退化伊犁绢蒿荒漠草地高光谱特征分析

利用SVC HR-768 便携式光谱仪测定围栏内、外不同退化梯度伊犁绢蒿（Seriphidium transiliense）荒漠草地植物群落的光谱反射率,以及该类草地退化过程中的优势种伊犁绢蒿（减少种）、叉毛蓬（Petrosimonia sibirica）（增加种）、萹蓄（Polygonum aviculare）（侵入种）和裸地的光谱反射率,为实现伊犁绢蒿荒漠草地的高光谱遥感监测奠定基础。结果表明：在近红外波段,特征种与裸地的光谱反射率为裸地>萹蓄>伊犁绢蒿>叉毛蓬。萹蓄光谱反射率对退化草地植物群落的反射特征影响很小。退化草地植物群落冠层光谱反射率主要受伊犁绢蒿和叉毛蓬反射特征的影响,与优势种重要值成正相关关系,中度退化草地植物群落光谱反射率为围栏内>围栏外;重度和极度退化草地植物群落光谱反射率为围栏内<围栏外。     

新疆伊犁绢蒿荒漠草原恢复重建技术及改良对策

伊犁绢蒿荒漠是新疆荒漠草地的重要组成部分,但是目前80%出现不同程度退化,以伊犁绢蒿为优势种的草地群落已大面积演替为以一年生植物为主的草地群落,草地失去了原有的利用价值。本文概述了伊犁绢蒿荒漠草原退化的过程和原因,着重对伊犁绢蒿荒漠草原恢复重建技术介绍,提出保护和恢复该类草原的应对措施和改良对策。     

基于CNN和SVM的地面高光谱遥感草地植物识别

物候期和识别模型的选择直接影响植物识别的精度。本研究以蒿类荒漠草地主要植物伊犁绢蒿(Seriphidium transiliense)、角果藜(Ceratocarpus arenarius)以及裸地为识别对象,选择4月、6月、9月3个时期,通过SOC 710 VP高光谱成像仪采集草地群落高光谱数据,在分析地物光谱反射率差异的基础上,利用最佳指数因子(OIF)筛选特征波段,通过卷积神经网络(CNN)和支持向量机(SVM)建立识别模型。结果表明：1)不同物候期的伊犁绢蒿与角果藜在可见光波段均表现为“低-高-低”的光谱反射率趋势,并随月份增加峰谷现象逐渐不明显;红边波段这两种植物表现出快速上升;在NIR平台区4月各识别对象间反射率大小差异最明显。2)利用OIF筛选的识别波段组合在月份间表现一致,为638.64、789.49和923.79 nm。3)在识别精度上,SVM> CNN;4月> 9月> 6月;裸地>伊犁绢蒿>角果藜。综合来看,采用SVM在4月对蒿类荒漠草地主要植物进行识别的精度最高,为92.12%。     

伊犁绢蒿生物学特性的初步研究

伊犁绢蒿荒漠草地既是新疆天山北坡重要的春秋牧场,又是新疆草地退化最严重的一类草地。通过对伊犁绢蒿生长发育节律的观测及生物量动态分析,初步探究伊犁绢蒿的生物学特性。研究结果表明：草地植物年内产量变化曲线与伊犁绢蒿生长规律一致为“双峰”型,植物群落生长主要受降水的影响;伊犁绢蒿在5月到6月初生长速度较快,春季生长强度是秋季的1．6倍。     

基于HJ-HSI数据的伊犁绢蒿荒漠草地生物量估测

以地面实测数据为依据,通过获取其同步HJ-HSI影像光谱反射率,筛选出光谱变量、波段变量,对不同利用状态的退化伊犁绢蒿(Seriphidium transiliense)荒漠草地(围栏封育区N,围栏外重度退化区W_1,围栏外中度退化区W_2)的地上生物量进行估测。结果表明,1)各季节不同利用状态伊犁绢蒿荒漠草地群落HJ-HSI光谱反射率不同,春季为W_2NW_1,夏季为W_2W_1N,秋季为W_1W_2N;2)HJ-HSI可以实现对伊犁绢蒿荒漠草地地上生物量的估测,估测模型因群落类型和季节不同而存在差异。春、夏、秋3个季节的估测模型,N分别由DVI、NDVI、620.225-627.895nm反射率平均值所构建,W_1分别由近红外波段(Rn)、656.305nm和776.8199nm反射率归一化值、MSAVI构建,W_2分别由652.09和732.01nm反射率归一化值、红外波段(Rr)、584.52-598.295nm反射率平均值构建。     

