Monitoring Of Vegetation Changes Research Articles

Monitoring of vegetation changes based on RS monitoring in Changbai Mountain Nature Reserve

High vegetation cover helps maintain the ecosystem's stability and reduces the risk of soil erosion and natural disasters. Vegetation restoration is essential for the ecological value of Changbai Mountain Nature Reserve. In this study, the interannual changes in vegetation cover in Changbai Mountain Nature Reserve, China, from 2013 to 2022 were studied in depth based on Landsat remote sensing data and land use transfer matrix. The study includes interannual dynamic changes in vegetation types, quantitative analysis of interannual changes in surface vegetation types, and the land use transfer matrix analysis. In addition, the drivers of dynamic changes in vegetation types were analysed. The research indicates a notable advancement in ecological restoration within Changbai Mountain Nature Reserve between 2013 and 2022. There was a substantial reduction in the extent of barren land, which predominantly transitioned into grass and woodland areas. In particular, the year 2022 exhibited a significant increase in grassland area when compared to 2013, with a remarkable expansion of 27.18 square kilometers, primarily concentrated in the central-eastern sector of the study area. The findings of this investigation serve as a valuable scientific foundation for guiding ecological preservation and management decisions within Changbai Mountain Nature Reserve, contributing to a deeper comprehension of the evolving patterns in vegetation cover.

Google Earth Engine and Sentinel 1/2 data-based forest degradation monitoring of Sundarban Biosphere Reserve

Tropical cyclone was effect through biodiversity, environment-nature circumstances, mangrove deforestation; flood inundation and high salinity at worldwide landscape and regularly in coastal ecosystem. Indian coastal areas recorded numerous tropical cyclones were impact through nature-environment effects, like Titli, Fani, Amphan, Bulbul, and Yass. This analysis was to recognize flood inundation and LULC alteration regions because of tropical cyclones. The cloud computing-based Google Earth Engine (GEE) stage is applied for growth of the flood inundation regions assessment in the affected Indian coastal area. The Random Forest (RF) approach is applied for vegetation change monitoring and Sentinel-1 Synthetic Aperture Rader (SAR) of the pre- and post-cyclone is applied for the monitoring and mapping of cyclonic result on landforms. In the outcome, vegetation loss is 238.34 km2 (2015/2016), 181.79 km2 (2016/2017), 443.00 km2 (2017/2018), 22.64 km2 (2018/2019), 34.39 km2 (2019/2020), 118.51 km2 (2020/2021) and 134.24 km2 (2021/2022) because of those years happens numerous cyclones Fani (2019), Bulbul (2019), Amphan (2022) and Yass (2021). The NDVI of the examining years are 2016, 2017, 2018, 2019, 2020, 2021, 2022 highest values were 0.172, 0.726, 0.593, 0.831, 0.634, 0.568 and 0.387 respectively. South most parts of Ganga-delta were recording enormous alteration in certain periods; similarly, dynamics of mangrove forests are recorded. This analysis indicates that novel adaptation strategies, future disaster management and planning can help to reduce the mangrove deforestation. Mangrove plantation, river bank management and coastal planning is necessary to reducing the climate change effects. Those analyses can help to the future disaster management team, stakeholders and for awareness purpose.

Better localized predictions with Out-of-Scope information and Explainable AI: One-Shot SAR backscatter nowcast framework with data from neighboring region

