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Monitoring semi-natural grasslands is difficult and costly because they occur in highly dynamic and extremely complex habitat mosaics. We combined the use of a low-cost unmanned aerial vehicle (UAV) and Structure from Motion (SfM) photogrammetry to acquire high spatial resolution (∼1.5 cm pixel) RGB imagery. After image classification through Object-Based Image Analysis (OBIA), we accurately were able to distinguish three semi-natural grassland types, one of which is a habitat of conservation concern. The use of orthomosaics, digital elevation models (DEMs), and canopy height models (CHMs) yielded excellent overall classification accuracies (>89%) assessed through both remotely validated and ground-truthed points. We identified two layers of woody vegetation with a user's (UA) and producer's (PA) accuracies >73% and three grassland types: closed grassland (UA = 94%; PA = 97%), open grassland habitat (UA = 97%; PA = 93%) and open grasslands with soil erosion (UA = 96%; PA = 98%). The grassland types differed substantially in the cover of vegetation, rocks, stones, and bare soil measured in the field, as well as in the number and relative cover of the habitat diagnostic species. The proposed methodology is highly promising for mapping and monitoring semi-natural grassland of conservation concern in support of tailored management actions. [Display omitted] • Fine-scale mapping identifies three different semi-natural grassland classes. • High-resolution imagery and dense point clouds improve vegetation classification. • A useful tool for monitoring habitats of conservation concern. • Cost-effective and time-saving tool for frequent monitoring programs. [ABSTRACT FROM AUTHOR]

Reclaiming wetlands following open pit mining for industrial oil sand extraction is challenging due to the physical and chemical conditions of the post-mined landscape. The aim of our study was to examine and compare the influence of oil sands process water (OSPW) and material (fine fluid tails or FFT) on the plant community composition of created wetlands. Compared to created-unamended and natural wetlands, the created wetlands amended with OSPW and/or FFT (created-tailings wetlands) had significantly higher water salinity, conductivity, dissolved oxygen concentration and lower oxidative-reductive potential. Water chemistry parameters of created-unamended did not differ significantly from those of natural wetlands. The sediment of created wetlands had significantly less moisture, total nitrogen, and organic content than the natural wetlands. The application of OSPW/FFT in created wetlands will likely lead to initial vegetation composition atypical of natural regional wetlands. For the objective of reclaiming vegetation composition to the status of natural regional wetlands, unamended wetlands were the best reclamation option, based on the physical and chemical parameters measured. Despite being the favored reclamation option, created-unamended wetlands’ physical and chemical characteristics remain atypical of natural wetlands. Most significantly, the basin morphometry of created wetlands was significantly different from that of naturally-formed wetlands in the region, and this appears to partly explain difference in vegetation composition. We also demonstrate that species richness alone is not a useful measure in wetland monitoring. Instead, plant community composition is a better indicator of wetland conditions. [ABSTRACT FROM AUTHOR]

Accurate and up-to-date land cover maps inform and support effective management and policy decisions. Describing phenological changes in spectral response using time-series data may help to distinguish vegetation types, thereby allowing for more specificity within vegetation classification. In this research, we test this by classifying indigenous forest vegetation in New Zealand, using PlanetScope (PS) and Sentinel-2 (S-2) satellite time-series data. The study was undertaken in a podocarp forest in New Zealand's central north island, which was classified into nine land cover classes. Phenological features, based on S-2 imagery, were extracted, including the enhanced vegetation index (EVI), enhanced vegetation index 2 (EVI2) and normalised difference vegetation index (NDVI). Google Earth Engine (GEE) harmonic analysis and TIMESAT double logistic fitting function were used to extract phenological features. Pixel-based classifications were performed using a Neural Network on six different scenarios. The accuracy of the classification scenarios was determined and the importance score for each feature was evaluated. Using only the fused PS and S-2 bands, the land cover in the study area was classified with 90.1% accuracy. Adding phenological features increased the classification accuracy to 93.1%. When combined with VIs, texture features, and a digital terrain model, the addition of phenological features increased the classification accuracy to 96.6%. Including GEE-generated phenological features resulted in better classification accuracies than TIMESAT features. In terms of feature importance evaluation, EVI2- and NDVI-generated phenological features all had high scores; the effectiveness of EVI features could potentially have been limited by the quality of the blue band. The results demonstrate that it is possible to produce a more accurate classification of New Zealand's native vegetation by using phenological features. This method offers important cost-savings as the platforms for phenological analysis are free to use. • Sentinel-2 phenological features were extracted using Google Earth Engine harmonic analysis and TIMESAT double logistic fitting function. • Higher classification accuracy was achieved with the assistance of phenological features. • Phenological features generated from harmonic analysis were more suitable for native forest classification. [ABSTRACT FROM AUTHOR]

