage may be more intense comparing to climate change because that their effect trends on vegetation are opposite. The changing trend of vegetation coverage grade can be summarized that low grades developed to high grades because of converting cultivated land into forest/grass and medium grades presented a degenerate trend because of urbanization. It is necessary to take measures to protect the vegetation around built-up area, and avoid irreversible degradation.

Through long-term interactions with the natural environment, the ethnic groups in the mid-Himalayas have formed unique urban environmental characteristics. Effectively identifying urban environmental characteristics is a prerequisite for implementing sustainable urban management strategies. This study took 194 towns in the mid-Himalayan as the research objects. GIS was used to statistically analyze the terrain, climate, soil, and other environmental characteristics of the towns. The SOM (Self-organizing map) method was used to classify the comprehensive environmental characteristics of the towns. The results show that the main urban environmental characteristics in this area are low-altitude towns account for a large proportion, gentle-slope towns account for a small proportion, rainfall is mainly 125–265 mm, vegetation cover is dominated by high-coverage towns, mainly distributed in central and southern parts, the soil is dominated by embryonic soil and alluvial soil. The SOM method overcomes the subjectivity and low degree of automation in traditional research on urban environmental characteristics using threshold indicator methods or feature interpretation methods. Based on environmental characteristics, the towns were divided into six categories, and the classification results showed a distinct north–south zonal distribution pattern. There were significant differences in the environmental characteristics of towns in different clusters, such as the towns in cluster 5 had high altitudes, low rainfall, and low vegetation coverage, while the towns in cluster 2 had low altitudes, high rainfall, and high vegetation coverage. Finally, based on the SOM clustering results, governance strategies were proposed for towns in different clusters to cope with climate and environmental changes and promote sustainable development in the mid-Himalayan.

Each of the NDVI, EVI, NIRv, and kNDVI has varying strengths and weaknesses in terms of representing vegetation dynamics. Identifying the comparative advantages of these indices is crucial to objectively determine the dynamics of vegetation in dryland. In this study, Central Asia was selected as the research area, which is a typical drought-sensitive and ecologically fragile region. The Mann–Kendall trend test, coefficient of variation, and partial correlation analyses were used to compare the ability of these indices to express the spatiotemporal dynamics of vegetation, its heterogeneity, and its relationships with temperature and precipitation. Moreover, the composite vegetation index (CVI) was constructed by using the entropy weighting method and its relative advantage was identified. The results showed that the kNDVI exhibited a stronger capacity to express the relationship between the vegetation and the temperature and precipitation, compared with the other three indices. The NIRv best represented the spatiotemporal heterogeneity of vegetation in areas with a high vegetation coverage, while the kNDVI had the strongest expressive capability in areas with a low vegetation coverage. The critical value for distinguishing between areas with a high and low vegetation coverage was NDVI = 0.54 for temporal heterogeneity and NDVI = 0.50 for spatial heterogeneity. The CVI had no apparent comparative advantage over the other four indices in expressing the trends of changes in vegetation coverage and their correlations with the temperature and precipitation. However, it enjoyed a prominent advantage over these indices in terms of expressing the spatiotemporal heterogeneity of vegetation coverage in Central Asia.

Long-term remote sensing normalized difference vegetation index (NDVI) datasets have been widely used in monitoring vegetation changes. In this study, the NASA Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g dataset was used as the data source, and the dimidiate pixel model, intensity analysis, and residual analysis were used to analyze the changes of vegetation coverage in Inner Mongolia—from 1982 to 2010—and their relationships with climate and human activities. This study also explored vegetation changes in Inner Mongolia with respect to natural factors and human activities. The results showed that the estimated vegetation coverage exhibited a high correlation (0.836) with the actual measured values. The increased vegetation coverage area (49.2% of the total area) was larger than the decreased area (43.3%) from the 1980s to the 1990s, whereas the decreased area (57.1%) was larger than the increased area (35.6%) from the 1990s to the early 21st century. This finding indicates that vegetation growth in the 1990s was better than that in the other two decades. Intensity analysis revealed that changes in the average annual rate from the 1990s to the early 21st century were relatively faster than those in the 1980s–1990s. During the 1980s–1990s, the gain of high vegetation coverage areas was active, and the loss was dormant; in contrast, the gain and loss of low vegetation coverage areas were both dormant. In the 1990s to the early 21st century, the gains of high and low vegetation coverage areas were both dormant, whereas the losses were active. During the study period, areas of low vegetation coverage were converted into ones with higher coverage, and areas of high vegetation coverage were converted into ones with lower coverage. The vegetation coverage exhibited a good correlation (R2 = 0.60) with precipitation, and the positively correlated area was larger than the negatively correlated area. Human activities not only promote the vegetation coverage, but also have a destructive effect on vegetation, and the promotion effect during 1982 to 2000 was larger than from 2001 to 2010, while, the destructive effect was larger from 2000 to 2010.

