Environmental Change and the Sustainability of European Forests

Summary

It is some years now since forest decline was a major public concern in Europe and was one of the principal environmental issues around which international research programs were focused. A number of internationally coordinated activities were initiated in the 1980s and 1990s and have continued until now; these contributed significantly to our current understanding of forestry and to the way in which forestry policies have developed. In short, the concept of sustainable development has had an increasing influence and is now of immeasurable value in forest policy, with sustainable forest management well established as its guiding principle. This sequence of events is examined here. The extent to which understanding has advanced is remarkable; much has changed. Arguably this period gave the first indication of the extent to which forests and ecosystems globally are threatened by environmental change. On the basis of the last 20 years, it is tempting to conclude that we now have an effective institutional framework and have made excellent progress. However, some of the recorded ecosystem responses seem anomalous; there are surprises in system responses, e.g., the linkage between sulphur and nitrogen depositions and forest growth. Even more importantly some specific pollutant problems remain and will intensify, and climate change has become an environmental issue of overwhelming importance.     

A new galago species for South Africa (Primates: Strepsirhini: Galagidae)

The primate fauna of South Africa has historically been viewed as comprising three diurnal cercopithecoid taxa – chacma baboons (Papio ursinus), vervet (Chlorocebus pygerythrus) and samango monkeys (Cercopithecus albogularis) – and two nocturnal lorisoid species – the thick-tailed greater galago (Otolemur crassicaudatus) and the southern lesser galago (Galago moholi). Here we report the positive identification of a third galago species within South Africa’s borders: the Mozambique dwarf galago or Grant’s galago, Galagoides granti (Thomas and Wroughton, 1907). The taxon was previously held to be restricted to Mozambique, eastern Zimbabwe, Malawi and Tanzania, but we have also observed it in the sand forest of Tembe Elephant Park and the Tshanini Community Reserve, near the Mozambique border. The species was formerly mistaken for Galago moholi, erroneously (we believe) extending the range of the latter species into northern KwaZulu-Natal. In South Africa the two small galagos are unlikely to have overlapping ranges: Galago moholi prefers dry savanna woodlands, whereas Galagoides granti is apparently confined to dry sand forest. However, both species may coexist with the larger and more widespread Otolemur crassicaudatus, an inhabitant of moist savanna, forest edge and thicket. The true South African ranges of both small galago species need to be ascertained.     

Detection of Ecballium elaterium in hedgerow olive orchards using a low-cost uncrewed aerial vehicle and open-source algorithms

BACKGROUND

Ecballium elaterium (common name: squirting cucumber) is an emerging weed problem in hedgerow or superintensive olive groves under no tillage. It colonizes the inter-row area infesting the natural or sown cover crops, and is considered a hard-to-control weed. Research in other woody crops has shown E. elaterium has a patchy distribution, which makes this weed susceptible to design a site-specific control strategy only addressed to E. elaterium patches. Therefore, the aim of this work was to develop a methodology based on the analysis of imagery acquired with an uncrewed aerial vehicle (UAV) to detect and map E. elaterium infestations in hedgerow olive orchards.

RESULTS

The study was conducted in two superintensive olive orchards, and the images were taken using a UAV equipped with an RGB sensor. Flights were conducted on two dates: in May, when there were various weeds infesting the orchard, and in September, when E. elaterium was the only infesting weed. UAV-orthomosaics in the first scenario were classified using random forest models, and the orthomosaics from September with E. elaterium as the only weed, were analyzed using an unsupervised algorithm. In both cases, the overall accuracies were over 0.85, and the producer's accuracies for E. elaterium ranged between 0.74 and 1.00.

CONCLUSION

These results allow the design of a site-specific and efficient herbicide control protocol which would represent a step forward in sustainable weed management. The development of these algorithms in free and open-source software fosters their application in small and medium farms. © 2022 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.     

基于森林资源节约的出版规范改进研究

针对当前我国森林资源和文字规范工作的实际情况,在统计分析了森林资源流向、书刊发行现状的基础上,从节约资源的角度出发,应用工业工程的理论思想和方法对文字的规范标准及纸张节约等相关问题进行分析研究,提出了具有较强操作性和实际意义的改进方法。     

Prediction of pine mistletoe infection using remote sensing imaging: A comparison of the artificial neural network model and logistic regression model

