Palaeohydrology in climatological context: Developing the case for use of remote predictors in Australian streamflow reconstructions

The dry continent of Australia experiences frequent periods of devastating regional drought, making high quality palaeohydrological reconstructions essential for water resource management and planning. In other parts of the world tree-rings form a core component of such reconstructions. Yet for much of Australia, annually resolved palaeohydrological reconstructions derived from tree-rings have proven elusive. The island state of Tasmania in the far south is an important exception, with over 50 tree-ring chronologies available. Coupled ocean-atmosphere processes that drive precipitation across mainland Australia also influence Tasmanian precipitation. Here, we provide a basic analysis of how geographic relationships between important drivers of seasonal Tasmanian streamflow and potential streamflow predictors such as tree-ring chronologies may extend the spatial applicability of Tasmania's tree-ring predictors beyond the local context. We find clear geographically and seasonally defined patterns in Tasmanian streamflow that are reflected in the relationships between individual Tasmanian tree-ring chronologies and their relationships with streamflows. We find strong evidence that quality Tasmanian summer (DJF) streamflow reconstructions based on tree-rings are possible. Evidence also suggests that streamflow reconstructions for other seasons are also likely to be possible, especially as additional chronologies based on wood properties such as tracheid radial diameter, microfibril angle, density and cell wall thickness become available. Based on the relationships between streamflows and tree-ring chronologies, and between streamflows and major precipitation drivers across Australia, we conclude that Tasmanian tree-ring chronologies are likely to prove useful as predictors for seasonal streamflow reconstructions, particularly in eastern Australia. Successful streamflow reconstructions for a much broader portion of Australia than currently available will be invaluable for future water resource management and planning.     

Developing an Event Tree for probabilistic hazard and risk assessment at Vesuvius

Probabilistic characterizations of possible future eruptive scenarios at Vesuvius volcano are elaborated and organized within a risk-based framework. In the EXPLORIS project, a wide variety of topics relating to this basic problem have been pursued: updates of historical data, reinterpretation of previous geological field data and the collection of new fieldwork results, the development of novel numerical modelling codes and of risk assessment techniques have all been completed. To achieve coherence, many diverse strands of evidence had to be unified within a formalised structure, and linked together by expert knowledge. For this purpose, a Vesuvius ‘Event Tree’ (ET) was created to summarise in a numerical-graphical form, at different levels of detail, all the relative likelihoods relating to the genesis and style of eruption, development and nature of volcanic hazards, and the probabilities of occurrence of different volcanic risks in the next eruption crisis. The Event Tree formulation provides a logical pathway connecting generic probabilistic hazard assessment to quantitative risk evaluation. In order to achieve a complete parameterization for this all-inclusive approach, exhaustive hazard and risk models were needed, quantified with comprehensive uncertainty distributions for all factors involved, rather than simple ‘best-estimate’ or nominal values. Thus, a structured expert elicitation procedure was implemented to complement more traditional data analysis and interpretative approaches. The structure of the Vesuvius Event Tree is presented, and some of the data analysis findings and elicitation outcomes that have provided initial indicative probability distributions to be associated with each of its branches are summarized. The Event Tree extends from initiating volcanic eruption events and hazards right through to human impact and infrastructure consequences, with the complete tree and its parameterisation forming a quantitative synoptic framework for comprehensive hazard evaluation and mapping of risk impacts. The organization of the Event Tree allows easy updating, as and when new information becomes available.     

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On the ecology of Azolla filiculoides Lam. in Damietta district, Egypt

Azolla filiculoides Lam. is a tiny aquatic fern growing as free floating mats covering the stagnant water of the irrigation and drainage canals of Damietta district, Egypt. Ecological characteristics and distribution of the fern were investigated and results were discussed.Vegetation analysis of Azolla indicate that the most common associated species with Azolla are: Phragmites australis [presence (P) = 93.3%], Echinochloa stagnina (P = 70%), Eichhornia crassipes (P = 57%), Pluchea dioscoridis (P = 56.7%), Persicaria salicifolia (P = 26.7%), Phylla nodiflora (P = 13.3%) and Ludwigia stolonifera (P = 10%). Submerged species, e.g. Ceratophyllum spp. and Potamogeton spp. are completely absent in the stands dominated with solid mats of Azolla. This could be explained by that these mats prevent light penetration and oxygen which are required to submerged plants which in turn are decayed.The pH of the water where Azolla was recorded ranged from 7.1 to 9.0 (mean = 7.8), total dissolved salts ranged from 280 to 2600 mg/l (mean = 880 mg/l). The total phosphorus showed the range from 0.01 to 0.82 mg/l (mean = 0.22 mg/l). The ammonia-N is relatively high, varying from 2 to 18 mg/l (mean = 7.0 mg/l), however, the nitrate-N varied from 2.1 to 54.1 mg/l with a mean value of 20.5 mg/l.The fresh biomass of Azolla ranged from 114 to 4280 g/m² in irrigation canals and from 506 to 2760 g/m² in drainage canals. The dry biomass varied from 16 to 23 g/m² in irrigation canals. The range from 26 to 320 g/m² was obtained for drainage canals.     