This research utilized GeoSOS-FLUS model to simulate and predict the distribution of vegetation coverage grade in the Tibetan Plateau in the year 2035 under two scenarios: natural development scenario based on historical evolution projection, and ecological protection scenario based on limited transformation policy. First of all, future precipitation and maximum temperature are selected as the main driving factors, supplemented by other factors including relative humidity, sunshine hours, population spatial distribution, slope, slope aspect, elevation, distance to railway and distance to highway, etc.. The vegetation coverage grade of the Tibetan Plateau in 2003 is taken as the base period, and that in 2019 is taken as the end period. Then, a GeoSOS-FLUS prediction model is constructed, based on the cost matrix and neighborhood factors and other related parameters obtained from the two scenarios. Finally, the future vegetation coverage grade distribution in 2035 is predicted by using Markov chain.The results indicated that: ( 1 ) From 1998 to 2019, the bare land of the Tibetan Plateau has the tendency of been transformed into low vegetation coverage, medium vegetation coverage or medium-high vegetation coverage, whereas the area of high vegetation coverage is decreasing.( 2 ) Under the natural development scenario, the areas of the bare land, medium-high and high vegetation coverage of the Tibetan Plateau in 2035 will be reduced by about 2 % compared with the actual vegetation coverage in 2019; the areas of low and medium vegetation coverage will be increased by 2.8 % and 10.3 % respectively. Under this scenario, the vegetation coverage evolution of the Tibetan Plateau bears a positive trend, although the trend will be weakened compared to the historical period.( 3 ) Under the ecological protection scenario, compared with the natural development scenario, the areas of bare land and high vegetation coverage will decrease, and the area of low, medium and medium-high vegetation coverage of the Tibetan Plateau in 2035 will increase. Compared with the results predicted under the natural development scenario, under the ecological protection scenario, the improvement of future vegetation coverage of the Tibetan Plateau is very obvious, and the improvement is mainly contributed by the increase of medium vegetation coverage.

Taking Fu County, a typical area of Weibei dry plateau, as the research object, the normalized difference vegetation index ( NDVI ) was calculated by using Landsat 8 OLI remote sensing image. On this basis, the vegetation coverage in the study area was estimated and graded according to the binary pixel model, and the dynamic changes of vegetation coverage in the study area from 2013 to 2017 were quantitatively analyzed. Results showed that the overall NDVI value in the study area increased from 2013 to 2017. The vegetation cover in the study area is dominated by extremely high vegetation cover. The coverage area of low, medium and high vegetation cover decreases, and the coverage area of extremely high and extremely low vegetation cover increases. The coverage area of high vegetation and extremely high vegetation cover accounts for more than 75% of the study area. The study area showed a transfer trend of extremely low vegetation→low vegetation→medium vegetation→high vegetation coverage. The vegetation coverage generally developed to a good trend, but there was also a transfer of high vegetation coverage to medium vegetation coverage. The areas with the greatest changes in the study area are mainly concentrated in the northwest and near the southern Damagou, and the high NDVI area of Ziwuling National Nature Reserve has moved eastward and narrowed. Accuracy assessment indicates that the dynamic monitoring using the fused image time series produces results with relatively high accuracy. Bangladesh J. Bot. 52(2): 479-486, 2023 (June) Special