天山北坡伊犁绢蒿荒漠不同退化阶段草地特征分析

对天山北坡不同退化阶段伊犁绢蒿(Seriphidium transillense(Poljak.)Poljak.)荒漠草地植被特征和土壤特性进行分析,以期为该地区的草地恢复提供理论依据.结果表明:随着草地退化程度的加剧,伊犁绢蒿荒漠的优势种逐渐被叉毛蓬(Petrosimonia sibirica(Pall.)Bunge)替代;地带性植被伊犁绢蒿的频度、盖度、生物量和密度随着草地退化程度的加剧逐渐降低,而叉毛蓬却呈相反趋势;叉毛蓬种群在扩繁时采用"r"对策,产生大量种子迅速占领领地,阻止了草地的完全崩溃.随着伊犁绢蒿荒漠退化,草地的饲用价值劣化,草地植物种类向低矮、适口性差、短命和类短命植物过度.草地的植被退化与土壤退化不完全一致,随着草地退化,土壤表层颗粒有变粗趋势,然而土壤有机质和全氮却呈现稳定升高的趋势;极度退化地区的高养分含量主要是由家畜排泄物导致.     

伊犁绢蒿荒漠不同退化阶段草地特征与载畜力研究

伊犁绢蒿(Seri phidium transiliense)荒漠是新疆北疆地区重要的草地类型,但长期的超载过牧使该区域草地呈现不同程度的退化,严重地制约草地生产及生态功能的发挥.对伊犁绢蒿荒漠不同退化阶段草地的植物性状和载畜力进行分析,以期为区域草地的合理利用和畜牧业的可持续发展提供参考.结果表明:随着草地退化程度的加剧,伊犁绢蒿在群落中的优势地位逐渐被叉毛蓬(Petrosimonia sibirica)取代；伊犁绢蒿和群落的粗灰分与粗纤维含量在春季变化趋势相似,而伊犁绢蒿和群落的粗蛋白及粗灰分含量与粗纤维含量在秋季变化趋势相反；未退化草地春、秋季载畜力分别为1.20个绵羊单位/hm2和1.78个绵羊单位/hm2,中度退化草地春、秋季载畜力分别为0.29个绵羊单位/hm2和1.15个绵羊单位/hm2.为防止当地生态环境恶化,应在优势种发生更替前对草地进行有效的保护措施,即在对未退化和中度退化的草地利用时应以理论载畜力为最大承载力阈值.     

封育年限对伊犁绢蒿荒漠群落特征及草场质量的影响

以伊犁绢蒿(Seriphidium transiliense)荒漠草地为对象,研究不同封育年限对伊犁绢蒿荒漠草地植被群落特征、物种多样性及草场质量指数的影响.结果表明:伊犁绢蒿荒漠草地总盖度、密度及生物量,随封育时间的增加呈先降后升再降的波动变化,且以封育6年达到最高;围栏封育有利于伊犁绢蒿、木地肤(Kochia prostrata)的生长与恢复,生物量较对照区分别提高了158.63%~221.44%,100%,且封育9年时伊犁绢蒿重要值恢复到首位;封育后群落的物种多样性指数较对照区均有增加,且随封育年限的增加呈现先增后减再增趋势;通过群落物种多样性分析认为,封育9年后伊犁绢蒿荒漠草地尚未恢复到原始顶级状态;以草地的生产力与经济效益来考虑,初步推测封育5年为该区域伊犁绢蒿荒漠草地的最佳围栏时间.     

基于高光谱成像技术的伊犁绢蒿荒漠草地主要植物识别参数的筛选

为获得伊犁绢蒿荒漠草地主要植物最佳识别参数，本研究利用SOC710 VP成像光谱仪于4月采集群落影像，以伊犁绢蒿（Seriphidium transiliense）、角果藜（Ceratocarpus arenarius）、叉毛蓬（Petrosimonia sibirica）和裸地为识别对象，基于光谱学响应与峰谷特性选取8个位置参数、2个面积参数、4个植被指数，按照显著性差异从小到大逐一筛选敏感参数，并使用Fisher判别分析进行精度验证。结果表明：4种识别对象的反射率大小在可见光和近红外波段表现出不同的特征；用筛选出的红谷位置、红边位置、红谷幅值、蓝边面积、NDVI₁、RVI₁和RVI₂进行判别，精度分别为伊犁绢蒿91.11%、角果藜80.56%、叉毛蓬91.11%、裸地100%，总精度为92.13%。本试验筛选出的识别参数建立判别模型可为进一步对群落影像进行物种定量分类提供依据。     