Mapping beech (Fagus sylvatica L.) forest structure with airborne hyperspectral imagery

Estimating forest structural attributes using multispectral remote sensing is challenging because of the saturation of multispectral indices at high canopy cover. The objective of this study was to assess the utility of hyperspectral data in estimating and mapping forest structural parameters including mean diameter-at-breast height (DBH), mean tree height and tree density of a closed canopy beech forest (Fagus sylvatica L.). Airborne HyMap images and data on forest structural attributes were collected from the Majella National Park, Italy in July 2004. The predictive performances of vegetation indices (VI) derived from all possible two-band combinations (VI_(i,j) = (R_i − R_j)/(R_i + R_j), where R_i and R_j = reflectance in any two bands) were evaluated using calibration (n = 33) and test (n = 20) data sets. The potential of partial least squares (PLS) regression, a multivariate technique involving several bands was also assessed. New VIs based on the contrast between reflectance in the red-edge shoulder (756–820 nm) and the water absorption feature centred at 1200 nm (1172–1320 nm) were found to show higher correlations with the forest structural parameters than standard VIs derived from NIR and visible reflectance (i.e. the normalised difference vegetation index, NDVI). PLS regression showed a slight improvement in estimating the beech forest structural attributes (prediction errors of 27.6%, 32.6% and 46.4% for mean DBH, height and tree density, respectively) compared to VIs using linear regression models (prediction errors of 27.8%, 35.8% and 48.3% for mean DBH, height and tree density, respectively). Mean DBH was the best predicted variable among the stand parameters (calibration R² = 0.62 for an exponential model fit and standard error of prediction = 5.12 cm, i.e. 25% of the mean). The predicted map of mean DBH revealed high heterogeneity in the beech forest structure in the study area. The spatial variability of mean DBH occurs at less than 450 m. The DBH map could be useful to forest management in many ways, e.g. thinning of coppice to promote diameter growth, to assess the effects of management on forest structure or to detect changes in the forest structure caused by anthropogenic and natural factors.     

Accuracy assessment of the global forest watch tree cover 2000 in China

Inaccurate information on forest resources could hamper forest conservation, reforestation and sustainable management. Remote-sensing products have emerged as key tools in forest cover monitoring. The Global Forest Watch (GFW) dataset as an interactive remote sensing product, is now applied by more than 2 million users including researchers, conservationists and local communities for analyzing forest cover changes. The quality of this product varies spatially, and local validations are recommended before using the data for inventory and management tasks. Our study evaluated the accuracy and suitability of the GFW dataset for analyzing China’s forest cover. We conducted a validation based on a streamlined visual interpretation procedure using high-resolution optical imagery on Google Earth to map the uncertainties and inaccuracies of GFW Tree Cover 2000 in China. We then estimated China’s forest area after considering the data uncertainty, made a comparison with the data reported by the National Forest Inventory of China (CNFI) to understand where and how the land-based inventory differs from the presence/absence-based remote sensing data. The results showed that the overall accuracy of the GFW Tree Cover 2000 data reached 94.5 %. The user’s and producer’s accuracy of forest classification was 89.26 % and 82.13 %. The sample-based area estimation using GFW showed a larger forest area than the figure reported by CNFI in mainland China, while data discrepancy varied at provincial levels. The study provides a detailed performance assessment of GFW in terms of accuracy of defining forest, and we advise the consideration of data uncertainty in forest cover estimates for future forest management.     

Tree cover mapping based on Sentinel-2 images demonstrate high thematic accuracy in Europe

The spatial and temporal distribution of trees has a large impact on human health and the environment through contributions to important climate mechanisms as well as commercial, recreational and social activities in society. A range of tree mapping methodologies has been presented in the literature, but tree cover estimates still differ widely between the individual datasets, and comparisons of the thematic accuracy of the resulting tree maps are rather scarce. The Copernicus Sentinel-2 satellites, which were launched in 2015 and 2017, have a combination of high spatial and temporal resolution. Given that this is a new satellite, a substantial amount of research on development of tree mapping algorithms as well as accuracy assessment of said algorithms have to be done in the years to come. To contribute to this process, a tree map produced through unsupervised classification was created for six Sentinel-2 tiles. The agreement between the tree map and the corresponding national forest inventory, as a function of the band combination chosen, was analysed and the thematic accuracy was assessed for two out of the six tiles. The results show that the highest agreement between the present tree map and the national forest inventory was found for bands 2, 3, 6 and 12. The present tree map has a relative difference in tree cover between 8% and 79% compared to previous estimates, but results are characterised by large scatter. Lastly, it is shown that the overall thematic accuracy of the present map is up to 90%, with the user’s accuracy ranging from 34.85% to 92.10%, and the producer’s accuracy ranging from 23.80% to 97.60% for the various thematic classes. This demonstrates that tree maps with high thematic accuracy can be produced from Sentinel-2. In the future the thematic accuracy can be increased even more through the use of temporal averaging in the mapping procedure, which will enable an accurate estimate of the European tree cover.     