Taking Fu County, a typical area of Weibei dry plateau, as the research object, the normalized difference vegetation index ( NDVI ) was calculated by using Landsat 8 OLI remote sensing image. On this basis, the vegetation coverage in the study area was estimated and graded according to the binary pixel model, and the dynamic changes of vegetation coverage in the study area from 2013 to 2017 were quantitatively analyzed. Results showed that the overall NDVI value in the study area increased from 2013 to 2017. The vegetation cover in the study area is dominated by extremely high vegetation cover. The coverage area of low, medium and high vegetation cover decreases, and the coverage area of extremely high and extremely low vegetation cover increases. The coverage area of high vegetation and extremely high vegetation cover accounts for more than 75% of the study area. The study area showed a transfer trend of extremely low vegetation→low vegetation→medium vegetation→high vegetation coverage. The vegetation coverage generally developed to a good trend, but there was also a transfer of high vegetation coverage to medium vegetation coverage. The areas with the greatest changes in the study area are mainly concentrated in the northwest and near the southern Damagou, and the high NDVI area of Ziwuling National Nature Reserve has moved eastward and narrowed. Accuracy assessment indicates that the dynamic monitoring using the fused image time series produces results with relatively high accuracy. Bangladesh J. Bot. 52(2): 479-486, 2023 (June) Special

Analyzing vegetation cover provides a basis for detecting ecological and environmental health in urban areas. We analyzed the temporal and spatial changes in vegetation cover using NDVI data from the central Yunnan urban agglomeration (CYUA). The dimidiate pixel model (DPM) and intensity analysis were used to study changes at three levels: time intervals, category, and transition. Analysis of time series data from 1990–2020 using the Theil–Sen Median with Mann–Kendal test identified the overall trends. Geodetector explored the relationship between natural and human factors in vegetation cover change. The CYUA’s vegetation cover gradually decreases from west to east and south to north, with middle–high and high vegetation occupying over 55%. During 1990–2020, significant improvement was observed in the east and north regions, with an increase of 22.49%. The anthropogenic core area showed severe degradation with nearly 1.56% coverage. The transformation intensity of middle vegetation coverage was dominant from 1990–2010 but was replaced by middle–high vegetation coverage from 2010–2020. Meanwhile, high vegetation coverage became the most prominent gains target, and the conversion of middle–high to high vegetation showed a system tendency to exceed the average in absolute number and relative intensity. Spatial and temporal differences in vegetation cover were mostly affected by land cover (q = 0.4726, p < 0.001), and the most influential topographic factor was the slope (q = 0.1491, p < 0.001). The impact of human activities has increased to 16%, double that of 2000. The CYUA’s vegetation cover improved more than it degraded, but required site-specific forest management due to human activities.

Does high vegetation coverage equal high giant panda density?

Dynamic monitoring and analysis of the earthquake Worst-hit area based on remote sensing

As the main component of terrestrial ecosystem, vegetation plays a very important role in regional ecosystem environmental change, global carbon cycle and climate regulation. The Lower Mekong region (LMR) is at the core of Southeast Asia, its vegetation changes will affect the regional ecosystem and climate. Five countries of LMR were selected as the study area, based on MODIS (Moderate-Resolution Imaging Spectroradiometer) NDVI(Normal Difference Vegetation Index) data from 2000 to 2022, using the Sen’s slope estimator, Mann–Kendall trend test and geographic detector to study the spatial and temporal variation trends and driving forces of vegetation coverage. The results showed that:(1) From 2000 to 2022,the vegetation coverage in the LMR showed an overall fluctuating upward trend, the annual average Fractional Vegetation Cover(FVC) value was 0.70, mainly with high vegetation coverage and relatively high vegetation coverage. Vegetation distribution had obviously spatial heterogeneity, and the vegetation of Myanmar, Laos and Vietnam was significantly larger than Thailand and Cambodia.(2) The variation trend analysis of Sen_MK showed that the proportion of improved and degraded vegetation coverage areas in the LMR were 56.33% and 37.55% respectively. The vegetation improvement area was much larger than the vegetation degradation area during 2000–2022. According to the variation trend analysis of different countries, the vegetation coverage improvement area in Vietnam, Myanmar and Thailand were larger than the degraded , the overall vegetation coverage variation trend were good. However, in Laos and Cambodia, the degraded areas were larger than the improved, the overall variation trends of coverage were not good.(3) The results of geographic detector showed that the Land Use and Land Cover(LULC) had the greatest influence on vegetation coverage in the study area.The influencing factors of vegetation coverage were different in the LMR. For Vietnam, Thailand and Laos,elevation and slope factors were second only to LULC, for Myanmar and Cambodia, the influence of precipitation factor was second only to LULC. The results provide scientific data support for understanding the ecological environment status and future changes in the research area.