基于高光谱遥感的退化伊犁绢蒿荒漠草地群落盖度估算

实地测定不同利用状态的退化伊犁绢蒿荒漠草地群落覆盖度,获取草地的地面光谱和相对应的HJ-HSI高光谱影像两种数据,并通过植被指数和筛选波段变量估测相应的植被覆盖度。结果表明:利用两种高光谱数据构建的回归模型所选用的光谱变量和波段变量不一致,地面实测光谱的回归方程决定系数大于HJ-HSI光谱数据,利用地面光谱对较低决定系数的群落盖度估测模型进行纠正,精度有一定的提高;不同光谱数据估测盖度的最佳模型精度均在85%以上,围栏封育区模型均由敏感波段反射率的归一化值构建,围栏外重度退化区模型均由DVI构建,围栏外中度退化区模型可由光谱变量R_r、R_n构建。     

天山北坡草地盖度高光谱遥感估算

以天山北坡乌鲁木齐县甘沟乡为研究区,利用美国SVC HR-768便携式光谱仪采集25块样方的高光谱数据,并测定对应样方中草地盖度,分析草地盖度与原始光谱、一阶微分光谱和高光谱特征变量之间的相关关系;采用回归统计的方法,基于高光谱位置变量、高光谱面积变量和高光谱植被指数变量构建草地盖度的估测模型,并进行模型精度评价。结果表明,研究区草地盖度与植被冠层光谱反射率相关性较强的波段范围为354-704、1 420-1 481和1 904-2 512nm;基于一阶微分光谱和高光谱植被指数构建的估测模型能更好地反演草地盖度。通过模型检验,确定基于560nm的光谱一阶微分模型y=-384.153x+72.096可作为草地盖度的最优估测模型,模型均方根误差为7.344%,估算精度为90.343%。     

基于高光谱数据的高寒草甸氮磷钾含量估测方法研究——以青海省贵南县及玛沁县高寒草甸为例

以青海省玛沁县和贵南县高寒草甸作为典型研究区,利用地物光谱仪采集了20块样地的高光谱数据,并测定了对应样地所有样方中牧草的养分含量,分析了牧草中氮磷钾素含量与冠层原始光谱反射率和一阶微分光谱反射率之间的相关关系;采用回归统计方法,基于光谱位置变量,光谱面积变量及植被指数变量构建了高寒草甸氮磷钾素的估测模型,并对模型进行了精度评价.结果表明,1)与原始光谱反射率曲线相比,一阶微分光谱反射率曲线能较好地反映牧草中N,P,K素所对应的敏感波段;2)高寒草甸牧草中N,P,K素含量与冠层高光谱相关性较强的波段大多分布在红光区域(680~760 nm);3)基于光谱位置变量构建的估测模型能更好地反演高寒草甸N,P,K素含量.其中,以光谱位置变量R'_708.88为自变量的对数模型对氮素含量估测效果较好,R²为0.67,估测精度达到83.56%;以光谱位置变量R'_704.85为自变量的对数模型对磷素含量估测效果较好,R²为0.55,估测精度达到92.15%;以光谱位置变量R'_697.36为自变量的对数模型对钾素含量估测效果较好,R²为0.86,估测精度达到82.44%.     

基于高光谱影像的高寒牧区土地覆盖分类与草地生物量监测模型

利用环境减灾卫星HJ 1A高光谱图像数据,分析了研究区不同土地覆盖类型的波谱曲线特征,比较了监督分类和光谱角分类方法对高光谱影像的分类精度,研究了高寒牧区草地生物量超光谱遥感监测模型。结果表明,1）不同地物波谱曲线的吸收位置和吸收深度等波谱特征在可见光波段具有较大差异,在近红外波段吸收特征相似。在可见光波段,云和植被的吸收位置最少,都只有1处,但云的吸收深度小于植被;裸地吸收位置有5处;水域吸收位置最多,有6处。2）光谱角与监督分类均适于高光谱影像分类,但光谱角分类方法的总精度可达85.9%,远高于监督分类法。3）依据草地生物量与9种植被指数间的回归分析结果,选出了适合研究区草地植被生物量动态监测的两种植被指数,即归一化植被指数和比值植被指数。     

基于SPOT-5卫星影像的灌区作物识别

高分辨率卫星影像是作物精确分类和评估的重要数据源,在农作物种植规划、估产等领域具有重要的应用价值。本研究利用分辨率为2.5 m的SPOT 5影像分析张掖市盈科灌区的作物分布状况,同时分别生成分辨率为10和30 m的影像,用于尺度验证。最终得到研究区作物分类图,所用方法主要有最小距离法、马氏距离、最大似然法、光谱角制图仪（SAM）和支持向量机（SVM）。Kappa系数分析表明,最大似然法和SVM的分类效果好于其它分类器,分别为0.871 9和0.862 5,但这两种方法的统计量无明显区别;分类图精度评价表明,基于最大似然法的分类图总体精度最高,为90.6%;随像元空间尺度的增加,分类精度未产生明显变化。研究结果表明,最大似然法和SVM技术可以与SPOT 5影像结合,用于作物类型识别和作物面积估算。     

