Estimation of biomass change in montane forests in Norway along a 1200 km latitudinal gradient using airborne laser scanning: a comparison of direct and indirect prediction of change under a model-based inferential approach

The current study compared two approaches for estimating change of aboveground biomass (AGB) in montane forests in Norway using field- and remotely sensed data from airborne laser scanning (ALS) from two points in time (four-year interval). The first was an indirect method that involved modeling and prediction of AGB at two points in time using ALS metrics as predictors, estimating the change from differences between AGB predictions. The second was a direct method, where change was modeled and predicted directly using differences between corresponding ALS metrics derived at the two measurement occasions as predictors, and the estimate was based on the predicted differences. Both methods were applied over a 1500?km long and 250?m wide transect from south to north in Norway comprising 250?m² grid cells. The results showed that the indirect method was more precise than the direct method. The indirect method estimated 0.65?Mg?ha^?1 change in AGB over the observation period, with a corresponding 95% confidence interval of ±0.27?Mg?ha^?1. The corresponding figures for the direct method was 0.54 and ±0.51?Mg?ha^?1. The direct method has been recommended previously. We conclude that the indirect method is both more precise and versatile.     

东莞市针叶类森林生物量遥感模型研究

基于 Landsat 8 影像数据,对东莞市松树林 (Pinus sp.)、杉木林 (Cunninghamia lanceolata)、针 叶混交林 3 种针叶类森林生物量进行估算,利用相关分析、主成分分析和逐步回归分析,建立针叶类森 林生物量遥感估算模型,其决定系数 (R2) 值分别为 0.880 9、 0.832 5、 0.964 0,均达显著水平。经适用性 检验,模型均达 0.05 显著水平,可用于东莞市针叶类森林生物量估算。     

Comparison of estimators and feature selection procedures in forest inventory based on airborne laser scanning and digital aerial imagery

Digital maps of forest resources are a crucial factor in successful forestry applications. Since manual measurement of this data on large areas is infeasible, maps must be constructed using a sample field data set and a prediction model constructed from remote sensing materials, of which airborne laser scanning (ALS) data and aerial images are currently widely used in management planning inventories. ALS data is suitable for the prediction of variables related to the size and volume of trees, whereas optical imagery helps in improving distinction between tree species. We studied the prediction of forest attributes using field data from National Forest Inventory complemented with ad hoc field plots in combination with ALS and aerial imagery data in Aland province, Finland. We applied feature selection with genetic algorithm and greedy forward selection and compared multiple linear and nonlinear estimators. Maximally around 40 features from a total of 154 were required to achieve the best prediction performances. Tree height was predicted with normalized root mean squared error value of 0.1 and tree volume with a value around 0.25. Predicting the volumes of spruce and broadleaved trees was the most challenging due to small proportions of these tree species in the study area.     

Effects of nitrogen,phosphorus, and potassium on SPAD-502 and atLEAF sensor readings of Salvia

There are various optical sensors in the market for precision nitrogen (N) management, which estimate leaf N status from chlorophyll content of leaves. However, readings may also be affected by the amounts of other nutrients in the plant leaves like potassium (K) and phosphorus (P), as well as sampling location within a plant or leaf. SPAD-502 and atLEAF optical sensors were used on Salvia ‘Vista Red’ plants grown with 0N-7.9P-0K, 41N-0P-0K, 0N-0P-31.6K, 0N-7.9P-0K + 41N-0P-0K, 0N-7.9P-0K + 0N-0P-31.6K, 41N-0P-0K + 0N-0P-31.6K, or 0N-7.9P-0K + 41N-0P-0K + 0N-0P-31.6K fertilizer plus a control. Both sensors were correlated with leaf N and each other. However, both sensor readings were affected by the presence or absence of P and K in the leaves, and thus these nutrients should be analyzed and reported along with leaf N values. Sensor readings were found to vary within a leaf and within leaf canopy location, so sampling needs to be consistent.     

