Random point sampling to detect gain and loss in tree canopy cover in response to urban densification

Urban tree canopy cover (UTC) is a simple, and common, measure of urban forest resource. Urban infill development is likely to lead to losses in UTC under private tenure, at a time when local governments are setting ambitious targets to increase UTC overall. Simple, statistically rigorous methods are required to benchmark and track change in UTC, whilst identifying which land-use types or tenures experience change.We estimated UTC in six Melbourne suburbs in 2010 and 2015 by randomly sampling 2000 points across public land, public streetscapes and private land. We were able to detect a net change in UTC of <2% over five years to a 95% level of confidence. A significant net decrease in UTC (−2.4%) was only detected in one of the six suburbs. Two suburbs had a net increase in UTC by +2.7% over five years. On private land, there was often areas of UTC loss, but this was generally offset by canopy gain in other areas of the private realm as well as in streetscapes and public land. Losses in UTC on private land were mainly due to tree removal, with or without subsequent construction works.This study describes a simple, but statistically rigorous, method to quantify UTC change and the drivers of change in different land-use types and tenure. Despite studying two suburbs will high rates of infill development, only one suburb showed evidence of net UTC decrease. The ‘dynamic equilibrium’ in UTC, whereby canopy losses area approximately offset by concurrent canopy gain, means that ambitious targets being set by local governments to increase UTC may be difficult to achieve without changes in tree protection and infill development policy and planning.     

Enhancing pedotransfer functions with environmental data for estimating bulk density and effective cation exchange capacity in a data‐sparse situation

Soil bulk density (BD) and effective cation exchange capacity (ECEC) are among the most important soil properties required for crop growth and environmental management. This study aimed to explore the combination of soil and environmental data in developing pedotransfer functions (PTFs) for BD and ECEC. Multiple linear regression (MLR) and random forest model (RFM) were employed in developing PTFs using three different data sets: soil data (PTF‐1), environmental data (PTF‐2) and the combination of soil and environmental data (PTF‐3). In developing the PTFs, three depth increments were also considered: all depth, topsoil (<0.40 m) and subsoil (>0.40 m). Results showed that PTF‐3 (R²; 0.29–0.69) outperformed both PTF‐1 (R²; 0.11–0.18) and PTF‐2 (R²; 0.22–0.59) in BD estimation. However, for ECEC estimation, PTF‐3 (R²; 0.61–0.86) performed comparably as PTF‐1 (R²; 0.58–0.76) with both PTFs out‐performing PTF‐2 (R²; 0.30–0.71). Also, grouping of data into different soil depth increments improves the estimation of BD with PTFs (especially PTF‐2 and PTF‐3) performing better at subsoils than topsoils. Generally, the most important predictors of BD are sand, silt, elevation, rainfall, temperature for estimation at topsoil while EVI, elevation, temperature and clay are the most important BD predictors in the subsoil. Also, clay, sand, pH, rainfall and SOC are the most important predictors of ECEC in the topsoil while pH, sand, clay, temperature and rainfall are the most important predictors of ECEC in the subsoil. Findings are important for overcoming the challenges of building national soil databases for large‐scale modelling in most data‐sparse countries, especially in the sub‐Saharan Africa (SSA).     

基于GF-6卫星影像多特征优选的酿酒葡萄精准识别

多源遥感信息和特征优选是提高农作物识别精度的重要支撑，高分六号（GF-6）卫星作为首次引入红边波段的国产卫星，其丰富的光谱信息为作物识别提供了新的思路和解决途径。该研究基于宁夏回族自治区银川市永宁县2018年6月-2019年3月的GF-6数据，充分利用红边优势提取光谱特征、纹理特征和植被指数特征，构建多种特征组合方案，并根据随机森林算法对特征重要性进行度量，选取最优特征组合对酿酒葡萄进行精准识别。结果表明，与单一特征相比，多源遥感特征的增加显著改善了酿酒葡萄分类效果，其中，植被指数贡献程度最大，光谱特征次之；基于随机森林的优选特征组合分类效果最佳，其中，总体分类精度为94.15%，酿酒葡萄用户精度为94.23%，制图精度为92.59%；以实地调查的4个酒庄为验证区，将酿酒葡萄提取结果与统计数据进行对比，面积相对精度均在70%以上，其中优选特征结果相对精度在90%以上，研究结果将为国产卫星红边波段在植被分类和识别方面的应用提供数据参考。     

天然杨桦用材林高效经营技术研究

选择不同林分类型和立地条件,按不同经营密度分别设立固定标准地,开展立地类型划分和最适密度选择。同时,在固定标准地内,对林分生长性状、形质指标、干物质积累量、枯枝落叶分解量及养分归还量进行连续定位观测。结果表明:经营密度对林分生长、干物质积累和枯枝落叶分解影响较大,且以中等经营密度(0.7)效果最理想。在立地类型划分、最适经营密度选择、密度效应分析、径级-株数分布规律研究的基础上,针对不同培育目标,分别提出不同立地条件下天然杨桦(胶合板材、普通建筑材、纸浆材)用材林高效经营技术,为科学经营存量种质资源,最大限度提高森林生产力奠定理论基础。     