The rapid development of information technology has generated substantial data, which urgently requires new storage media and storage methods. DNA, as a storage medium with high density, high durability, and ultra-long storage time characteristics, is promising as a potential solution. However, DNA storage is still in its infancy and suffers from low space utilization of DNA strands, high read coverage, and poor coding coupling. Therefore, in this work, an adaptive coding DNA storage system is proposed to use different coding schemes for different coding region locations, and the method of adaptively generating coding constraint thresholds is used to optimize at the system level to ensure the efficient operation of each link. Images, videos, and PDF files of size 698 KB were stored in DNA using adaptive coding algorithms. The data were sequenced and losslessly decoded into raw data. Compared with previous work, the DNA storage system implemented by adaptive coding proposed in this paper has high storage density and low read coverage, which promotes the development of carbon-based storage systems.

To study the effects of biological soil crusts (BSCs) on hydrological processes and their implications for disturbance in the Mu Us Sandland, the water infiltration, evaporation and soil moisture of high coverage (100% BSCs), middle coverage (40% BSCs) and low coverage (0% BSCs, bare sand) of moss-dominated crusts were conducted in this study, respectively. The conclusions are as follows: (1) the main effects of moss-dominated crusts in the Mu Us Sandland on the infiltration of rainwater were to reduce the infiltration depths and to retain the limited rainwater in shallow soil; (2) moss-dominated crusts have no significant effects on daily evaporation when the volumetric water content at 4 cm depth in 100% BSCs (VWC4) was over 24.7%, on enhanced daily evaporation when the VWC4 ranged from 6.5% to 24.7% and on reduced daily evaporation when the VWC4 was less than 6.5%; and (3) decreasing the coverage of moss-dominated crusts (from 100% to 40%) did not significantly change its effects on infiltration, evaporation and soil moisture. Our results demonstrated that for the growth and regeneration of shrubs, which were dominated by Artemisia ordosica in the Mu Us Sandland, high coverage of moss-dominated crusts has negative effects on hydrological processes, and these negative effects could not be significantly reduced by decreasing the coverage of moss-dominated crusts from 100% to 40%. Therefore, for the sustained and healthy development of shrub communities in the Mu Us Sandland, it is necessary to take appropriate measures for the well-developed BSCs in the sites with high vegetation coverage in the rainy season. Copyright © 2015 John Wiley & Sons, Ltd.

Temporal and Spatial Changes Monitoring of Vegetation Coverage in Qilian County Based on GF-1 Image

While urbanisation contributes to global biodiversity declines, flower-rich urban habitats may provide beneficial pollinator habitats. We investigated the potential of urban residential areas to contribute to pollinator diversity by analysing wild bee and hoverfly species richness and composition of species assemblages of summer-active species, sampled in 53 gardens across urban and rural landscapes of Malmö, the regional capital of Sweden’s southernmost county. Species richness differed between urban and rural gardens, and between four urban residential types (ranging from low human density and high vegetation cover, to high human density and low vegetation cover), and taxonomic groups responded differently. Solitary bee species richness was higher in urban than rural gardens, driven by a higher richness in low-density urban gardens compared to both high-density urban gardens and rural gardens. In contrast, bumblebee species richness was higher in rural than urban gardens, whereas differences among the urban types were less clear. Hoverfly species richness was consistently higher in rural gardens than any urban garden type. Species richness of all groups was negatively related to human population density at the landscape scale (radius 500 m), but unrelated to vegetation cover. This indicates that population density affects pollinator habitat quality through associated green space management and design. Rural and urban wild bee species assemblages consisted of different species (significant species turnover), whereas urban hoverfly assemblages were a subset of rural ones (significant nestedness). Species nestedness of hoverflies, but not bees, increased with human population density. We show that urban areas can complement the regional wild bee species pool, mainly caused by large variation in tenure and management at small spatial scales, while urbanisation drives a systematic loss of hoverfly species. We suggest alternatives to improve dense residential areas for pollinators.