The increasing wildfire occurrence due to global climate changes urged the improvement of present wildfire growth prediction and evaluation methods. This study aimed to propose novel solutions to their two primary limitations, including the lack of robust fuel classification method and the low spatial resolution of wildfire growth accuracy assessment while ensuring wide applicability using open data satellite missions and software. The first objective was to create a robust two-step fuel model classification method consisted of the supervised machine learning classification of generalized land cover classes in the 1st level and their individual unsupervised classification to vegetation subtypes in the 2nd level. The second objective was creating a wildfire prediction accuracy assessment method using MODIS 250 m images, which overcome the limitations of low spatial resolution while preserving sub-daily temporal resolution. The wildfire on the Korčula island in Croatia was analyzed in the study, being specific for its long duration from 18 to 24 July 2015. The wildfire ignition occurred in the isolated area, which prolonged the response time from emergency agencies. Random Forest (RF) with input Landsat 8 spectral bands and indices resulted in the highest classification accuracy in the 1st classification level with an overall agreement of 83.6%. The vegetation subclasses from the 2nd classification level were matched to the 13 standard fuel models for the input in FARSITE software. The predicted wildfire evaluation showed the highest mean accuracy of 0.906 for the first two days, which decreased to 0.722 in the latter stages of the active wildfire caused by overprediction. The proposed two-step fuel model classification presented a cost-efficient solution to the fuel map creation in any part of the world, with a disadvantage of no in-situ ground truth identification and accuracy assessment for 2nd classification level. The evaluation of wildfire growth prediction with 250 m images enabled high spatial and temporal resolution of the assessment, while its limitations of wildfire overprediction and the negative effects of wildfire smoke in MODIS images should be addressed in future research. [Display omitted] • Wildfire growth was predicted and evaluated using Landsat and MODIS images. • The fuel model was classified using a robust two-step classification method. • Wildfire perimeters were evaluated using 250 m daily NDVI time series. • FARSITE prediction mean accuracy was 0.906 in the first two days of active wildfire. • Proposed cost-efficient methods support the decision-making in web services. [ABSTRACT FROM AUTHOR]

The mapping of habitats as defined by plant communities is a common component of the planning and monitoring of conservation management. However, there are major concerns about the subjectivity and risk of observer bias in most commonly used plant community mapping protocols. This study provides the first test of the consistency of habitat maps based on the mapping units defined by the National Vegetation Classification (NVC), the most widely used classification of plant communities used for habitat mapping on conservation sites in the UK. Seven surveyors mapped the same upland site within five weeks in summer 2008 and the spatial correspondence of the resulting maps was assessed. The NVC is a hierarchical classification and pair-wise spatial agreement between maps decreased with lower levels of sub-classification. The average area of agreement between maps was 77.6% at the habitat level, 34.2% at the community level and 18.5% at the sub-community level. Spatial disparity in the location of mapped boundaries between vegetation types only made a small contribution to overall differences; the majority of variation between maps was due to discrepancies in classification, with vegetation types containing similar species composition most often confused. Factors relating to surveyor effort (cost, time taken and length of route) were not able to explain the substantial differences between maps. However, the methods used to assign areas to vegetation type did seem to have an effect, with surveyors who relied primarily on their own experience having the highest levels of mean agreement with other maps. The study raises serious concerns with current practice of using the NVC for site description and monitoring/surveillance. Since this is just a single case study, we recommend that further work is carried out with the aim of determining the degree and source of variation between surveyors and how consistency can be increased. [Copyright &y& Elsevier]

Abstract: Little is known about the effects of grazing on vegetation composition on the Arabian Peninsula. The aim of this study therefore was to analyse the vegetation response to environmental conditions of open woodlands along an altitudinal and a grazing gradient in the Jabal al Akhdar mountain range of Oman. The species composition, vegetation structure, grazing damage and several environmental variables were investigated for 62 samples using a nested plot design. Classification analysis and a Canonical Variate Analysis (CVA) were used to define vegetation types and to identify underlying environmental gradients. The relationship between environmental variables and diversity was analysed using correlation coefficients and a main-effects ANOVA. The plant species richness followed a unimodal distribution along the altitudinal gradient with the highest number of species at the intermediate altitudinal belt. The cluster analysis led to five vegetation groups: The Sideroxylon mascatense–Dodonaea viscosa group on grazed and the Olea europaea–Fingerhuthia africana group on ungrazed plateau sites at 2000m a.s.l., the Ziziphus spina-christi–Nerium oleander group at wadi sites and the Moringa peregrina–Pteropyrum scoparium group at 1200m a.s.l, and the Acacia gerrardii–Leucas inflata group at 1700m a.s.l. The CVA indicated a clear distinction of the groups obtained by the agglomerative cluster analysis. The landform, altitude and grazing intensity were found to be the most important variables distinguishing between clusters. Overgrazing of the studied rangeland is an increasing environmental problem, whereas the plant composition at ungrazed sites pointed to a relatively fast and high regeneration potential of the local vegetation. [Copyright &y& Elsevier]