Synthetic Aperture Radar (SAR) provides 10-m weather-independent global Earth surface observations for various tasks such as land cover land use mapping, water body delineation, and vegetation change monitoring. However, the application of SAR imagery has been limited to retrospective tasks by a “first event then observation” rule. Recent studies have proven the feasibility of a one-shot nowcast of backscatters of SAR imagery using meteorological driving forces, soil moisture, geomorphic factors, and previous SAR images collected for the target area. Although the approach is promising, spatial connectivity, more specifically, the influence of the status of surrounding areas of the target location has yet to be considered. To fill that gap, this study proposed two nowcasting frameworks that can integrate precipitation and soil moisture data collected from surrounding areas through spatial aggregation (SA) and by processing spatial series (SS), respectively. The catastrophic 2019 Central US Flooding was used as a case study with the goal of predicting backscatters of SAR imagery captured during the event. The results from SA, SS, and a framework that only considers localized input (S0) are compared against each other as well as with the benchmark performance created with persistence assumption. Results show that S0, SA, and SS outperform the benchmark. More importantly, considering data from upstream neighboring areas further improves prediction accuracy compared to S0 where only inputs from the target region are considered. The gradients of results considering/not considering neighboring data indicates that neighboring data can alter the model’s attention on each feature of the localized input matrix. The difference in gradients between SA and SS indicates the way the neighboring information is integrated also matters. The methodology proposed by this study can serve as a building block for more active usage of SAR imagery in forward-looking tasks such as early flood warning and response.

Suitability and limitations of ground-based imagery and thermography for long-term monitoring of vegetation changes in Victoria Land (continental Antarctica)

Antarctic vegetation has been recognized to be a valuable bio-indicator to track climatic and environmental changes through an accurate long-term monitoring. The extremely harsh climatic conditions of Antarctica, its limited logistical accessibility and remoteness encourage the substitution or integration of field surveys with remote sensing monitoring.Here we assess the applicability and limitations of ground-based remote sensing (visible imagery and thermography) for accurate long-term monitoring of vegetation changes in continental Antarctica with reference to: a) total vegetation coverage; b) cover of the dominant species; c) vegetation seasonality. We selected the three most widespread continental Antarctic vegetation types (high cover moss; low cover moss; low cover moss and/or lichen).For the total vegetation cover the best fitting with the field data was achieved by the fishnet grid analysis performed by the expert and by the RGB_sup analysis, the two methods providing the highest feasibility, especially for the high cover moss, while for the other vegetation types the remote sensing methods provided over- and/or under-estimations (including GEI, differently from the Arctic).Regarding the dominant species cover (%), none of the remote sensing methods provided suitable results, while we demonstrated that seasonality affects the quantification of total vegetation cover through remote sensing due to changes of vegetation temperature, hydration and activity, especially for moss vegetation, even analyzing mono-specific vegetation plots. This finding underlines the importance of the timing of the digital image acquisition, an issue that has never been addressed before in continental Antarctica.

Integrating Remote Sensing and Spatiotemporal Analysis to Characterize Artificial Vegetation Restoration Suitability in Desert Areas: A Case Study of Mu Us Sandy Land

One of the major barriers to hindering the sustainable development of the terrestrial environment is the desertification process, and revegetation is one of the most significant duties in anti-desertification. Desertification deteriorates land ecosystems through species decline, and remote sensing is becoming the most effective way to monitor desertification. Mu Us Sandy Land is the fifth largest desert and the representative area under manmade vegetation restorations in China. Therefore, it is essential to understand the spatiotemporal characteristics of artificial desert transformation for seeking the optimal revegetation location for future restoration planning. However, there are no previous studies focusing on exploring regular patterns between the spatial distribution of vegetation restoration and human-related geographical features. In this study, we use Landsat satellite data from 1986 to 2020 to achieve annual monitoring of vegetation change by a threshold segmentation method, and then use spatiotemporal analysis with Open Street Map (OSM) data to explore the spatiotemporal distribution pattern between vegetation occurrence and human-related features. We construct an artificial vegetation restoration suitability index (AVRSI) by considering human-related features and topographical factors, and we assess artificial suitability for vegetation restoration by mapping methods based on that index and the vegetation distribution pattern. The AVRSI can be commonly used for evaluating restoration suitability in Sandy areas and it is tested acceptable in Mu Us Sandy Land. Our results show during this period, the segmentation threshold and vegetation area of Mu Us Sandy Land increased at rates of 0.005/year and 264.11 km2/year, respectively. Typically, we found the artificial restoration vegetation suitability in Mu Us area spatially declines from southeast to northwest, but eventually increases in the most northwest region. This study reveals the revegetation process in Mu Us Sandy Land by figuring out its spatiotemporal vegetation change with human-related features and maps the artificial revegetation suitability.