In this study, the prediction of pine mistletoe distribution in Scots pine ecosystems was explored using remote sensing variables to compare the multilayer perceptron (MLP) artificial neural network (ANN) and logistic regression (LR) model performances. For this purpose, 109 sample plots were distinguished in pure Scots pine forests (natural) in the Eastern Black Sea Region of Turkey. Distinguishing mistletoe-infected stands (69) and uninfected stands (40) was performed with field observations. The variables acquired from Landsat 8 (Level 1) images were used as independent variables for independent-sample t-test, MLP ANN and LR models. Remote sensing variables indicated that mistletoe-infected stands were in drier areas with a lower vegetation-leaf area index. Based on the performance results of both models, the sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and accuracy of the MLP ANN model were superior to those of the LR model. The prediction percentages (SEN, SPE, PPV and NPV) of mistletoe-infected stands were better than the prediction percentages of uninfected stands. The prediction accuracies of LR and MLP ANN models were 74.3% and 89.6%, respectively. However, all remote sensing variables were included in the prediction equation of the MLP ANN model, while the thermal infrared 1 (TIRS1) variable was included in the LR model. In the MLP ANN model, the TIRS1 variable also had the highest normalized importance (100%). The area under the curve (AUC) value for identifying the mistletoe-infected stands of Scots pine forests used by the MLP ANN model (0.892 ± 0.034) was higher than in the LR model (0.838 ± 0.039), explaining the more accurate predictions obtained from the MLP ANN model. The MLP ANN model showed much better performance than the LR model. The results of this study are expected to make important contributions to the identification of potential mistletoe-infected areas.     

半固定沙地樟子松小叶锦鸡儿混交林营造及经营技术研究

在对嫩江沙地樟子松和小叶锦鸡儿人工混交林林分状况和生长指标调查的基础上,采用统计分析方法,探讨了混交林主辅林种间的生长消长关系,结果表明:随着林龄的增长,辅助树种小叶锦鸡儿对主栽树种樟子松的生长发育具有抑制作用,樟子松的生长发育进程减慢,应对小叶锦鸡儿林分进行人工干预,以促进樟子松的生长发育。     

千岛湖地区森林土壤类型与可持续利用研究

对千岛湖地区森林土壤类型及其分布进行了调查,结果表明:森林土壤有3个土类、5个亚类、10个土属。分布以黄红壤为主,占86.8%;钙质紫砂土次之,占7.8%;侵蚀性红壤占2.7%;石灰岩土占2.5%;黄壤面积最小,仅占0.2%。研究了不同林分类型的土壤理化性质,认为不同湖区林场的天然林土壤养分差异较大,从整个地区角度看各种林型间土壤养分无差异性,但从每个林场单独分析,无论是天然林,还是人工林,阔叶林地养分明显高于针叶林和混交林地。因而,实施针叶林阔叶化改造,对千岛湖地区林地的持续发展,提高其水源涵养、保持水土、景观生态等效益是有益的。     

相思树种更新木麻黄防护林对林地土壤化学性质的影响

通过对相思树种更新木麻黄防护林对沿海沙地土壤化学性质的分析,结果表明:与木麻黄林分相比,在沿海沙地后沿应用相思树种造林使沙地土壤中水溶性C l-和Na+含量降低,而使表层土壤中的水溶性Ca2+含量升高;相思树种能明显地改善沙地土壤中有效养分的供给,然而,对不同的相思树种来说,其改善程度有所差异;相思树种明显地增加了沿海沙地土壤中全氮和全碳的含量,且增加的量以沙地土壤的表层最为显著。     

基于超参数优化算法下的随机森林模型预测奶牛呼吸频率

奶牛呼吸频率是评估环境造成的奶牛热应激程度的重要指标之一。该研究基于随机森林（random forest,RF）算法提出了适用于生产条件下的奶牛个体呼吸频率准确预测模型,为了平衡模型精度与计算效率问题,利用遗传算法（genetic algorithm,GA）、差分进化（differential evolution,DE）算法、粒子群优化（particle swarm optimization,PSO）算法、贝叶斯优化（Bayesian optimization,BO）算法对模型超参数进行优化,并与网格搜索（grid search,GS）下的人工神经网络（artificial neural network,ANN）和极限梯度提升机（extreme gradient boosting,XGBoost）模型进行了对比分析。研究结果表明,使用融合环境参数的修正温湿指数（adjusted temperature-humidity index,ATHI）、时间区域、奶牛产奶量、泌乳天数、身体姿势以及胎次作为输入特征时,基准RF模型的预测性能最佳。在此基础上,4种智能优化算法下的RF模型性能优于GS-ANN和GS-XGBoost,其中BO-RF的综合性能最优,其决定系数、平均绝对误差、平均绝对百分比误差以及均方根误差分别为0.614、7.723、14.4%、9.737,超参数优化耗时约为DE-RF的1/220。特征重要性分析表明,输入因子对奶牛呼吸频率的影响程度不同,ATHI是影响力最高的因子,相对重要性（relative importance,RI）为0.71,其次是时间区域（RI=0.09）和奶牛产奶量（RI=0.07）。研究为奶牛生产、健康评价及牛舍环境精准调控提供了有效方法和基础。