An accurate local biogenic volatile organic compound (BVOC) emission inventory in Shandong Province is crucial for air pollution control in Shandong and the Beijing-Tianjin-Hebei region, China. We estimated the multi-year isoprenoid emissions from natural vegetation in Shandong Province at a spatial resolution of 4 km × 4 km using the MEGAN2.1 model. A new vegetation classification with 23 plant species/types was developed, and emission factors were determined based on the most detailed and localized investigation and statistics. Isoprene, monoterpene, and sesquiterpene emissions in 2018 were 325.6, 18.2, and 7.9 Gg (mass of carbon), respectively. β-Pinene, α-pinene, ocimene, farnescene, and caryophyllene were the dominant monoterpenes and sesquiterpenes. Broadleaf trees contributed the most to total emissions, particularly poplar, which had the highest emission rates. Wheat also had higher emissions owing to its large coverage. Isoprenoid emissions displayed an inverted "U" pattern when plotted against the months and peaked in summer. Emissions were concentrated in the western and southeastern areas with emission intensities of >10 ton/grid, including Dezhou, Liaocheng, and Rizhao cities. During 1981–2018, isoprenoid emissions experienced a rapid increase from 12.0 to 351.7 Gg, at a rate of 11.20 Gg/yr. Isoprene had the highest enhancement rate of 10.72 Gg/yr. The most rapid increase was observed in the northwestern cities Dezhou and Liaocheng, and the southeastern cities Rizhao, at an average rate of >100 kg/yr, even >500 kg/yr in some areas. The high emissions and their continued increase should be considered when studying the prevention and control of regional air pollution and making policies in China. • Multi-year isoprenoid emission inventory at high resolution in Shandong is studied. • Shandong has biogenic isoprenoid emissions of 351.7 GgC. • Rapid enhancements of isoprenoid emissions during 1981–2018 are estimated. • Isoprenoid emissions increase at an average rate of 11.2 GgC/yr. [ABSTRACT FROM AUTHOR]

for quantifying vegetative cover across landscapes have, until recently, been limited to ground-based surveys or remote sensing via satellites or aircraft, both of which can limit the spatial scale of resulting data. Unmanned Aircraft Systems (UAS) can efficiently collect high-resolution sub-decimeter imagery of landscapes; geographic, object-based image analysis (GEOBIA) of the collected imagery can then be used to estimate vegetation cover. To date, few researchers have utilized open-source programs for GEOBIA. We developed GEOBIA methods in the open-source Program R to analyze visible spectrum UAS imagery from four sites in the Chihuahuan Desert of North America. These desert grasslands are difficult to quantify due to the patchiness of ground cover at small scales (e.g. <1 m) and the rarity of shrubs on the landscape. We used site-specific training data and multiple segmentation parameters to create vegetative and shrub cover data layers at a 15 cm resolution. We report overall accuracies of 77.2%–88.8% for vegetation classification and 95.7%–99.2% for shrub classification. Our work is some of the first to use open-source GEOBIA in grasslands and provides objective, reproducible data layers of desert vegetation, particularly shrubs, at the spatial scale necessary to inform management and conservation of Chihuahuan Desert grassland communities. • It is possible to use open-source, object-based methods to classify desert vegetation using Unmanned Aircraft Systems. • Site-specific training data and multiple segmentation parameters were used to create data layers at a 15 cm resolution. • Overall accuracies ranged 77.2%–88.8% for vegetation classification and 95.7%–99.2% for shrub classification. [ABSTRACT FROM AUTHOR]