Utilizing National Agriculture Imagery Program Data to Estimate Tree Cover and Biomass of Piñon and Juniper Woodlands

With the encroachment of piñon (Pinus ssp.) and juniper (Juniperus ssp.) woodlands onto sagebrush steppe rangelands, there is an increasing interest in rapid, accurate, and inexpensive quantification methods to estimate tree canopy cover and aboveground biomass. The objectives of this study were 1) to evaluate the relationship and agreement of piñon and juniper (P-J) canopy cover estimates, using object-based image analysis (OBIA) techniques and National Agriculture Imagery Program (NAIP, 1-m pixel resolution) imagery with ground measurements, and 2) to investigate the relationship between remotely-sensed P-J canopy cover and ground-measured aboveground biomass. For the OBIA, we used eCognition® Developer 8.8 software to extract tree canopy cover from NAIP imagery across 12 P-J woodlands within the Sagebrush Steppe Treatment Evaluation Project (SageSTEP) network. The P-J woodlands were categorized based on the dominant tree species found at the individual sites for the analysis (western juniper, Utah juniper, and mixed P-J community). Following tree canopy cover extractions, relationships were assessed between remotely-sensed canopy cover and ground-measured aboveground biomass. Our OBIA estimates for P-J canopy cover were highly correlated with ground-measured tree canopy cover (averaged across all regions r = 0.92). However, differences between methods occurred for western and Utah juniper sites (P < 0.05), and were more prominent where tree canopy cover was > 40%. There were high degrees of correlation between predicted aboveground biomass estimates with the use of remotely-sensed tree canopy cover and ground-measured aboveground biomass (averaged across all regions r = 0.89). Our results suggest that OBIA methods combined with NAIP imagery can provide land managers with quantitative data that can be used to evaluate P-J woodland cover and aboveground biomass rapidly, on broad scales. Although some accuracy and precision may be lost when utilizing aerial imagery to identify P-J canopy cover and aboveground biomass, it is a reasonable alternative to ground monitoring and inventory practices.     

天然草地牧草营养品质的高光谱遥感研究进展

天然草地牧草营养品质的优劣不仅影响家畜的生长发育,同时也影响畜产品的品质,对草牧业的发展具有至关重要的意义。高光谱遥感技术的飞速发展使深入研究天然草地牧草品质的动态变化成为可能。本研究综述了目前可利用的高光谱遥感数据以及天然草地牧草营养品质遥感反演的主要成果、常用方法和最新研究动态,分析了我国在天然草地牧草营养品质监测与评价方面尚存在数据获取困难、相关研究缺乏、软硬件性能不足等问题;在多种观测平台及相关技术不断革新背景下,探索星载、机载和地面高光谱数据的有机结合,强化高光谱遥感仪器性能,提高关键营养成分的反演精度是未来研究的重点。     

甘南草地地上生物量的高光谱遥感估算研究

为了促进高光谱分辨率遥感技术在草地畜牧业动态监测和遥感估产中的应用,选择甘南草原为研究区,通过野外观测,测量了天然牧草的冠层高光谱和地上生物量数据,分析了4种主要草地类型的冠层光谱曲线特征,并分析了地上鲜生物量与冠层反射光谱和一阶微分光谱之间的相关关系,构建了光谱特征参数作为变量,建立了甘南草原牧草地上鲜生物量的高光谱估算模型,并对模型进行检验,结果表明：特征参数D723的对数回归模型,不仅相关系数较高,而且均方根和相对误差都较小,因此,估算精度较高,可作为甘南草地地上鲜生物量的最佳高光谱估算模型。     

基于植被指数的三江源高寒草地植物分类与识别方法研究

随着生态健康检测与保护工作的实践以及研究问题的深入，传统的植物分类手段不能完全满足当前研究的需要。因此为研究快速分类识别草地植物的方法，本研究利用ASD (Analytical spectral devices)地物光谱仪，采集了三江源地区高寒草地常见的阿尔泰葶苈(Draba altaica)、高山风毛菊(Saussurea japonica)和车前状垂头菊(Cremanthodium ellisii)等36种植物的原始光谱数据，并选择了比值植被指数等16种高光谱植被指数，基于支持向量机(Support vector machines，SVM)等3种机器学习算法，构建高寒草地植物光谱分类识别模型。研究结果表明：高寒草地植物的原始光谱均符合绿色植物特征，但由于植物形态特征不同光谱差异主要集中在可见光波段；基于植被指数结合3种算法构建的分类模型，精度依次为随机森林(Random forest,RF)(99.4%)>SVM (93.2%)>K邻近算法KNN (88.0%)，且模型的预测结果都出现了误判情况；相比SVM与KNN，RF为基于植被指数构建模型的最佳算法，同时能对所构建模型参数进行重要性分析，其中RGI和SAVI为提高RF分类模型精度的两个重要参数。     

