西北林学院校园绿地规划探讨

通过对西北林学院校园绿地规划布局的综合分析,确立了校园绿地规划的总体构思和设计原则,探讨各种园林造景手法在绿地设计中的表现方式,同时,对本校校园绿地建设提出合理化建议     

基于反射率及导数的草原植被冠层光合有效吸收分量高光谱反演

利用ASD便携式野外光谱仪和光量子仪实测了6种草原植被类型关键生育期的反射光谱数据和光合有效辐射值,利用可见光波段处反射率及近红外波段区处一阶导数分别与fPAR建立逐步回归方程,同时,将各波段反射率与各波段导数光谱建立逐步回归方程。结果表明:典型草原光合有效辐射分量与可见光反射率相关性好于近红外波段反射率,其中在405和470 nm波段相关性最好;fPAR与一阶导数相关关系在855和965 nm波段处较强。fPAR与405和470 nm反射率以及965 nm一阶导数的多波段逐步回归分析结果取得了较单波段和NDVI最优的估算效果,R²达0.939。利用高光谱数据进行fPAR估算时,需要综合考虑可见光和近红外波段信息,同时也要充分考虑反射率与反射率导数的方法。水分强吸收的光谱波段具有提高fPAR估算精度的潜力。     

ER Mapper软件进行几何精校正的原理和方法

几何精校正是利用地面控制点(GCP)对遥感图像进行几何校正.用ER Mapper软件进行几何精校正,主要包括参数设置、控制点输入和几何精校正.影响几何精校正的因素,主要表现在GCP的数量、分布和本身的定位精度,校正方法不同,影响也不同.     

基于高光谱图像技术的小麦种子分类识别研究

为了探讨高光谱图像技术在小麦种子分类识别中应用的可行性,采集了河南地区主要种植的7个小麦品种的种子高光谱图像及900～1 700nm范围的光谱信息,建立了主成分分析法(PCA)-支持向量机(SVM)分类模型。运用PCA对光谱数据进行降维处理,结合SVM模型比较了不同实验条件下小麦种子分类准确率以及在最佳条件下3个、4个和6个品种种子的分类准确率。结果显示,3个品种间种子分类准确率除个别外平均达到95%以上,4个品种间种子分类准确率在80%左右,6个品种间种子分类准确率在66%左右。这说明充分利用光谱信息可以对3个或4个小麦品种进行多籽粒分类。     

Spatial segregation of striped marlin (Kajikia audax) by size in the eastern Pacific Ocean

Striped marlin (Kajikia audax) is an epipelagic fish distributed in oceanic and coastal waters of the Pacific Ocean. This species is usually found in warm and coastal waters with high primary productivity. The main goal of this study was to describe the spatial segregation of striped marlin by average Eye‐Fork length (EFL) in the eastern Pacific Ocean (EPO) and its relationship with environmental variables using EFL data obtained from tuna purse‐seining and Generalized Additive Models (GAMs). The model suggested that larger individuals of striped marlin were more likely to be found in waters with high Chlorophyll‐a concentration (>2 mg/m³) and with temperatures lower than 25°C, within a region known as the “cold tongue” and the Humboldt current system, while smaller individuals were more likely to be found in warmer and low productive areas within a region known as the “warm pool of the EPO.” We observed that set type caused a large variation on average EFL of striped marlin; larger fish were captured in sets associated with floating objects (natural and manmade), while smaller fish were captured in sets associated with dolphins. Despite this, our findings suggest that striped marlin has a latitudinal gradient in average EFL; larger individuals occurred predominantly south of 10°N, while smaller ones occurred predominantly in coastal waters between 10°N and 20°N, thus demonstrating a spatial segregation of the species affected by its maturity stage.     

高光谱遥感技术在现代林业中的应用与发展

以高光谱遥感技术为例，阐述了遥感技术在林业中关于森林信息获取、森林监测和森林健康评价等各方面的运用和发展，以及“3S一体化技术”在现代林业中广泛应用的必然发展趋势。     

基于无人机热红外图像的核桃园土壤水分预测模型建立与应用

为了解北方核桃园区的土壤水分状况,实现优化水资源配置的目的。该文于2016和2017年采用固定式热红外成像仪(A310 f)连续观测得到核桃主要生长季节午后(13:00和14:00)的冠层温度,并同步观测温度、湿度、辐射、风速、降雨量和0~80 cm不同土层深度的土壤体积含水量。并于2017年8月11日利用无人机热成像系统(TC640)对连续灌溉区域和干旱胁迫区域进行了图像采集。结果表明,40~60 cm土层深度可能是核桃树主要吸收水分的区域。冠层温度普遍高于空气温度,其变化范围在0~5℃之间,冠气温差与土壤含水量呈负相关,与太阳辐射呈正向关系,其中,土壤含水量的贡献值达到了75%。利用2017年13:00时的冠层与空气温差数据来建立的土壤水分预测模型,R2=0.64;同时,利用14:00时的实测数据对所建立模型进行验证,R2=0.61,表明该模型具有一定的拟合精度。最后,将模型用于诊断核桃区域水分状况,证明了其具有较好的实际应用效果。该研究首次将固定式热成像设备与无人机热成像系统相结合来研究树木的冠层温度,并成功实现了从理论模型到实际应用,从单株水平到区域尺度的转换。     

