基于数码照片的草地植被覆盖度快速提取方法

草地植被覆盖度是草地健康与否的重要参数,提高草地植被覆盖度的计算效率和精度具有重要的意义。本研究提出了一种利用过绿特征植被指数和半自动阈值设定算法（半自动阈值法）的从数码照片中快速计算草地植被覆盖度的方法,并将该方法与最大似然法监督分类方法（最大似然法）和色度饱和度法进行了比较。对32张草地数码照片的植被覆盖度估算结果和准确结果进行回归分析,分别得到斜率、截距和回归系数这3个参数。结果表明,3种方法都未能考虑植物的非绿色组分而存在低估的问题。半自动阈值法的准确度堪比最大似然法,且没有后者存在的低覆盖率高估问题;而且该方法人工干预少,计算结果准确客观,适用性强;但是对绿色特征不明显的植物（如灰绿色植物）效果不佳。     

无人机RGB影像在高寒草地狼毒入侵监测及盖度估算中的应用

有毒植物入侵对草地生态系统和生物多样性的影响日益严重,无人机遥感为毒草防治提供了快速高效的监测方法。以青藏高原危害最严重的有毒植物狼毒为研究对象,探讨基于无人机影像的高精度狼毒遥感识别及盖度估算方法。在盛花期获取典型狼毒入侵草甸的无人机RGB正射影像,结合植被指数、色彩变换和纹理滤波方法提取狼毒识别特征,通过ReliefF-VIF/Pearson二次降维筛选出6项最优特征,基于RF、SVM和ANN三种机器学习算法构建狼毒识别模型。结果表明,与原始RGB波段相比,优选特征使狼毒识别精度有效提高4%～7%。三种分类方法的分类总精度和狼毒分类精度均大于81%,基于优选特征的RF和SVM模型的分类总精度和狼毒分类精度达到91%以上,狼毒识别效果最佳。随着统计单元的增大,利用无人机RGB影像分类结果估算狼毒盖度的精度明显下降但稳定性逐渐增加,斑块尺度50～60 cm是狼毒盖度估算的最优尺度。     

3种不同利用微卫星标记进行个体识别方法的比较研究

以鲁西黄牛和渤海黑牛（各30头）12个微卫星座位数据为基础,利用系统树分析法、最大似然法及Bayes方法,比较不同方法对个体识别效率的影响。结果表明：当利用12个微卫星座位和6个低杂合度位点数据时,3种个体识别方法鉴定的准确性均为100%。但利用6个高杂合度座位时,Bayes方法的准确性最高（83.33%）,最大似然法次之（76.67%）,系统树分析法最低（71.67%）。结果表明,基于Bayes理论的个体识别方法最准确,是进行同类研究的首选工具,而系统树分析法和最大似然法可以作为其补充加以应用。     

融合变换在紫茎泽兰监测中的应用研究

应用融合变换对四川西昌地区紫茎泽兰遥感图像ASTER数据进行处理,结果表明:对ASTER三种传感器生成的三种分辨率的影像数据进行Gram-Schmidt融合处理,可以将具有较高光谱分辨率的多光谱遥感波段的空间分辨率AK30m×30m提高到15 mx15 m,增加更多的地表地物组分信息,有助于识别各种不同地物类型,同时使同一地物的光谱信息变换前后保持不变,确保了后续图像分类的可靠性.应用ENVI中时融合后的图像进行马氏距离分类,分类结果总体精度为73.6983%,Kappa系数等于0.6936.     

融合变换在紫茎泽兰遥感监测中的应用研究

应用融合变换对四川西昌地区紫茎泽兰遥感图像ASTER数据进行处理,结果表明:对ASTER三种传感器生成的三种分辨率的影像数据进行Gram-Schmidt融合处理,可以将具有较高光谱分辨率的多光谱遥感波段的空间分辨率从30m×30m提高到15m×15m,增加更多的地表地物组分信息,有助于识别各种不同地物类型,同时使同一地物的光谱信息变换前后保持不变,确保了后续图像分类的可靠性。应用ENVI中对融合后的图像进行马氏距离分类,分类结果总体精度为73.6983%,Kappa系数等于0.6936。     

DEM for high-resolution and low altitude images of UAV

本文结合生产实践,介绍了基于无人机低空影像处理方法,生成比例尺1∶1000正射影像时构建的数字高程模型(DEM),利用检查点法对其所能达到的分辨率尺度范围进行实验讨论,得到无人机低空影像DEM合适的分辨率大小的结论,并且对无人机低空影像生成的DEM的精度进行评价.     