不同光谱植被指数反演冬小麦叶氮含量的敏感性研究

【目的】氮素是作物生长发育过程中最重要的营养元素之一,研究叶氮含量反演的有效光谱指标设置,为应用高光谱植被指数反演作物叶氮含量,以及作物的实时监测与精确诊断提供重要依据。【方法】以冬小麦为例,选取涵盖冬小麦全生育期不同覆盖程度225组冠层光谱与叶氮含量数据,通过遥感方法建立模型,模拟了不同光谱指标,即中心波长、信噪比和波段宽度对定量模型的影响,通过模型精度评价指标决定系数(coefficient of determination,R~2)、根均方差(root mean square error,RMSE)、平均绝对误差(mean absolute error,MAE)、平均相对误差(mean relative error,MRE)和显著性检验水平(P0.01)确定最优模型及最佳指标,分析光谱指标对叶氮含量定量模型反演的敏感性和有效性。【结果】反演冬小麦叶氮含量的最佳植被指数为MTCI_B,与实测叶氮含量的相关性最好(R~2=0.7674,RMSE=0.5511%,MAE=0.4625%,MRE=11.11个百分点,且P0.01),对应的最佳指标为中心波长420 nm、508 nm和405 nm,波段宽度1 nm,信噪比大于70 DB;高覆盖状况反演的最优指数为RVIinf_r(R~2=0.6739,RMSE=0.2964%,MAE=0.2851%,MRE=6.44个百分点,且P0.01),最优中心波长为826 nm和760 nm;低覆盖状况反演的最优指数为MTCI(R~2=0.8252,RMSE=0.4032%,MAE=0.4408%,MRE=12.22个百分点,且P0.01),最优中心波长为750 nm、693 nm和680 nm;应用最适于高低覆盖的植被指数RVIinf_r和MTCI构建的联合反演模型(R~2=0.9286,RMSE=0.3416%,MAE=0.2988%,MRE=7.16个百分点,且P0.01),明显优于最佳单一指数MTCI_B;模拟Hyperion和HJ1A-HSI传感器数据,联合反演模型精度(R~2为0.92—0.93,RMSE在0.37%—0.39%,MAE为0.285%左右,MRE约为7.00个百分点)明显优于单一植被指数反演精度(R~2为0.79—0.81,RMSE为0.63%—0.66%,MAE为0.455%左右,MRE约为10.90个百分点)。【结论】利用高光谱植被指数可有效实现作物叶氮含量反演,作物叶氮含量定量反演对不同光谱指标—中心波长、信噪比和波段宽度,具有较强敏感性。应用多指数联合反演模型,可显著提高反演精度,并且联合反演模型在不同高光谱传感器下有一定普适性。     

基于AOD数据的新疆大型露天煤炭开采区PM2.5和PM10反演

MODIS气溶胶产品AOD与PM2.5、PM10浓度高度相关,已广泛应用在PM2.5、PM10浓度模拟.该研究以新疆维吾尔自治区大型露天煤炭开采区准东矿区为研究对象,结合实测的2014年5月、7月、9月、12月PM2.5、PM10质量浓度数据与经过垂直湿度订正的MODIS气溶胶产品AOD,利用多元回归进行拟合建模,从建立的40个模型中选取最优模型并据此对研究区PM2.5、PM10的质量浓度进行定量估算.结果表明:AOD与PM2.5、PM10呈极显著正相关;4个月AOD与PM2.5、PM10质量浓度估算模型最优模型均为多项式模型;其中7月AOD与PM2.5质量浓度拟合模型较好(R2=0.6258),实测值与预测值拟合趋势线R2为0.8057;9月PM10拟合模型效果理想(R2=0.7329),实测值与预测值拟合趋势线R2为0.8077;将AOD代入最优模型反演PM2.5,从空间层面上反映出各区域PM2.5浓度差异明显.研究结果可为AOD的深度利用与PM2.5、PM10浓度的遥感估算提供参考,在大气污染物空间分布、监测大气环境质量、污染预测等方面都具有重要意义.     