基于归一化光谱指数的土壤有机质含量空间分布状况研究

【目的】研究土壤主要养分含量特征信息的光谱预测值,使其更具直观性、空间性及科学性,为大尺度对农田的土壤养分空间分布状况获取、评价与科学管理提供依据。【方法】利用ArcGIS 9.3的普通克里格(Ordinary Kriging)插值方法,对小面积及大尺度试验区的土壤样本点进行有机质反演插值填图,并将实验室化学测定土壤有机质含量的实测值与其高光谱模型预测值进行Kriging插值填图比较,分析土壤有机质含量的实测值与预测值的空间分布状况差异。【结果】从小面积试验区到大尺度条田地块插值后的空间分布情况来看,土壤有机质含量的光谱预测值(基于归一化光谱指数NDI[495,485]预测)与实测值之间具有较好的相似性,预测效果较好。【结论】通过土壤有机质含量信息状况的空间分布填图来确定农田分区基本管理单元的适宜尺度,为实施大区域农田养分分区精量管理的划分,提出科学的平衡施肥方案,为适合新疆及兵团特色的精准农业管理、土壤养分快速探测、精量施肥等技术提供技术理论支持。     

High resolution satellite imaging sensors for precision agriculture

The central concept of precision agriculture is to manage within-field soil and crop growth variability for more efficient use of farming inputs. Remote sensing has been an integral part of precision agriculture since the farming technology started developing in the mid to late 1980s. Various types of remote sensors carried on ground-based platforms, manned aircraft, satellites, and more recently, unmanned aircraft have been used for precision agriculture applications. Original satellite sensors, such as Landsat and SPOT, have commonly been used for agricultural applications over large geographic areas since the 1970s, but they have limited use for precision agriculture because of their relatively coarse spatial resolution and long revisit time. Recent developments in high resolution satellite sensors have significantly narrowed the gap in spatial resolution between satellite imagery and airborne imagery. Since the first high resolution satellite sensor IKONOS was launched in 1999, numerous commercial high resolution satellite sensors have become available. These imaging sensors not only provide images with high spatial resolution, but can also repeatedly view the same target area. The high revisit frequency and fast data turnaround time, combined with their relatively large aerial coverage, make high resolution satellite sensors attractive for many applications, including precision agriculture. This article will provide an overview of commercially available high resolution satellite sensors that have been used or have potential for precision agriculture. The applications of these sensors for precision agriculture are reviewed and application examples based on the studies conducted by the author and his collaborators are provided to illustrate how high resolution satellite imagery has been used for crop identification, crop yield variability mapping and pest management. Some challenges and future directions on the use of high resolution satellite sensors and other types of remote sensors for precision agriculture are discussed.     

不同生育时期冬小麦叶片相对含水量高光谱监测

为实现冬小麦不同生育时期叶片水分含量的快速监测,以冬小麦冠层高光谱数据和红外热成像数据为基础,计算得到5种光谱参数,通过对不同生育时期叶片相对含水量与光谱参数拟合状况进行分析和筛选,分别构建了基于光谱参数的叶片相对含水量反演模型,并对模型进行检验。结果表明,不同生育时期叶片相对含水量与比值指数（RVI）、归一化差值植被指数（NDVI）、比值/归一化植被指数（R/ND）、优化土壤调整植被指数（OSAVI）、冠气温差（TDc-a）均呈极显著相关（P＜0.01）;拔节期、抽穗期、开花期、灌浆前期和灌浆后期叶片相对含水量分别与NDVI、OSAVI、R/ND、TDc-a和TDc-a拟合效果较好,决定系数分别为0.842、0.884、0.831、0.864和0.945;预测模型的均方根误差分别为0.019、0.016、0.027、0.032和0.024,相对误差分别为2.16%、1.80%、3.30%、3.81%和3.53%。因此,在拔节期、抽穗期、开花期、灌浆前期和灌浆后期,可以分别利用NDVI、OSAVI、R/ND、TDc-a和TDc-a估测冬小麦叶片相对含水量。