融合变换在紫茎泽兰遥感监测中的应用研究

应用融合变换对四川西昌地区紫茎泽兰遥感图像ASTER数据进行处理，结果表明：对ASTER三种传感器生成的三种分辨率的影像数据进行Gram-schmidt融合处理，可以将具有较高光谱分辨率的多光谱遥感波段的空间分辨率从30m×30m提高到15m×15m，增加更多的地表地物组分信息，有助于识别各种不同地物类型，同时使同一地物的光谱信息变换前后保持不变，确保了后续图像分类的可靠性。应用ENVI中对融合后的图像进行马氏距离分类，分类结果总体精度为73．6983％，Kappa系数等于0．6936。     

方差组分估计方法的比较

本文通过蒙特卡罗模拟产生的八个模拟资料比较了我国常用的方差分析法(不考虑场年季效应)、Henderson方法1、Henderson方法3、最大似然法、改进最大似然法与约束最大似然法。结果表明:方差分析法估计值偏差最大,而约束最大似然法的估计值最准确。     

方差组分估计方法的研究

本文利用最大似然法、改进最大似然法与约束最大似然法估计了猪的出生重与二月龄体重的方差、协方差组分和遗传参数。配合的模型为多性状动物模型。典型变换的引入大大降低了计算量。     

基于灵活的时空融合模型的植被覆盖度与植被指数关系

时空数据融合模型被广泛地应用于获取高时间、高空间分辨率的植被指数与植被覆盖度,但是其反演的精度常常受输入的低空间分辨率影像(如MODIS影像)的影响。本研究基于灵活的时空数据融合方法(FSDAF),深入分析了赛里木湖流域与石河子地区两种不同情景的MODIS影像组合对FSDAF模型植被覆盖度提取精度的影响,并研究了6种植被指数与植被覆盖度的线性与非线性关系。研究结果表明,FSDAF模拟影像的植被覆盖度精度取决于2个时期MODIS影像的变化率,影像变化小时取得的精度明显好于影像差异大的情况。而采用植被指数对植被覆盖度模拟时,NDVI与OSAVI的线性拟合效果较好,可以获取较理想的结果。试验表明,采用时空模型用于研究区植被覆盖反演能取得较好的效果,具有一定的应用推广价值。     

Modeling Bare Ground With Classification Trees in Northern Spain

Bare ground abundance is an important rangeland health indicator and its detection is a fundamental part of range management. Remote sensing of bare ground might offer solutions for land managers but also presents challenges as modeling in semiarid environments usually involves a high frequency of spectral mixing within pixels. Classification tree analysis (CTA) and maximum likelihood classifiers were used to model bare ground in the semiarid steppes of the middle Ebro valley, Aragon, Spain using Satellite Pour l'Observation de la Terre 4 (SPOT 4) imagery and topographic data such as elevation, slope, aspect, and a morphometric characterization model. A total of 374 sample points of bare-ground fraction from sixteen 500-m transects were used in the classification and validation process. Overall accuracies were 85% (Kappa statistic = 0.70) and 57% (Kappa statistic = 0.13) from the CTA and maximum likelihood classifiers, respectively. Although spectral attributes were essential in bare-ground classification, the topographic and morphometric properties of the landscape were equally critical in this modeling effort. Although the specific layers best suited for each specific model will vary from region to region, this study provided an important insight on both bare-ground modeling and the potential advantages of CTA.     

Postfire Recovery of Sagebrush Communities: Assessment Using Spot-5 and Very Large-Scale Aerial Imagery

Much interest lies in long-term recovery rates of sagebrush communities after fire in the western United States, as sagebrush communities comprise millions of hectares of rangelands and are an important wildlife habitat. Little is known about postfire changes in sagebrush canopy cover over time, especially at a landscape scale. We studied postfire recovery of shrub canopy cover in sagebrush-steppe communities with the use of spectral mixture analysis. Our study included 16 different fires that burned between 1937 and 2005 and one unburned site at the US Sheep Experiment Station in eastern Idaho. Spectral mixture analysis was used with September 2006 Systeme Pour l’Observation de la Terre-5 (SPOT-5) satellite imagery to estimate percent shrub canopy cover within pixels. Very large-scale aerial (VLSA) imagery with 24-mm resolution was used for training and validation. SPOT-5 image classification was successful and the spectral mixture analysis estimates of percent shrub canopy cover were highly correlated with the shrub canopy cover estimates in the VLSA imagery (R² = 0.82; P < 0.0001). Additional accuracy assessment of shrub classification produced 85% overall accuracy, 98% user’s accuracy, and 78% producer’s accuracy. This successful application of spectral mixture analysis has important implications for the monitoring and assessment of sagebrush-steppe communities. With the use of the percent shrub canopy cover estimates from the classified SPOT-5 imagery, we examined shrub canopy recovery rates since different burn years. With the use of linear-plateau regression, it was determined that shrub cover in mountain big sagebrush (Artemisia tridentata Nutt. subsp. vaseyana [Rydb.] Beetle) communities recovered approximately 27 yr after fire, with an average shrub cover of 38%. These results are consistent with other field-based studies in mountain big sagebrush communities.     

