甘蔗茎外皮机械特性检测及像素分析

[目的]更加精确地表征甘蔗茎外皮的机械特性。[方法]将传统拉伸试验与像素分析法相结合,测算出不同组别甘蔗茎外皮试件的拉伸强度及弹性模量,进而进行对比分析。[结果]研究表明,4组试件的拉伸强度整体呈下降趋势,经碱处理不带有甘蔗节的试件的拉伸强度最高,而未经碱处理带有甘蔗节的试件,其拉伸强度相对微弱;较带有甘蔗节的试件而言,不带甘蔗节的试件的弹性模量相对较大。[结论]综合来看,碱处理可在一定程度上提高甘蔗皮的机械性能;甘蔗节对甘蔗皮机械特性有所影响,但影响较小。     

Modeling grain-size dependent bias in estimating forest area: a regional application

A better understanding of scaling-up effects on estimating important landscape characteristics (e.g. forest percentage) is critical for improving ecological applications over large areas. This study illustrated effects of changing grain sizes on regional forest estimates in Minnesota, Wisconsin, and Michigan of the USA using 30-m land-cover maps (1992 and 2001) produced by the National Land Cover Datasets. The maps were aggregated to two broad cover types (forest vs. non-forest) and scaled up to 1-km and 10-km resolutions. Empirical models were established from county-level observations using regression analysis to estimate scaling effects on area estimation. Forest percentages observed at 30-m and 1-km land-cover maps were highly correlated. This intrinsic relationship was tested spatially, temporally, and was shown to be invariant. Our models provide a practical way to calibrate forest percentages observed from coarse-resolution land-cover data. The models predicted mean scaling effects of 7.0 and 12.0% (in absolute value with standard deviations of 2.2 and 5.3%) on regional forest cover estimation (ranging from 2.3 and 2.5% to 11.1 and 23.7% at the county level) with standard errors of model estimation 3.1 and 7.1% between 30 m and 1 km, and 30 m and 10 km, respectively, within a 95% confidence interval. Our models improved accuracy of forest cover estimates (in terms of percent) by 63% (at 1-km resolution) and 57% (at 10-km resolution) at the county level relative to those without model adjustment and by 87 and 84% at the regional level in 2001. The model improved 1992 and 2001 regional forest estimation in terms of area for 1-km maps by 15,141 and 7,412 km² (after area weighting of all counties) respectively, compared to the corresponding estimates without calibration using 30 m-based regional forest areas as reference.     

Assessment of gully erosion susceptibility using different DEM-derived topographic factors in the black soil region of Northeast China

As a primary sediment source, gully erosion leads to severe land degradation and poses a threat to food and ecological security. Therefore, identification of susceptible areas is critical to the prevention and control of gully erosion. This study aimed to identify areas prone to gully erosion using four machine learning methods with derived topographic attributes. Eight topographic attributes (elevation, slope aspect, slope degree, catchment area, plan curvature, profile curvature, stream power index, and topographic wetness index) were derived as feature variables controlling gully occurrence from digital elevation models with four different pixel sizes (5.0 m, 12.5 m, 20.0 m, and 30.0 m). A gully inventory map of a small agricultural catchment in Heilongjiang, China, was prepared through a combination of field surveys and satellite imagery. Each topographic attribute dataset was randomly divided into two portions of 70% and 30% for calibrating and validating four machine learning methods, namely random forest (RF), support vector machines (SVM), artificial neural network (ANN), and generalized linear models (GLM). Accuracy (ACC), area under the receiver operating characteristic curve (AUC), root mean square error (RMSE), and mean absolute error (MAE) were calculated to assess the performance of the four machine learning methods in predicting spatial distribution of gully erosion susceptibility (GES). The results suggested that the selected topographic attributes were capable of predicting GES in the study catchment area. A pixel size of 20.0 m was optimal for all four machine learning methods. The RF method described the spatial relationship between the feature variables and gully occurrence with the greatest accuracy, as it returned the highest values of ACC (0.917) and AUC (0.905) at a 20.0 m resolution. The RF was also the least sensitive to resolutions, followed by SVM (ACC = 0.781–0.891, AUC = 0.724–0.861) and ANN (ACC = 0.744–0.808, AUC = 0.649–0.847). GLM performed poorly in this study (ACC = 0.693–0.757, AUC = 0.608–0.703). Based on the spatial distribution of GES determined using the optimal method (RF + pixel size of 20.0 m), 16% of the study area has very high level susceptibility classes, whereas areas with high, moderate, and low levels of susceptibility make up approximately 24%, 30%, and 31% of the study area, respectively. Our results demonstrate that GES assessment with machine learning methods can successfully identify areas prone to gully erosion, providing reference information for future soil conservation plans and land management. In addition, pixel size (resolution) is the key consideration when preparing suitable datasets of feature variables for GES assessment.     