China Data Cube (CDC) for Big Earth Observation Data: Practices and Lessons Learned

In the face of tight natural resources and complex as well as volatile environments, and in order to meet the pressure brought by population growth, we need to overcome a series of challenges. As a new data management paradigm, the Earth Observation Data Cube simplifies the way that users manage and use earth observation data, and provides an analysis-ready form to access big spatiotemporal data, so as to realize the greater potential of earth observation data. Based on the Open Data Cube (ODC) framework, combined with analysis-ready data (ARD) generation technology, the design and implementation of CDC_DLTool, extending the support for data loading and the processing of international and Chinese imagery data covering China, this study eventually constructs the China Data Cube (CDC) framework. In the framework of this CDC grid, this study carried out case studies of water change monitoring based on international satellite imagery data of Landsat 8 in addition to vegetation change monitoring based on Chinese satellite imagery data of GF-1. The experimental results show that, compared with traditional scene-based data organization, the minimum management unit of this framework is a pixel, which makes the unified organization and management of multisource heterogeneous satellite imagery data more convenient and faster.

Spatiotemporal changes and driving factors of vegetation in 14 different climatic regions in the global from 1981 to 2018.

Climate change affects the change of vegetation, and the analysis of vegetation change and its drivers in different globe climate zones is important for ecological conservation, energy balances, and climate change in different global climate zones. Based on the vegetation leaf area index (LAI) and climate factor datasets, this paper uses an integrated empirical model decomposition, sensitivity rate, contribution rate, and geographic detector analysis method to study the vegetation drivers and their changes in 14 different climate zones around the globe from 1981 to 2018. The results showed that (1) Vegetation changes were sensitive to precipitation and evapotranspiration in arid climate zones and to temperature and soil temperature in cold climate zones. In the tundra climate zone, the sensitivity of vegetation change to temperature was higher than that to precipitation and evapotranspiration. (2) Soil moisture has the highest contribution to vegetation change, and the areas with absolute contribution rates over 60% account for 50.26% of the total area of global vegetation cover. The areas with high contributions of temperature and soil temperature to the LAI are mainly distributed in the Northern Hemisphere, which indicates that temperature has a high contribution to vegetation change in low-temperature environments. (3) The areas with significant increasing trends for the global vegetation LAIs accounted for approximately 15.32% of the total global vegetation cover (slope ≥ 0.01), which are mainly located in equatorial savannahs with dry winters, warm temperate climates with dry winters, and warm temperate climates with fully humid climatic zones. (4) The LAIs were dominated by medium-high fluctuations and sustainable increasing changes, which accounted for 61.27% and 69.34% of the total global vegetation cover area, respectively. (5) Globally, the driving factors influencing LAI changes are specific humidity, temperature, soil temperature, evapotranspiration, precipitation, and soil moisture in descending order, with the largest interaction effect of specific humidity and soil moisture on LAI changes. This research provides a scientific basis for vegetation change monitoring, driving mechanisms, and ecological protection in different climate regions around the globe.

Monitoring of Vegetation Change using Lands at 8 Imagery and GIS Technique in Tehama Coastal Plain, Yemen

Monitoring of Vegetation Change using Lands at 8 Imagery and GIS Technique in Tehama Coastal Plain, Yemen