A Technique for Estimating Rangeland Canopy-Gap Size Distributions From High-Resolution Digital Imagery

The amount and distribution of gaps in vegetation canopy is a useful indicator of multiple ecosystem processes and functions. In this paper, we describe a semiautomated approach for estimating canopy-gap size distributions in rangelands from high-resolution (HR) digital images using image interpretation by observers and statistical image classification techniques. We considered two different classification methods (maximum-likelihood classification and logistic regression) and both pixel-based and object-based approaches to estimate canopy-gap size distributions from 2- to 3-cm resolution UltraCamX color infrared aerial photographs for arid and semiarid shrub sites in Idaho, Nevada, and New Mexico. We compare our image-based estimates to field-based measurements for the study sites. Generally, percent of input points correctly classified and kappa coefficients of agreement for plot image classifications was very high. Plots with low kappa values yielded canopy gap estimates that were very different from field-based estimates. We found a strong relationship (R² > 0.9 for all four methods evaluated) between image- and field-based estimates of the total percent of the plot in canopy gaps greater than 50 cm for plots with a classification kappa of greater than 0.5. Performance of the remote sensing techniques varied for small canopy gaps (25 to 50 cm) but were very similar for moderate (50 to 200 cm) and large (> 200 cm) canopy gaps. Our results demonstrate that canopy-gap size distributions can be reliably estimated from HR imagery in a variety of plant community types. Additionally, we suggest that classification goodness-of-fit measures are a potentially useful tool for identifying and screening out plots where precision of estimates from imagery may be low. We conclude that classification of HR imagery based on observer-interpreted training points and image classification is a viable technique for estimating canopy gap size distributions. Our results are consistent with other research that has looked at the ability to derive monitoring indicators from HR imagery.     

Season,Classifier, and Spatial Resolution Impact Honey Mesquite and Yellow Bluestem Detection using an Unmanned Aerial System

In Texas, mesquite and yellow-bluestem invasions are widespread. Identifying and monitoring juvenile and adult plants using high-resolution imagery from airborne sensors while they colonize new areas across the landscape can help land managers prioritize locations for treatment and eradication. In this study, we evaluated how data collection design using an unmanned aerial system (UAS) can affect plant detection and mapping. We used a Phantom 3 Professional unmanned aerial vehicle with a Parrot Sequoia multispectral camera for detecting and mapping native honey mesquite (Prosopis glandulosa) and non-native yellow bluestem (Bothriochloa ischaemum) at a rangeland site in northwest Texas. Flights were conducted seasonally during the period from summer 2017 to fall 2018 to test the seasonal impact of detecting plant species. Flights were conducted at altitudes of 30, 60, and 100 m, and four image classification techniques were tested to determine their viability of detecting distinct plant species. Results suggest that flights at 100-m aircraft altitude during the spring season are more effective (>80% user accuracies) for mapping mesquite canopies based on reflectance values and image segmentation information. Yellow bluestem mapping accuracies were low (< 20% user accuracies). Lower spatial resolution (100-m altitude flights, 12-cm pixel resolution) provided less noise and more generalization capabilities for the image classification methods. Overall, random forests and Support Vector Machine classification algorithms outperformed probability-based image classifiers. Land owners and rangeland ecologists using their own UAS in rangeland management can use this information to plan their data collection campaigns before the application of chemical treatments or manual eradication.     

