Dynamics of Grassland Vegetation Composition across different Land-use Types on the Qinghai Tibet Plateau: Implications to Combat Grassland Degradation

The constant biotic and abiotic interventions on the Qinghai Tibet Plateau (QTP) are seriously degrading the grasslands and, at the same time, restricting the active ecosystem function and grassland vegetation distribution on the plateau. This research analyses the dynamics of grassland vegetation composition across three land uses and counties. The degree of grassland degradation was divided into four land-use types based, i.e., healthy grassland (HG), restored grassland (RG), moderately degraded (MD) grassland, and severely degraded (SD) grassland. About 32 plant species were recorded in Tiebujia county, 28 in Maqin county, and 18 in Maduo county. Results showed Poa crymophila, Polygonum sibiricum, Leontopodium nanum and Oxytropis falcatabunge as the most abundant grassland species in all land-uses and counties. The richness of species ranged from 8 to 12 species per land-use, suggesting low richness and diversity in restored and degraded grassland. A positive non-significantly mean change (p<0.05) was detected for richness and evenness indices while a negative mean change (p<0.05) was detected for Simpson and Shannon indices in the alpine meadow and steppe in both Maqin and Maduo county. The results imply that degradation affects grassland vegetation, health, and distribution across the QTP. Plant total cover for the healthy grassland covered far more areas than other land-uses. Urgent mitigation measures to halt grassland degradation and decline in plant vegetation composition on the plateau should be adopted.

Impacts of Nicotiana glauca Graham Invasion on the Vegetation Composition and Soil: A Case Study of Taif, Western Saudi Arabia

Invasive species are considered a serious problem in different ecosystems worldwide. They can compete and interfere with native plants, leading to a shift in community assembly and ecosystem function. The present study aimed to evaluate the effects of Nicotiana glauca Graham invasion on native vegetation composition and soil of the most invaded locations in the Taif region, Western Saudi Arabia, including Alwaht (WHT), Ar-Ruddaf (RDF), and Ash-shafa (SHFA). Plant species list, life span, life form, and chorotypes were assessed. Six locations highly infested with N. glauca shrubs were selected, and the morphological parameters of the shrubs were measured. Within each location, richness, evenness, relative density of species, and soil were measured either under the canopy of N. glauca shrubs or outside the canopy. Floristic analysis revealed the existence of 144 plant species, mainly perennial. The shrubs at the SHFA1 location showed the highest values of all measured morphological parameters. The WHT 1 location showed high richness and evenness, while the WHAT 2 location showed less richness and evenness. The invaded locations showed substantial variation in the community composition. Additionally, the effect of N. glauca on the understory species varied from competition to facilitation, where most of the understory species were inhibited. As an average of all locations, 65.86% of the plant species were recorded only outside the canopy of N. glauca. The vegetation analysis revealed that the SHFA location is more vulnerable to invasion that could be ascribed to its wide range of habitats and high disturbance. The soil–vegetation relationships showed significant variations among the studied locations regarding soil composition, and thereby showed a wide ecological range of the invasive shrubs N. glauca. Therefore, the invasion of N. glauca in the Taif region altered the species interactions, nutrients, and soil properties.

SiDroForest: A comprehensive forest inventory of Siberian boreal forest investigations including drone-based point clouds, individually labelled trees, synthetically generated tree crowns and Sentinel-2 labelled image patches