湘南典型残次阔叶林人工促进更新技术研究

于2003年在湖南省汝城县益将国有林场天然残次阔叶林内设置试验样地和对照样地各3个,采用林地清理、补植、补播等手段促进天然更新,并分别于2006年和2011年进行2次人工促进干预;同时,在6个样地内各设置3个样方,共计18个样方,分别于2003、2008、2013年冬,对幼树、幼苗、萌条的数量及地径进行统计,并对样方内胸径≥5 cm的立木进行每木调查。结果表明:种子补播保存率43%,幼苗补植保存率20%,平均保存率32%。树高≤30 cm且地径0.5 cm、31 cm≤树高≤50 cm且0.5 cm地径0.7 cm幼树的数量下降率分别是对照样地的17.5%、21.59%,树高≥51 cm且地径0.7 cm幼树的数量下降率是对照样地的121.66%。2008年单位面积蓄积量是对照样地的2.28倍,2013年达到3.80倍;经过10年更新,单位面积蓄积量是试验前的11.57倍,对照样地单位面积蓄积量仅是试验前的2.55倍;单位面积蓄积量是对照样地的4.54倍。人工促进天然更新所需的人工成本为1 860元/hm2,仅为同期人工营造杉木幼林所需人工成本5 310元/hm2的35%。     

基于随机森林的高寒草地地上生物量高光谱估算

草地地上生物量（Aboveground biomass,AGB）是衡量草地生产力的关键因素,准确测定草地AGB具有重要意义。高光谱因具有时效性强、不破坏草地等特点被广泛用于草地生理生态指标的测定。本研究提取和计算了海北试验站高寒草地冠层的原始光谱（Original spectrum,OR）反射率、一阶微分光谱（First derivative spectrum,FD）反射率、光谱位置面积参数（Spectral parameters of spectral position and area,PA）和植被指数（Vegetation indices,VI）4种不同类型的特征变量,使用连续投影算法（Successive projections algorithm,SPA）和递归特征消除算法（Recursive feature elimination,RFE）进行特征选择,采用随机森林算法（Random forest,RF）构建草地AGB估测模型。结果表明：在由4种特征变量分别构建的草地AGB估测模型中,基于VI的RF模型精度最高（测试集R²=0.70,RMSE=557.87 kg·ha^-1）,实测AGB与估测AGB的线性R²达到0.72;不同类型特征变量组合构建的草地AGB估测模型中,PA+VI组合的RF模型精度最高（R²=0.71,RMSE=548.97 kg·ha^-1）,实测AGB和估测AGB的线性R²达到0.73。     

小麦随机多交后代中抗条锈(Puccinia striiformis)性的选择

本试验用16个被认为可能具有水平抗性的小麦品种为基础材料,采用乙烯利化学杀雄或太谷核不育为手段进行随机多交,以相对抗病性综合指数(RRCI)表示抗病性强度,研究了小麦条锈病水平抗性的遗传力及人工选择下的遗传进度,结果指出:1)随机多交的后代系统中,出现了一些抗条锈性显著高于优良亲本的系统。2)抗条锈性的遗传力较高:化     

拖拉机－挂车机组非线性系统随机响应分析

推导了随机激励下参数频变系统的统计线性化方法。利用该方法对铁牛－６５４拖拉机挂车机组非线性系统进行了平稳激励下随机振动响应的计算，计算结果表明，把拖拉机挂车机组作为非线性系统来处理可显著地提高振动分析的精度。     

基于随机森林回归算法的小麦叶片SPAD值遥感估算

使用机器学习中的随机森林(RF)回归算法构建小麦叶片SPAD值遥感反演模型。以2010—2013年江苏地区试验点稻茬小麦3个生育期(拔节、孕穗、开花)的叶片为材料,结合我国自主研发的环境减灾卫星HJ-1对研究区域进行同步监测,分析了各生育期叶片SPAD值与8种植被指数间的相关性;以0.01水平下显著相关的植被指数作为输入参数,使用RF回归算法构建了每个生育期的小麦SPAD反演算法模型,即RF-SPAD模型,以支持向量回归(SVR)和反向传播(BP)神经网络算法构建的SVR-SPAD模型和BP-SPAD模型作为比较模型,以R2和均方根误差(RMSE)为指标,分析了每个生育期3个模型的学习能力和回归预测能力,结果表明:RF-SPAD模型在3个生育期都表现出最强的学习能力,R2和RMSE在拔节期分别为0.89和1.54,孕穗期分别为0.85和1.49,开花期分别为0.80和1.71;RF-SPAD模型在3个生育期的回归预测能力都高于BP-SPAD模型,高于或接近于SVR-SPAD模型,R2和RMSE在拔节期分别为0.55和2.11,孕穗期分别为0.72和2.20,开花期分别为0.60和3.16。     

拖拉机驾驶室可靠性疲劳试验研究

分析了拖拉机驾驶室的振动环境，提出了一种用定频正弦振动等效模拟窄带随机振动的试验方法，编制了拖拉机驾驶室的室内可靠性疲劳试验载荷谱，达到以较少的试验投入实现拖拉机驾驶室振动环境室内疲劳模拟试验的目的。