Improving Potential Geographic Distribution Models for Invasive Plants by Remote Sensing

Remote sensing is used to map the actual distribution of some invasive plant species, such as leafy spurge (Euphorbia esula L.), whereas geospatial models are used to indicate the species’ potential distribution over a landscape. Geographic data layers were acquired for Crook County, Wyoming, and the potential distribution of leafy spurge presence or absence were predicted with the use of the Weed Invasion Susceptibility Prediction (WISP) model. Hyperspectral imagery and field data were acquired in 1999 over parts of the study area. Leafy spurge presence or absence was classified with the use of the Spectral Angle Mapper with a 74% overall accuracy. However, the user accuracy was 93%, showing that where leafy spurge was indicated in the image, leafy spurge was usually found at that location. With the use of Kappa analysis, there was no agreement between WISP model predictions and either the field data or the classified hyperspectral image. Kappa analysis was then used to compare predictions based on single geographic data layers, to increase the power to detect subtle relationships between independent variables and leafy spurge distribution. The WISP model was revised for leafy spurge based on the remote-sensing analyses, and only a few variables contributed to predictions of leafy spurge distribution. The revised model had significantly increased accuracy, from 52.8% to 61.3% for the field data and from 30.4% to 80.3% for the hyperspectral image classification, primarily by reducing the areas predicted to have potential for invasion. It is generally more cost effective to deal with the initial stages of invasion by only a few plants, compared to an invasion that is large enough to be detected by remote sensing. By reducing the potential area for monitoring, management of invasive plants could be performed more efficiently by field crews.     

Estimation of the standardized ileal digestible valine-to-lysine ratio in 13- to 32-kilogram pigs

Three experiments were conducted to determine the optimum standardized ileal digestible Val-to-Lys (SID Val:Lys) ratio for 13- to 32-kg pigs. In Exp. 1, 162 pigs weaned at 17 d of age (8 pens/treatment) were used, and a Val-deficient basal diet containing 0.60% l-Lys·HCl, 1.21% SID Lys, and 0.68% SID Val was developed (0.56 SID Val:Lys). Performance of pigs fed the basal diet was inferior to a corn-soybean meal control containing only 0.06% l-Lys·HCl, but was fully restored with the addition of 0.146% l-Val to the basal diet (68% SID Val:Lys). In Exp. 2, 54 individually housed barrows (21.4 kg) were utilized in a 14-d growth assay. Pigs were offered a similar basal diet (1.10% SID Lys), ensuring Lys was marginally limiting with no supplemental l-Val (55% SID Val:Lys). The basal diet was fortified with 4 graded levels of l-Val (0.055% increments) up to a ratio of 75% SID Val:Lys. In Exp. 3, 147 barrows (13.5 kg) were fed identical diets, only with 1 additional level at a SID Val:Lys of 80% and fed for 21 d. In Exp. 2 and 3, a high protein, control diet was formulated to contain 1.10% SID Lys and 0.20% l-Lys·HCl. In Exp. 2, linear effects on ADG (713, 750, 800, 796, and 785 g/d; P = 0.05) and G:F (P = 0.07) were observed with increasing SID Val:Lys, characterized by improvements to a ratio of 65% and a plateau thereafter. In Exp. 3, quadratic improvements in ADG (600, 629, 652, 641, 630, and 642 g/d; P = 0.08) and G:F (P = 0.07) were observed with increasing SID Val:Lys, as performance increased to a ratio of 65% but no further improvement to a ratio of 80%. Pigs fed the control diet did not differ from those fed a ratio of 65% SID Val:Lys in Exp. 2, but did have improved G:F in Exp. 3 (P = 0.03). To provide a more accurate estimate of the optimum SID Val:Lys, data from Exp. 2 and 3 were combined. With single-slope broken-line methodology, the minimum ratio estimate was 64 and 65% SID Val:Lys for ADG and G:F, respectively. With combined requirement estimates, the data indicate that a SID Val:Lys of 65% seems adequate in maintaining performance for pigs from 13 to 32 kg.