Agreement Between Measurements of Shrub Cover Using Ground-Based Methods and Very Large Scale Aerial Imagery

Very large scale aerial (VLSA) photography is a remote sensing method, which is collected and analyzed more efficiently than ground-based measurement methods, but agreement with ground-based measurements needs to be quantified. In this study, agreement between ground- and image-measured cover and precision, and accuracy of image locations and scale, were assessed. True image locations were determined by georeferencing images and conducting a ground search. Accuracy and precision of planned, aircraft, and georeferenced locations were evaluated by comparison with true image locations. Shrub cover was measured at true image locations using ground-based line-intercept and on the image using point-intercept. Sagebrush (Artemisia spp. L.), antelope bitterbrush (Purshia tridentata &lsqb;Pursh] DC.), and spineless horsebrush (Tetradymia canescens DC.) were distinguished in the imagery. Agreement between ground- and image-based measurements was quantified using limit-of-agreement analysis. True ground locations of the VLSA images were within a 41-m radius of the aircraft location at the time of image acquisition, with 95% confidence. Using a panchromatic image from the QuickBird satellite (0.6-m pixel resolution) as a base map, 90% of true ground locations were within a 5-m radius of the location estimated from georeferencing the VLSA image to the base map. VLSA image-measured cover was, in general, unbiased with mean absolute differences between VLSA- and ground-based methods less than 1.3%. The degree of agreement and absence of bias between VLSA image–measured and ground-measured cover is sufficient to recommend using VLSA imagery to measure shrub cover.     

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。     

Assessment of Juniper Encroachment With The Use of Satellite Imagery and Geospatial Data

Juniper encroachment into otherwise treeless shrub lands and grasslands is one of the most pronounced environmental changes observed in rangelands of western North America in recent decades. Most studies on juniper change are conducted over small areas, although encroachment is occurring throughout regions. Whether changes in juniper cover can be assessed over large areas with the use of long-term satellite data is an important methodological question. A fundamental challenge in using satellite imagery to determine tree abundance in rangelands is that a mix of trees, sagebrush, and herbaceous cover types can occur within a given image pixel. Our objective was to determine if spectral mixture analysis could be used to estimate changes in Rocky Mountain juniper (Juniperus scopulorum Sarg) and Utah juniper (Juniperus osteosperma [Torr.] Little) cover over 20 yr and 20000 ha in southeast Idaho with the use of Landsat imagery. We also examined the spatial patterns and variation of encroachment within our study area using Geographic Information Systems–based data sets of grazing use, land-cover types, and topography. Juniper cover determined from 15-cm-resolution digital aerial orthophotography was used to train and validate juniper presence/absence classification in 1985 and 2005 Landsat images. The two classified images were then compared to detect changes in juniper cover. The estimated rate of juniper encroachment over our study area was 22–30% between 1985 and 2005, consistent with previous ground-based studies. Moran’s I analysis indicated that juniper encroachment pattern was spatially random rather than clustered or uniform. Juniper encroachment was significantly greater in grazed areas (P = 0.02), and in particular in grazed shrub land cover type (P = 0.06), compared to ungrazed areas. Juniper encroachment was also greater on intermediate slopes (10–35% slopes) compared to steeper or flatter terrain, and encroachment was somewhat less on north-facing (P = 0.03) and more on west-facing (P = 0.02) slopes compared to other aspects.     

Towards real-time prediction of Rift Valley fever epidemics in Africa

Outbreaks of Rift Valley fever (RVF) virus in Africa are characterized by distinct spatial and temporal patterns that are directly related to specific environmental parameters associated with mosquito vectors that function in the maintenance (endemic) and transmission (epizootic) cycles of the virus. National Oceanic and Atmospheric Administration (NOAA) satellites with limited resolution (1 km) can indirectly measure rainfall inexpensively over subcontinental or continental areas at a high temporal frequency, and identify regions with a high potential for viral activity. Within regions of likely viral activity, mosquito vector-breeding habitats (dambos) are identified and mapped with archived, high-quality, cloud-free, higher resolution data from LANDSAT (thematic mapper resolution 30 m) and SPOT (multispectral resolution 20 m) satellites. Active sensors, like airborne synthetic aperture radar systems (range resolution 1.6 m), are capable of detecting flooded habitats, even through cloud cover. By identifying flooded habitats within regions with a high likelihood of RVF activity, the potential source foci of an outbreak may be detected close to real-time and control efforts implemented prior to the start of a RVF epidemic/epizootic.     

基于IKONOS高分辩率影像的城市草地信息提取研究

在IKONOS影像图上提取草地信息试用了2种方法进行探索,分别采用植被指数法和基于灰度共生矩阵的纹理量分类法.前者用MSAVI指数分类提取得到的精度为87.48%;后一种方法是通过从近红外波段提取灰度共生矩阵和灰度联合矩阵,计算并提取理想窗口的最能反映类别差异的纹理量值,试验发现取3X3窗口的CON纹理量可以较好地提取出草地信息,通过精度评估发现具有较高的精度(平均精度达90.56%).研究证明用该法提取草地信息可取得相对理想的精度效果.