Tree species classification using UAS-based digital aerial photogrammetry point clouds and multispectral imageries in subtropical natural forests

Tree species composition of forest stand is an important indicator of forest inventory attributes for assessing ecosystem health, understanding successional processes, and digitally displaying forest biodiversity. In this study, we acquired high spatial resolution multispectral and RGB imagery over a subtropical natural forest in southwest China using a fixed-wing UAV system. Digital aerial photogrammetric (DAP) technique was used to generate multi-spectral and RGB derived point clouds, upon which individual tree crown (ITC) delineation algorithms and a machine learning classifier were used to identify dominant tree species. To do so, the structure-from-motion method was used to generate RGB imagery-based DAP point clouds. Then, three ITC delineation algorithms (i.e., point cloud segmentation (PCS), image-based multiresolution segmentation (IMRS), and advanced multiresolution segmentation (AMRS)) were used and assessed for ITC detection. Finally, tree-level metrics (i.e., multispectral, texture and point cloud metrics) were used as metrics in the random forest classifier used to classify eight dominant tree species. Results indicated that the accuracy of the AMRS ITC segmentation was highest (F₁-score = 82.5 %), followed by the segmentation using PCS (F₁-score = 79.6 %), the IMRS exhibited the lowest accuracy (F₁-score = 78.6 %); forest types classification (coniferous and deciduous) had a higher accuracy than the classification of all eight tree species, and the combination of spectral, texture and structural metrics had the highest classification accuracy (overall accuracy = 80.20 %). In the classification of both eight tree species and two forest types, the classification accuracies were lowest when only using spectral metrics, indicated that the texture metrics and point cloud structural metrics had a positive impact on the classification (the overall accuracy and kappa accuracy increased by 1.49–4.46 % and 2.86–6.84 %, respectively).     

Can regional aerial images from orthophoto surveys produce high quality photogrammetric Canopy Height Model? A single tree approach in Western Europe

Forest monitoring tools are needed to promote effective and data driven forest management and forest policies. Remote sensing techniques can increase the speed and the cost-efficiency of the forest monitoring as well as large scale mapping of forest attribute (wall-to-wall approach). Digital Aerial Photogrammetry (DAP) is a common cost-effective alternative to airborne laser scanning (ALS) which can be based on aerial photos routinely acquired for general base maps. DAP based on such pre-existing dataset can be a cost effective source of large scale 3D data. In the context of forest characterization, when a quality Digital Terrain Model (DTM) is available, DAP can produce photogrammetric Canopy Height Model (pCHM) which describes the tree canopy height. While this potential seems pretty obvious, few studies have investigated the quality of regional pCHM based on aerial stereo images acquired by standard official aerial surveys. Our study proposes to evaluate the quality of pCHM individual tree height estimates based on raw images acquired following such protocol using a reference filed-measured tree height database. To further ensure the replicability of the approach, the pCHM tree height estimates benchmarking only relied on public forest inventory (FI) information and the photogrammetric protocol was based on low-cost and widely used photogrammetric software. Moreover, our study investigates the relationship between the pCHM tree height estimates based on the neighboring forest parameter provided by the FI program.Our results highlight the good agreement of tree height estimates provided by pCHM using DAP with both field measured and ALS tree height data. In terms of tree height modeling, our pCHM approach reached similar results than the same modeling strategy applied to ALS tree height estimates. Our study also identified some of the drivers of the pCHM tree height estimate error and found forest parameters like tree size (diameter at breast height) and tree type (evergreenness/deciduousness) as well as the terrain topography (slope) to be of higher importance than image survey parameters like the variation of the overlap or the sunlight condition in our dataset. In combination with the pCHM tree height estimate, the terrain slope, the Diameter at Breast Height (DBH) and the evergreenness factor were used to fit a multivariate model predicting the field measured tree height. This model presented better performance than the model linking the pCHM estimates to the field tree height estimates in terms of r² (0.90 VS 0.87) and root mean square error (RMSE, 1.78 VS 2.01 m). Such aspects are poorly addressed in literature and further research should focus on how pCHM approaches could integrate them to improve forest characterization using DAP and pCHM. Our promising results can be used to encourage the use of regional aerial orthophoto surveys archive to produce large scale quality tree height data at very low additional costs, notably in the context of updating national forest inventory programs.     

秦岭南坡佛坪1789年以来1~4月平均温度重建

采用秦岭南坡陕西佛坪龙草坪、光头山、三个包及三官庙建立的秦岭冷杉、油松和铁杉树轮宽度、密度年表, 分析树木生长对气候要素变化的响应关系, 重建了佛坪1789年以来1~4月平均温度变化, 并对其进行了变化特征分析。结果表明, 各样点的树木生长明显受生长季前期温度影响, 呈较为显著的正相关关系。利用多个样点的年表并引入主分量分析方法建立回归方程, 显著地提高了回归方程的稳定性和显著性, 进而提高了重建结果的可信度。功率谱分析结果表明, 佛坪1~4月平均温度变化具有显著的3~4年周期。而滑动t检验、滑动F检验和Lepage检验结果表明, 18世纪后期以来于1896年前后发生了10年时间尺度的方差突变, 表现为温度波动幅度的变化。