一种基于Photoshop的叶片相对病斑面积快速测定方法

[目的]提出一种利用Photoshop软件快速测量作物叶片相对病斑面积（RLA）的新方法。[方法]利用相机或扫描仪等获取病叶图像后,直接利用Photoshop软件显示病斑及整个叶片的像素,利用像素数计算出图像中的叶片相对病斑面积。[结果]分别通过1组检验试验和重复测量试验来验证该方法,发现该方法准确,重复性好,误差小。[结论]与利用其他软件求RLA方法相比,该方法简单,快速,准确且便于操作,具有广阔的应用前景。     

QuickBird遥感图像变形定量估测模型

以深圳市区一景QuickBird Pan波段遥感图像(总行列数为26 574×28 606,对应15.944 km×17.164km的实地范围)为研究对象,利用深圳市区GPS虚拟参考网络定位系统,以厘米级精度测定了65个在图像上易于识别的地面控制点三维坐标,通过65个控制点之间真实距离和在变形图像上对应距离之间的偏差,在分析线性阵列传感器成像原理,遥感图像像点位移影响因素及规律的基础上,利用平均残差平方和准则,系统分析了Quick-Bird Pan波段遥感图像的变形规律和定量估测模型。研究成果对高空间分辨率遥感图像几何精纠正算法研究具有应用价值。     

Remote sensing applications for monitoring rangeland vegetation

Remote sensing techniques hold considerable promise for the inventory and monitoring of natural resources on range‐lands. A significant lack of information concerning basic spectral characteristics of range vegetation and soils has resulted in a lack of rangeland applications. To use remote sensing technology for measuring vegetation and soil changes on rangelands, certain things must be accomplished. First, the spectral characteristics of scene components must be determined for the various vegetation types. Second, determinations must be made of the appropriate kinds of remotely sensed data that should be used for the task. And finally, procedures must be outlined for acquiring the remotely sensed data that will measure changes in the range vegetation and soils. The interpretation of the remotely sensed data for such monitoring purposes will depend upon the use of various vegetation indices, pixel modelling and appropriate statistical tests. The parameters of interest for range condition must be identified and a means of measuring them either directly or indirectly developed. The paper describes an approach to the use of remotely sensed data to accomplish rangeland monitoring.     

基于机器视觉技术的无精蛋鉴别研究

[目的]采用机器视觉技术鉴别无精蛋,提高孵化生产过程中剔除无精蛋的准确率和速度。[方法]首先用CCD照相机获取入孵蛋图像,再将入孵蛋图像传输到计算机主机,主机测控软件对图像进行灰度特征分析、比较等,对入孵蛋进行检测,主机输出信号给核心控制器,由核心控制器控制检测台对无精蛋进行分选。[结果]采用机器视觉技术可以在入孵后第6天快速准确地剔除无精蛋。[结论]利用机器视觉检测技术鉴别无精蛋,具有较高的理论价值和实际生产意义。     

改进的蚁群算法在灌区渠系优化配水中的应用研究

针对我国大多数地区渠系配水通用性较低的问题,将农田水利作为农村基础设施建设的重点任务,以吉林省大安灌区渠系为例,基于地理信息及遥感测控技术,通过改进的蚁群算法对灌区渠系进行优化配水,最终达到节水灌溉增产的效果。在北方实际灌溉过程中,优化配水在农业节水灌溉领域中具有重要的意义,其算法简单,收敛性好。采用改进的蚁群算法可以使渠系配水模式在满足一定约束条件下将水量损失降到最低,使有限的水资源发挥最大的作用。结果更贴近实际,值得全国范围推广应用。     

基于像元尺度的移民区资源承载力评价模型及应用--以宁夏红寺堡区为例

[目的]利用GIS技术从社会经济发达度和可利用资源两方面构建基于像元尺度的资源承载力综合评价模型。[方法]监测局部区域的资源承载力状况并分析空间变化趋势,以红寺堡区为例研究其资源承载力状况。[结果]红寺堡区资源承载力存在明显的区域差异,中部地区承载力较高,南部地区承载力较低,符合红寺堡区资源开发与利用现实状况。[结论]基于像元尺度的资源承载力评估模型能够将评价单元细化到像元尺度,有利于弥补传统评价方法无法反映区域内部异质性的不足。     

矢量中值滤波彩色图像增强算法研究

矢量中值作为基于排序的非线性滤波方法,在消除彩色图像的噪声和保证图像边缘与细节上均有较好的稳健性.但是,由于其算法与窗口象素数的平方成正比,因而计算量很大.本研究提出一种改进的矢量中值滤波的彩色图像增强方法,该方法既可以提高其图像处理的运算速度,又能保证图像质量,其算法具有实用价值.