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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]

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]