基于温度感知RFID标签的冷链厢体中温度监测

针对温度感知RFID(radio frequency identification)标签应用于冷链物流温度监测中缺乏有效数据验证的问题,该研究通过将42个温度感知RFID标签部署于冷链模拟平台中,划分了7个横截面、3个纵截面和两个层,设置了机械降温-冷链维持-自然回温3个不同阶段,同时在42个监测位点中选择7个位点同步部署了便携式温度记录仪,获取了不同条件下的温度监测数据,并与便携式温度记录仪数据和CFD(computational fluid dynamics)模拟数据进行了比较。7个温度感知RFID标签与便携式温度记录仪同步监测位点的数据表明,两种监测方法温差分布于±0.5℃范围内的数据点最多,占43.6%,温差分布于-1.0~-0.5℃区间的数据占了24.6%,考虑到2种设备自身的温度采集精度,温差在±0.8℃范围内可接的,其比例占71.3%,因此利用温度感知RFID标签进行冷链温度监测是可行的。对42个位点在3个不同阶段的温度监测数据表明,机械降温阶段各位点用时在1 h以内、冷链维持阶段大部分位点表现出温度在在0~4℃之间振荡的特征、自然回温阶段用时约49 h。深入分析机械降温阶段及冷链维持阶段不同截面的温度监测数据,结果表明3种截面均表现为降温初始阶段温度差值不稳定、稳定后具有明显的分布特征且离出风口较近降温较快的特点。以横截面2和横截面6平均温度为例,将温度感知RFID标签数据采集数据与CFD模拟数据进行比较,去除测量精度的干扰,截面2的均方根误差为0.73℃、平均相对误差为13.58%、截面6的均方根误差为0.56℃、平均相对误差为10.94%,具有较好的空间一致性。研究结果可为实现冷链物流中低成本、连续的温度监测奠定基础。     

基于TVDI和气象数据的陕西省春季旱情时空分析

陕西省居于中国腹地,受干旱影响严重,开展遥感旱情监测工作,认识旱情的时空特征具有重要的现实意义。利用MODIS产品MOD11A2和MOD13A2构建NDVI-Ts特征空间,获取陕西地区温度植被干旱指数(Temperature Vegetation Dryness Index,TVDI),在利用土壤湿度和气象指数验证了TVDI反映旱情有效性的基础上,以TVDI为干旱指标分析了2000—2015年陕西省春季(2—5月)旱情的时空变化特征。结果表明:(1)土壤相对湿度和降水量与TVDI均呈负相关,TVDI对降水量存在一定的滞后性;(2)近16年来,陕西省年年有旱,但春季旱情总体呈逐年减轻的趋势,年内2—5月旱情会逐渐加重;(3)空间上,近16年来陕西省春旱较为严重的地区主要分布在陕北北部以及关中东北部,而陕北南部、关中西南部及陕南地区旱情较轻。     

激光传感器在喷雾靶标检测中的研究应用

树木尺寸、形状等特征信息的获取,是现代化农业中重要的环节,为农林管理中的变量精密喷雾提供依据。为了实现对树木靶标信息的精确测量,搭建了基于UTM-30LX型激光传感器的室内滑台靶标检测系统,实现对规则物体、仿真树的扫描检测,通过编写的C++程序,实时读取数据信息并存储于控制计算机中。利用MATLAB软件对存储的数据信息进行后期的离线分析,重构物体三维图像,通过最大相对误差、轮廓相似度两个参数,分析激光传感器检测得到靶标目标尺寸、重构目标三维图像的精确度。以长方体柜子、圆柱体泡沫、2棵仿真树为试验对象,按照选取的3个检测距离和4个检测速度分别对4个试验对象进行检测与数据分析。试验结果显示,多次试验下,与人工测量尺寸比较而言,激光传感器测得的长方体柜子长度、宽度和高度尺寸最大相对误差分别为6.76%、6.86%和3.92%,圆柱体泡沫的高度、直径尺寸最大相对误差分别为4.25%和7.33%,仿真树1高度、宽度尺寸最大相对误差分别为4.06%和5.91%,仿真树2高度、宽度尺寸最大相对误差分别为3.24%和4.00%;仿真树1、2的激光扫描重构图像与对应实物轮廓相似度最小值分别为0.928 8和0.932 6。研究表明,UTM-30LX型激光传感器在各种试验条件下对多种试验靶标特征检测均具有较好的精确性和准确性,可以担负变量喷雾检测的任务,具有应用到农业生产中的前景。     

克雷伯氏肺炎杆菌群体感应系统缺失对合成2,3丁二醇的影响

通过同源重组技术敲除克雷伯氏肺炎杆菌的luxS基因,使其群体感应系统失活,研究突变株在2,3-丁二醇发酵培养基中的生长状况及对2,3-丁二醇合成的影响;同时检测对其他副产物合成的影响,通过对野生株和luxS突变株发酵合成2,3-丁二醇的结果进行研究发现,与野生株相比,失去了群体感应功能的luxS突变株利用同样质量浓度的葡萄糖合成2,3-丁二醇的能力提高了47.75%,同时琥珀酸、乳酸和乙醇等主要副产物的质量浓度大幅降低。