2000-2019年京津冀地区植被覆盖状况变化及驱动因素

PDF HTML阅读 XML下载 导出引用 引用提醒 2000-2019年京津冀地区植被覆盖状况变化及驱动因素 DOI: 10.5846/stxb202104070900 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点研发计划项目（2016YFC0501904）；生物多样性调查评估项目（2019HJ2096001006） Change of vegetation coverage and the driving factor in the Beijing-Tianjin-Hebei region from 2000 to 2019 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:近20年来中国和印度通过土地利用活动改变地表覆盖使植被变得更绿，京津冀地区是植被变得更绿的典型地区。收集京津冀地区2000-2019年MODIS增强型植被指数（EVI）、植被覆盖百分比数据，以及年平均温度和降水量等数据，分析该区近20年来自然植被和农田植被EVI变化过程和趋势，揭示其变化的驱动因素，结果显示2000-2019年京津冀地区自然植被和农田植被EVI显著增加，自然植被EVI增加速率是农田植被的1.8倍。99.51%的自然植被和96.95%的农田植被生长状况改善。2000-2019年京津冀地区自然植被EVI与年平均温度和年降水量相关性不显著。农田植被EVI与年平均温度显著相关，与年降水量相关性不显著。农田灌溉和年平均温度变化是农田植被EVI显著增加的主要驱动因素。近20年京津冀地区通过实施以造林为主的生态建设工程，自然植被生长覆盖状况呈现极显著变好。同时森林植被比非森林植被覆盖百分比增加趋势更明显。以造林为主的生态建设工程是京津冀地区自然植被生长覆盖状况显著变好的主要驱动因素。 Abstract:With the development of remote sensing technology, large-scale vegetation change monitoring becomes possible. It was found that global vegetation had become greener in last decades. China and India have changed the vegetation coverage through land use activities to make land greener in the past 20 years under climate change. The Beijing-Tianjin-Hebei region is a typical area where land has become greener. The remote sensing vegetation index was widely used in monitoring the coverage change of surface vegetation. In this study we collected MODIS Enhanced Vegetation Index(EVI) data, MODIS vegetation growth and coverage data, land use and land cover data of the Beijing-Tianjin-Hebei region from 2000 to 2019, as well as annual average temperature and precipitation data. Then we analyzed the annual average EVI variation process and trend of the natural vegetation and cropland vegetation of the study area in the past 20 years, and revealed the driving factor, with the expectation of providing a scientific basis for objectively evaluating the effectiveness of the Beijing-Tianjin-Hebei ecological construction and coordinated development, and building the regional ecological security. The results showed that the EVI of natural vegetation and farmland vegetation in the Beijing-Tianjin-Hebei region increased significantly from 2000 to 2019. The increase rate of natural vegetation was 6.96%/10 a, the rate of increase of farmland vegetation was 3.91%/10 a, and the rate of increase of natural vegetation EVI was 1.8 times that of farmland vegetation. The growth of 99.51% of natural vegetation and 96.95% of farmland vegetation had improved. From 2000 to 2019, the EVI of natural vegetation in the Beijing-Tianjin-Hebei region had no significant correlation with annual average temperature and annual precipitation. The EVI of farmland vegetation was significantly correlated with the annual average temperature, but not significantly related to the annual precipitation. The main reason was that the irrigation water for the farmland vegetation in the Beijing-Tianjin-Hebei region comes from groundwater and agricultural water for the South-to-North Water Diversion Project. The growth of the farmland vegetation was not correlated with the fluctuation of the annual precipitation in the area. The annual average temperature was directly related to the growth of farmland vegetation. Farmland irrigation and annual average temperature changes were the main driving factors for the significant increase in farmland vegetation EVI. In the past 20 years, the Beijing-Tianjin-Hebei region had implemented a number of ecological construction projects focusing on afforestation, and the growth and coverage of natural vegetation have shown a remarkable improvement. Statistics showed that in 2019, the total afforestation area in the Beijing-Tianjin-Hebei region was 5712.66 km2, accounting for about 2.62% of the total area of the region. The accumulated afforestation area in the Beijing-Tianjin-Hebei region from 2000 to 2019 was 84367.27 km2, and the afforestation area in the Beijing-Tianjin-Hebei region had increased year by year since 2006. At the same time, the percentage increase of forest vegetation coverage was more obvious than that of non-forest vegetation. The ecological construction project focusing on afforestation was the main driving factor for the remarkable improvement of the growth and coverage of natural vegetation in the Beijing-Tianjin-Hebei region. 参考文献 相似文献 引证文献

Changes in the grasslands of the Caucasus based on Cumulative Endmember Fractions from the full 1987–2019 Landsat record