This data collection is an attempt to remedy the scarcity of tree level forest structure data in the circum-boreal region, whilst providing, as part of the data collection, adjusted and labelled tree level and vegetation plot level data for machine learning and upscaling practices. Publicly available comprehensive datasets on tree level forest structure are rare, due to the involvement of governmental agencies, public sectors, and private actors that all influence the availability of these datasets. We present datasets of vegetation composition and tree and plot level forest structure for two important vegetation transition zones in Siberia, Russia; the summergreen–evergreen transition zone in central Yakutia and the tundra–taiga transition zone in Chukotka (NE Siberia). The SiDroForest collection contains a variety of data mainly based on unmanned aerial vehicle (UAV) and field data collected from 64 vegetation plots during fieldwork jointly performed by the Alfred Wegener Institute for Polar and Marine Research (AWI) and the North-Eastern Federal University of Yakutsk (NEFU) during the Chukotka 2018 expedition to Siberia. The data collection consists of four separate datasets. The fieldwork locations are the anchors that bind the data types together based on the location of the vegetation plot. i) The first dataset (Kruse et al., 2021, https://doi.pangaea.de/10.1594/PANGAEA.933263) provides UAV-borne data products covering the 64 vegetation plots surveyed during fieldwork: including structure from motion (SfM) point clouds, point-cloud products such as Digital Elevation Model (DEM), Canopy Height Model (CHM), Digital Surface Model (DSM) and Digital Terrain Model (DTM) constructed from Red Green Blue (RGB) and Red Green Near Infrared (RGN) orthomosaics. Forest structure and vegetation composition data are crucial in the assessment of whether a forest is to act as a carbon sink under changing climate conditions. Fieldwork and UAV-products can provide such data in depth. ii) The second dataset contains spatial data in the form of points and polygon shape files of 872 labelled individual trees and shrubs that were recorded during fieldwork at the same vegetation plots with information on tree height, crown diameter, and species (van Geffen et al., 2021c, https://doi.pangaea.de/10.1594/PANGAEA.932821). These tree- and shrub-individual labelled point and polygon shape files were generated and are located on the UAV RGB orthoimages. The individual number links to the information collected during the expedition such as tree height, crown diameter and vitality provided in table format. This dataset can be used to link individual trees in the SfM point clouds, providing unique insights into the vegetation composition and also allows future monitoring of the individual trees and the contents of the recorded vegetation plots at large. iii) The third dataset contains a synthesis of 10 000 generated images and masks that have the tree crowns of two species of larch (Larix gmelinii and Larix cajanderi) automatically extracted from the RGB UAV images in the common objects in context (COCO) format (van Geffen et al., 2021a, https://doi.pangaea.de/10.1594/PANGAEA.932795). The synthetic dataset was created specifically to detect Siberian larch species. iv) If publicly available forest-structure datasets at tree level are rarely available for Siberia, even fewer ready-to-use tree and plot level data are available for machine learning approaches, for example optimised data formats containing annotated vegetation categories. The fourth set contains Sentinel-2 Level-2 bottom of atmosphere labelled image patches with seasonal information and annotated vegetation categories covering the vegetation plots (van Geffen et al., 2021b, https://doi.pangaea.de/10.1594/PANGAEA.933268). The dataset is created with the aim of providing a small ready-to use validation and training data set to be used in various vegetation-related machine-learning tasks. The SidroForest data collection serves a variety of user communities. First of all, the UAV-derived top of canopy structure information, orthomosaics and the detailed vegetation information in the labelled data set provide detailed information on forest type, structure and composition for scientific communities with ecological and biological applications. The detailed Land Cover and Vegetation structure information in the first two data sets are of use for the generation and validation of Land Cover remote sensing products in radar and optical remote sensing. In addition to providing information on forest structure and vegetation composition of the vegetation plots, parts of the SiDroForest dataset are prepared to be used as training and validation data for machine learning purposes. For example, the Synthetic tree crown dataset is generated from the raw UAV images and optimized to be used in neural networks. Furthermore, the fourth SiDroForest data set contains standardized Sentinel-2 labelled image patches that provide training data on vegetation class categories for machine learning classification with JSON labels provided. The SiDroForst data collective serves as a basis to add future data collected during expeditions performed by the Alfred Wegener Institute, creating a larger dataset in the upcoming years that can provide unique insights into remote hard to reach boreal regions of Siberia.

Using Hyperspectral Imagery to Characterize Rangeland Vegetation Composition at Process-Relevant Scales

Rangelands are composed of patchy, highly dynamic herbaceous plant communities that are difficult to quantify across broad spatial extents at resolutions relevant to their characteristic spatial scales. Furthermore, differentiation of these plant communities using remotely sensed observations is complicated by their similar spectral absorption profiles. To better quantify the impacts of land management and weather variability on rangeland vegetation change, we analyzed high resolution hyperspectral data produced by the National Ecological Observatory Network (NEON) at a 6500-ha experimental station (Central Plains Experimental Range) to map vegetation composition and change over a 5-year timescale. The spatial resolution (1 m) of the data was able to resolve the plant community type at a suitable scale and the information-rich spectral resolution (426 bands) was able to differentiate closely related plant community classes. The resulting plant community class map showed strong accuracy results from both formal quantitative measurements (F1 75% and Kappa 0.83) and informal qualitative assessments. Over a 5-year period, we found that plant community composition was impacted more strongly by weather than by the rangeland management regime. Our work displays the potential to map plant community classes across extensive areas of herbaceous vegetation and use resultant maps to inform rangeland ecology and management. Critical to the success of the research was the development of computational methods that allowed us to implement efficient and flexible analyses on the large and complex data.

Health assessment of vegetation composition in the reclamation area of PT Natarang Mining, Tanggamus Regency, Lampung Province

Reclamation is a recovery activity in mining areas, one of which is revegetation. The success of revegetation plants can be seen from the health of the composition and structure of the vegetation when the trees are six years old and over. This study aims to determine the health of the vegetation composition (location, type, and level of tree damage) in the reclamation area of PT Natarang Mining, Tanggamus Regency, Lampung Province. The location of this research is in the area around the main office of PT Natarang Mining, Way Linggo Forest, Bandar Negeri Semuong, Tanggamus Regency, covering an area of 2.97 Ha. The research was conducted through vegetation analysis using the circular plot method and assessing the health of the vegetation composition based on the damage index at the plot level. The results showed that tree vegetation obtained 12 species. The most common tree species found was Falcataria moluccana (Sengon) which amounted to 22 individuals. The most significant contribution of tree species indicated by the highest importance value index was Falcataria moluccana. The location of most damage found in most of the plots was at the bottom of the stem (code 3) and the bottom and top of the stem (code 4). Types of open wound damage (code 03) and gall rust (code 26) were the most dominant causes of damage, with an average damage rate of 35% and 22%, respectively. The health value of the vegetation composition has a value range of 1.20 – 2.00. The average health value of the vegetation composition is 1.50, which is included in the medium category.