Wetlands conservation and resilience capacities are key issues in many places over the globe. Understanding these issues will benefit from a precise knowledge of seagrass species occupancy and coverage over time and over space. Such information can be obtained from remote sensing images and their classification thanks to a vegetation index, to be used in a complementary manner to field work inventories. Sentinel-2 data, which are available with a frequent revisit time (<5 days) and a high spatial resolution (10m pixel size) can be used to map grassbeds at the surface or slightly below the surface of permanent lagoons, hence enabling the characterization of its seasonal dynamics, which was not possible with previous remote-sensing tools. We have proved the feasibility of such a method in the natural reserve of the Bagnas (Herault, France) where Stuckenia pectinata coverage can be tracked over a full year thanks to Sentinel-2 images and field work. Inter-annual dynamics (seasonal growth and senescence) can be mapped over time with 10m resolution and will be extended to pluriannual studies thanks to the long-term objective of the Sentinel-2 mission. This opens the way to a concerted management of natural reserves based on data analysis and field knowledge, a better understanding of seagrass coverage with fluctuating environmental conditions, and predictive mechanistic and/or stochastic models of future qualitative trends. • A detailed method to map seagrass beds in wetlands using remote sensing techniques is presented. • Two classification schemes from Sentinel-2 images are proved complementary to map seagrass beds in wetlands.. • The feasibility of the method in the natural reserve of the Bagnas is shown and it is transferable to other lagoons. • Interannual dynamics can be mapped over time with 10m resolution and can be extended to pluriannual studies. • Concerted management of wetlands based on data analysis and field knowledge will benefit from this method. [ABSTRACT FROM AUTHOR]

Globally, some vegetation has grown significantly over the past decades, but the climate benefits remain unclear, especially in the temperate regions. Understanding the biophysical effects and identifying the potential of vegetation will help to mitigate climate change. Here, we propose a vegetation-adjusted temperature index to understand how terrestrial vegetation growth in China affects the air temperature for 2001–2013, based on satellite-derived normalized difference vegetation index, near-surface air temperature (T a) and the land surface temperature. Grassland growth and cropland growth are found to cool the T a by −0.08 ± 0.32 °C (mean ± one standard deviation) and −0.06 ± 0.28 °C, respectively. Forest growth results in net climate warming by 0.05 ± 0.29 °C. Biophysical effects, elevation and background climate are used to explain the climate impacts of vegetation. Results show that the biophysical effects dominate the climate impact. More specifically, evapotranspiration (ET) controls the daytime climate impact, and at night, an indirect effect of albedo (the release of daytime heat storage) dominates it. Lower precipitation, temperature and elevation reinforce the warming potential. Moreover, the effects of albedo and ET on climate are nonlinear. During the day, although lower albedo absorbs more incoming radiation, it releases more extra heat per unit ET that can compensate for the increased incoming radiation. At night, the warming effect reflects the release of daytime heat storage. Overall, tropical vegetation growth (−0.04 ± 0.10 °C) and warm temperate vegetation growth (−0.08 ± 0.15 °C) achieve the climatic benefits. Overall, the method proposed contributes to quantitatively evaluate the role of afforestation plan on regional climate cooling, and provides some policy/practical implications for future afforestation projects. Future afforestation projects should carefully consider the biophysical process and background climate to mitigate climate change. • A vegetation-adjusted temperature index is proposed based on satellite data. • Different terrestrial vegetation types have different biophysical effects on climate. • Geographical location affects the biophysical effects of vegetation on climate. • Evapotranspiration controls the daytime climate impact. • An indirect effect of albedo (heat storage) controls the nighttime climate impact. [ABSTRACT FROM AUTHOR]

Salt marshes are areas of high conservation value encompassing diverse ecological gradients responsible for creating unique vegetation structure and composition. In complement to the large body of studies developing vegetation mapping methods through the use of remote sensing data, we tested for the possibility of developing a cost-effective method to map salt marsh vegetation as a basis for monitoring a French Nature Reserve. Using classical multivariate ordination and cluster analyses, accurate and ecologically relevant vegetation groups matching existing typologies were determined from a vegetation database collected for management and conservation rather than mapping purposes. This resulted in six distinct vegetation groups, which were mapped through the combination of the NIR spectral band and radiometric indices (NDVI and NDWI) derived from multispectral 2 m-resolution satellite images (Pleiades images). The addition of a LiDAR-derived digital elevation model (DEM) layer was also tested. The final classified map of the reserve based only on optical layers had an overall accuracy of 75.5% (Kappa coefficient of 0.71), with varying success between the different vegetation groups. The addition of the DEM slightly decreased map accuracy to 73.6% (Kappa of 0.68). Decreasing the number of records used for map training had detectable negative effects on map accuracy. This study demonstrated that using already existing field observations combined with only a few spectral bands and radiometric indices from multi-temporal multispectral images with a high spatial resolution can be used as a basis to aid in vegetation classification and mapping of saltmarsh habitats, with the goal of monitoring their dynamics. • A combination of field and remote sensing data was used to map saltmarsh vegetation. • Field vegetation records were not initially designed in mapping purpose. • Rather easily acquired and processed multi-temporal multispectral imagery was used. • Decreasing the number of records for map training decreased map accuracy. • The addition of the DEM slightly decreased map accuracy. [ABSTRACT FROM AUTHOR]