Grasslands are important for global biodiversity, food security, and climate change analyses, which makes mapping and monitoring of vegetation changes in grasslands necessary to better understand, sustainably manage, and protect these ecosystems. However, grassland vegetation monitoring at spatial and temporal resolution relevant to land management (e.g., ca. 30-m, and at least annually over long time periods) is challenging due to complex spatio-temporal pattern of changes and often limited data availability. Here we assess both short- and long-term changes in grassland vegetation cover from 1987 to 2019 across the Caucasus ecoregion at 30-m resolution based on Cumulative Endmember Fractions (i.e., annual sums of monthly ground cover fractions) derived from the full Landsat record, and temporal segmentation with LandTrendr. Our approach combines the benefits of physically-based analyses, missing data prediction, annual aggregations, and adaptive identification of changes in the time-series. We analyzed changes in vegetation fraction cover to infer the location, timing, and magnitude of vegetation change episodes of any length, quantified shifts among all ground cover fractions (i.e., green vegetation, non-photosynthetic vegetation, soil, and shade), and identified change pathways (i.e., green vegetation loss, desiccation, dry vegetation loss, revegetation green fraction, greening, or revegetation dry fraction). We found widespread long-term positive changes in grassland vegetation (32.7% of grasslands), especially in the early 2000s, but negative changes pathways were most common before the year 2000. We found little association between changes in green vegetation and meteorological conditions, and varied relationships with livestock populations. However, we also found strong spatial heterogeneity in vegetation dynamics among neighboring fields and pastures, demonstrating capability of our approach for grassland management at local levels. Our results provide a detailed assessment of grassland vegetation change in the Caucasus Ecoregion, and present an approach to map changes in grasslands even where availability of Landsat data is limited.

Effect of climate and ecological restoration on vegetation changes in the "Three-River Headwaters" region based on remote sensing technology.

Surface temperature and precipitation are factors effecting vegetation growth. Vegetation coverage change is one of the important factors influencing global and regional climate change. Dynamic monitoring of vegetation change can reflect the trend of climate change to a certain extent. Three-River Headwaters are located in the hinterland of the Qinghai-Tibet Plateau. It has the characteristics of "high, cold, and dry" (higher altitude, cold and dry weather) and its ecosystem is fragile. In recent years, with the global climate change, a series of eco-environmental problems such as river flow cutoff, permafrost degradation, and vegetation destruction has occurred in the headwaters area, which are closely related to climate and vegetation changes. At the same time, in order to solve the problem of ecological environment degradation in the region, various ecological restoration policies have implemented. Several uncertainties in the relationship between vegetation and climate change in the Three-River Headwaters region. This study aims to find out the uncertainties. In this study, the spatial distribution of vegetation coverage was calculated by using NDVI (normalized difference vegetation index) from the first-level product of MODIS (moderate resolution imaging spectroradiometer) remote sensing data. Combining policy factors, the relationship between rainfall, surface temperature, and vegetation growth status were analyzed. The results show that during the study period (1948-2019), the temperature rose significantly and the rainfall increased especially after the implementation of ecological restoration policy (after 2000). Vegetation coverage increased year-by-year (2000-2015). The rainfall effect on surface temperature and vegetation growth, when the summer rainfall increased, the temperature decreased, leads to vegetation coverage decreased (for example, 2001, 2003, 2008 and 2011); the dependence of vegetation on rainfall has obvious lag in Three-River Headwaters in summer. In the years with suitable rainfall and higher temperature in summer, the vegetation grows better and the vegetation coverage increases. This is mainly because the Three-River Headwaters is located in the alpine zone, and vegetation growth is more dependent on temperature. The implementation of ecological restoration policy promotes vegetation coverage. Studying the impact of climate and policy factors on vegetation cover is of great scientific significance and practical value for understanding the ecological restoration mechanism in high cold and arid regions.

Threshold- and trend-based vegetation change monitoring algorithm based on the inter-annual multi-temporal normalized difference moisture index series: A case study of the Tatra Mountains

Numerous algorithms are used in remote sensing to detect changes in vegetation. Majority of them require several tunable parameters or can only detect abrupt forest disturbances. The aim of this study was to develop a new threshold- and trend-based vegetation change monitoring algorithm (TVCMA) that can detect abrupt and gradual changes in vegetation within forested and non-forested areas. To test the algorithm, the Polish and Slovak Tatra Mountains were used as the study area. Strong winds and bark beetle outbreaks (BBOs) are the primary causes of vegetation disturbances in this region. An annual time series of vegetation indices from 1984 to 2016 was used as the input. The long time span necessitated the use of scenes from the Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI). Fifty-one images were atmospherically and topographically corrected. The collected in situ data included the chlorophyll content, leaf area index, absorbed photosynthetically active radiation, and spectral signatures of non-forest vegetation, dwarf pine, and forest stands in 190 sample plots. To select the vegetation indices (VIs) most suitable for disturbance detection, ten satellite-based VIs were correlated with the acquired field data. The normalized difference moisture index (NDMI) was found to be more sensitive to vegetation disturbances and more resistant to data noise than any other tested index. The TVCMA uses two separate approaches, namely, thresholding, which indicates where and when the disturbances occurred, and a regression analysis, which presents the general trend in the time series for each pixel. The number of detected disturbances, the Spearman's correlation coefficient between the modeled trend line and satellite observations, and p-values were calculated. Different threshold values were tested to identify the value that yielded the most accurate results. By using 200 randomly selected validation points, we achieved an 83.3% producer's accuracy for disturbances (PAD), 46.3% user's accuracy for disturbances (UAD), and 97.8% overall accuracy (OA). These results confirm the potential of TVCMA for monitoring abrupt and gradual changes in vegetation. Moreover, the simplicity and data-driven character of the proposed algorithm make it suitable for multi-temporal analyses of other types of satellite data.

관리에 따른 마을비보숲의 식생 변화 - 진안 서촌 마을비보숲과 원연장 마을비보숲을 사례로 -

관리에 따른 마을비보숲의 식생 변화 - 진안 서촌 마을비보숲과 원연장 마을비보숲을 사례로 -

The Use of Indicator Species and Ecological Degradation Model for Condition Assessment in Turkey

Monitoring of vegetation change is essential to detect good management practices for the sustainable use of natural rangeland resources. An understanding of how the existing species in vegetation react to management methods is important for decision makers. For this reason, the vegetation changes should be determined by suitable techniques. The multivariate analysis as Detrended Correspondence Analysis and Principal Components Analysis was highly examined and its results were easily interpreted to draw conclusions. A relatively homogenous data set first, a matrix of 221 species and 44 sample sites in the province of Yozgat in the Central Anatolia Region in year 2010, was used by detrended correspondence analysis (DCA). The effects of environmental variation was minimized by this method (DCA) so that the major pattern of vegetation composition change was explained by grazing impact. Principal components analysis was performed to detect the distribution of sites along the first axis, and the distance off the x-axis. Fifteen species out of the promising 89 plant species were found to be indicators. These indicator species should be benefited for the vegetation change status and sustainable rangeland management under semiarid rangeland conditions. The fifteen plant species and bare ground showed clear responses along the first axis (grazing impact gradient), and became abundant over certain ranges of this axis.

Spectral Discrimination of Vegetation Classes in Ice-Free Areas of Antarctica

Detailed monitoring of vegetation changes in ice-free areas of Antarctica is crucial to determine the effects of climate warming and increasing human presence in this vulnerable ecosystem. Remote sensing techniques are especially suitable in this distant and rough environment, with high spectral and spatial resolutions needed owing to the patchiness and similarity between vegetation elements. We analyze the reflectance spectra of the most representative vegetation elements in ice-free areas of Antarctica to assess the potential for discrimination. This research is aimed as a basis for future aircraft/satellite research for long-term vegetation monitoring. The study was conducted in the Barton Peninsula, King George Island. The reflectance of ground patches of different types of vegetation or bare ground (c. 0.25 m 2 , n = 30 patches per class) was recorded with a spectrophotometer measuring between 340 nm to 1025 nm at a resolution of 0.38 n m . We used Linear Discriminant Analysis (LDA) to classify the cover classes according to reflectance spectra, after reduction of the number of bands using Principal Component Analysis (PCA). The first five principal components explained an accumulated 99.4% of the total variance and were added to the discriminant function. The LDA classification resulted in c. 92% of cases correctly classified (a hit ratio 11.9 times greater than chance). The most important region for discrimination was the visible and near ultraviolet (UV), with the relative importance of spectral bands steeply decreasing in the Near Infra-Red (NIR) region. Our study shows the feasibility of discriminating among representative taxa of Antarctic vegetation using their spectral patterns in the near UV, visible and NIR. The results are encouraging for hyperspectral vegetation mapping in Antarctica, which could greatly facilitate monitoring vegetation changes in response to a changing environment, reducing the costs and environmental impacts of field surveys.

Vegetation dynamics at the upper elevational limit of vascular plants in Himalaya.

A rapid warming in Himalayas is predicted to increase plant upper distributional limits, vegetation cover and abundance of species adapted to warmer climate. We explored these predictions in NW Himalayas, by revisiting uppermost plant populations after ten years (2003–2013), detailed monitoring of vegetation changes in permanent plots (2009–2012), and age analysis of plants growing from 5500 to 6150 m. Plant traits and microclimate variables were recorded to explain observed vegetation changes. The elevation limits of several species shifted up to 6150 m, about 150 vertical meters above the limit of continuous plant distribution. The plant age analysis corroborated the hypothesis of warming-driven uphill migration. However, the impact of warming interacts with increasing precipitation and physical disturbance. The extreme summer snowfall event in 2010 is likely responsible for substantial decrease in plant cover in both alpine and subnival vegetation and compositional shift towards species preferring wetter habitats. Simultaneous increase in summer temperature and precipitation caused rapid snow melt and, coupled with frequent night frosts, generated multiple freeze-thaw cycles detrimental to subnival plants. Our results suggest that plant species responses to ongoing climate change will not be unidirectional upward range shifts but rather multi-dimensional, species-specific and spatially variable.

Игорь Германович Левичев (в связи с 70-летием)

6 октября 2015 г. исполнилось 70 лет Игорю Германовичу Левичеву, к. б. н., старшему научному сотруднику отдела Гербарий сосудистых растений Ботанического института им. В. Л. Комарова РАН, широко известному монографу крупного рода Gagea и близких к нему родов, а также автору оригинальных гипотез в области эволюции морфологических структур однодольных. Его научный путь начался с экспедиций на Чукотку, Камчатку, в Забайкалье, Кызылкумы (Узбекистан), Бадхызский заповедник (Туркмения, 1972 г.). С осени 1972 г. И. Г. Левичев работал в Чаткальском заповеднике (Узбекистан), где создал основу мониторинга изменений растительного покрова в двух ключевых относительно мало измененных участках Узбекистанской части Западного Тянь-Шаня. Работал над созданием «Красной книги Узбекской ССР» (1984), «Определителя растений Средней Азии. Критический конспект флоры» (1987). В 1996 г. защитил кандидатскую диссертацию на тему «Род Gagea Salisb. Западного Тянь-Шаня». В содружестве с рядом немецких, итальянских и др. ученых Левичев опубликовал немало интересных результатов молекулярно-филогенетических исследований ви­дов рода Gagea (и близких к нему родов). Он также описал один новый род – Kharkevichia Levichev из родства Lloydia – Gagea (сем. Liliaceae). Игорь Германович – автор более 120 научных (и научно-популярных) работ, 53 из них – собственно по видам рода Gagea.

Application of Synthetic NDVI Time Series Blended from Landsat and MODIS Data for Grassland Biomass Estimation

Accurate monitoring of grassland biomass at high spatial and temporal resolutions is important for the effective utilization of grasslands in ecological and agricultural applications. However, current remote sensing data cannot simultaneously provide accurate monitoring of vegetation changes with fine temporal and spatial resolutions. We used a data-fusion approach, namely the spatial and temporal adaptive reflectance fusion model (STARFM), to generate synthetic normalized difference vegetation index (NDVI) data from Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat data sets. This provided observations at fine temporal (8-d) and medium spatial (30 m) resolutions. Based on field-sampled aboveground biomass (AGB), synthetic NDVI and support vector machine (SVM) techniques were integrated to develop an AGB estimation model (SVM-AGB) for Xilinhot in Inner Mongolia, China. Compared with model generated from MODIS-NDVI (R2 = 0.73, root-mean-square error (RMSE) = 30.61 g/m2), the SVM-AGB model we developed can not only ensure the accuracy of estimation (R2 = 0.77, RMSE = 17.22 g/m2), but also produce higher spatial (30 m) and temporal resolution (8-d) biomass maps. We then generated the time-series biomass to detect biomass anomalies for grassland regions. We found that the synthetic NDVI-derived estimations contained more details on the distribution and severity of vegetation anomalies compared with MODIS NDVI-derived AGB estimations. This is the first time that we have generated time series of grassland biomass with 30-m and 8-d intervals data through combined use of a data-fusion method and the SVM-AGB model. Our study will be useful for near real-time and accurate (improved resolutions) monitoring of grassland conditions, and the data have implications for arid and semi-arid grasslands management.

Vertical Height Errors in Digital Terrain Models Derived from Airborne Laser Scanner Data in a Boreal-Alpine Ecotone in Norway

It has been suggested that airborne laser scanning (ALS) could be used for operational monitoring of vegetation changes in the alpine tree line caused by climate change. Because the vegetation is low in such tree-less areas close to the alpine zone, the accuracy of the digital terrain model (DTM) becomes crucial for early detection of, e.g., pioneer trees representing an ongoing tree migration given that the height of the vegetation may be on the same order of magnitude as the DTM uncertainty. The goal of this study was to assess and exemplify the vertical height errors of DTMs derived from ALS data under varying flying altitudes and pulse repetition frequencies (PRF). Important effects in the analysis were local terrain form, terrain surface, ground vegetation height, and terrain slope, because they may be correlated with recruitment patterns of pioneer trees. Based on 426 ground control points collected in a boreal-alpine ecotone, a standard deviation of 0.07–0.08 m was found for the lowest flying altitudes and lowest PRFs. For the highest PRF the standard deviation was 0.13 m. There were statistically significant mean errors for the different terrain forms and ground vegetation heights (−0.11 to 0.13 m).

Effect of Alnus subcordata, Acer insigne and Sequoia sempervirens plantations on plant diversity in Hyrcanian forest of Iran

Gheibi F, Akbarinia M, Kooch Y. 2015. Effect of Alnus subcordata, Acer insigne and Sequoia sempervirens plantations on plant diversity in Hyrcanian forest of Iran. Biodiversitas 16: 10-15. Forest plantation is a common action in order to restore thedegraded forests in Hyrcanian forests of Iran. This study compares the plant biodiversity in four 25-year-old stands of plantation,adjacent understorey of alder (Alnus subcordata C. A. Mey.), maple (Acer insigne Boiss.), sequoia or red wood (Sequoia sempervirens(D. Don) Endl.) and mixed stand (maple and sequoia), located in Salmanshahr of Mazandaran Province, northern Iran. Research carriedout in, 10 sample plots with 20m × 20m area which taken by systematic-random in each plantation. All understorey species wereidentified, recorded and then the biodiversity indices (diversity, richness and evenness) were calculated. Our findings show that theplanted species had significant effects on understorey diversity. Statistical comparisons revealed that the highest and lowest diversity(Simpson and Shanon-Winer) and richness (Margalef and Menhinic) indices occurred in sequoia and alder stands, respectively. Theevenness indices (Camargo and Smith-Wilson) were significantly greater in maple, sequoia and mixed stands compared with the aldertype. As a conclusion, floristic change trends were different according to the planted tree species. A good understanding of thecomplexity of vegetation processes requires long-term monitoring of vegetation change.