云阴影区机载高光谱影像森林树种分类

[目的]使用窄波段植被指数、纹理信息等特征对影像进行分类,探究植被指数和纹理信息对于云阴影下树种分类的潜力。[方法]使用经过大气校正后的高光谱影像进行窄波段植被指数的计算、纹理分析以及主成分分析,并对计算的结果进行波段组合。用于计算纹理信息的波段通过最佳指示因子进行选择,选取的波段数为31(0.67 nm),51(0.86 nm),55(0.89 nm) 3个波段。结合高分辨率的航空相片进行训练样本的选择,采用Support Vector Machine(SVM)方法对经过大气校正后的反射率影像和重组后的特征影像分别进行分类,使用样地实测的树种信息对分类结果进行验证,使用总体精度和Kappa系数作为分类精度的评价指标。[结果]相对于直接使用反射率影像进行分类,使用窄波段植被指数以及纹理信息可以显著地提高云阴影下地物的分类精度,其分类精度和Kappa系数分别为90.4%和0.88,比直接使用反射率影像的分类精度和Kappa系数分别提高了18%和0.2。[结论]使用重新组合后的影像进行树种分类比直接使用反射率影像进行分类,其分类精度更高,说明窄波段植被指数与纹理特征可以提高云阴影区树种分类的精度。使用波段重组后的影像对云阴影下地物分类,其对于单个地物的分类精度也有明显的提高。     

Estimating aboveground biomass of Pinus densata-dominated forests using Landsat time series and permanent sample plot data

Southwest China is one of three major forest regions in China and plays an important role in carbon sequestration.Accurate estimations of changes in aboveground biomass are critical for understanding forest carbon cycling and promoting climate change mitigation.Southwest China is characterized by complex topographic features and forest canopy structures,complicating methods for mapping aboveground biomass and its dynamics.The integration of continuous Landsat images and national forest inventory data provides an alternative approach to develop a long-term monitoring program of forest aboveground biomass dynamics.This study explores the development of a methodological framework using historical national forest inventory plot data and Landsat TM timeseries images.This method was formulated by comparing two parametric methods:Linear Regression for Multiple Independent Variables(MLR),and Partial Least Square Regression(PLSR);and two nonparametric methods:Random Forest(RF)and Gradient Boost Regression Tree(GBRT)based on the state of forest aboveground biomass and change models.The methodological framework mapped Pinus densata aboveground biomass and its changes over time in Shangri-la,Yunnan,China.Landsat images and national forest inventory data were acquired for 1987,1992,1997,2002 and 2007.The results show that:(1)correlation and homogeneity texture measures were able to characterize forest canopy structures,aboveground biomass and its dynamics;(2)GBRT and RF predicted Pinus densata aboveground biomass and its changes better than PLSR and MLR;(3)GBRT was the most reliable approach in the estimation of aboveground biomass and its changes;and,(4)the aboveground biomass change models showed a promising improvement of prediction accuracy.This study indicates that the combination of GBRT state and change models developed using temporal Landsat and national forest inventory data provides the potential for developing a methodological framework for the long-term mapping and monitoring program of forest aboveground biomass and its changes in Southwest China.     

Land use change modeling through an integrated Multi-Layer Perceptron Neural Network and Markov Chain analysis(case study: Arasbaran region,Iran)

Temporal land use/land cover (LULC) change information provides a variety of applications for informed management of land resources. The aim of this study was to detect and predict LULC changes in the Arasbaran region using an integrated Multi-Layer Perceptron Neural Network and Markov Chain analysis. At the first step, multi-temporal Landsat images (1990, 2002 and 2014) were processed using ancillary data and were classified into seven LULC categories of high density forest, low-density forest, agriculture, grassland, barren land, water and urban area. Next, LULC changes were detected for three time profiles, 1990–2002, 2002–2014 and 1990–2014. A 2014 LULC map of the study area was further simulated (for model performance evaluation) applying 1990 and 2002 map layers. In addition, a collection of spatial variables was also used for modeling LULC change processes as driving forces. The actual and simulated 2014 LULC change maps were cross-tabulated and compared to ensure model simulation success and the results indicated an overall accuracy and kappa coefficient of 97.79% and 0.992, respectively. Having the model properly validated, LULC change was predicted up to the year 2025. The results demonstrated that 992 and 1592 ha of high and lowdensity forests were degraded during 1990–2014,respectively, while 422 ha were added to the extent of residential areas with a growth rate of 17.58 ha per year. The developed model predicted a considerable degradation trend for the forest categories through 2025, accounting for 489 and 531 ha of loss for high and low-density forests, respectively. By way of contrast, residential area and farmland categories will increase up to 211 and 427 ha, respectively. The integrated prediction model and customary area data can be used for practical management efforts by simulating vegetation dynamics and future LULC change trajectories.     

一种基于航空可见光图像的烟草数量统计方法

传统烟草(Nicotiana tabacum L.)数量清点工作主要依靠人工现场抽样的方式,这种方法费时、费力且统计误差较大。针对这一缺点,提出一种基于航空可见光图像处理的烟草数量统计方法。利用无人机所获取的高分辨率影像,采用K-means聚类方法对烟田图像进行图像分割分类,提取图像中绿色植物部分,提取颜色、面积、长宽比等简单特征对杂草进行预剔除,通过构建烟株与杂草样本库,利用灰度梯度共生矩阵,提取其灰度平均、梯度均方差、相关、惯性等4种特征参量,并基于BP神经网络算法进一步对杂草进行识别,剔除杂草,统计烟株,提取连通域数量,即为烟株数量。     

Flash动画在中国农民科普教育中的应用研究

Flash动画是数字科普最常见的形式和实现方法之一,利用Flash动画的技术、制作和传播优势,笔者探讨以家庭为单位的科普教育新模式,提高农民科学文化素质。以北京市郊区县农民为例,对当前农村科普教育的手段和形式进行了调研,分析比较了Flash动画在农村、农民科普教育中的受众对象和应用效果;针对农户的年龄和家庭结构,提出了利用Flash动画形式以家庭为单位进行科普教育的新模式,作为中国农民科普教育形式的有益补充,并提出了具体的推进措施。     

基于GF-1与Landsat8 OLI影像的作物种植结构与产量分析

作物种植结构监测和估产是精准农业遥感的重点领域,其研究对于指导作物种植结构和制定农业政策具有重要意义。该文以黑龙江省北安市为研究区,以2015年的Landsat8 OLI和多时相GF-1为遥感数据源,基于物候信息和光谱特征确定的农作物识别关键时期和特征参数,构建面向对象的决策树分类模型,开展作物种植结构监测研究;综合植被光谱指数和地面采样数据,采用逐步回归方法建立产量遥感估算模型。结果表明:多源与多时相的遥感数据可以反映不同农作物的季相特征,应用本文所构建的决策树分类模型,作物分类效果较好,总体精度达87.54%,Kappa系数为0.8115;2015年,北安市的主要作物类型为大豆、玉米、水稻和小麦,面积分别为2204、1955、122和19 km~2,其中大豆的种植面积最大,占作物种植面积的51.24%。基于NDVI、EVI和GNDVI构建的多元回归模型为北安市大豆和玉米产量估算最优模型(R~2=0.823 7,均方根误差135.45 g/m~2,精度80.55%);北安市玉米高产区集中分布在西部,大豆的高产区主要分布在东部;2015年北安市玉米和大豆的单产分别为8 659、2 846 kg/hm~2,总产量分别为16.93×10~8、6.27×10~8 kg。利用作物关键物候期的多源多时相遥感数据能够精确高效地提取作物种植结构,构建的产量估算多元回归模型,为精准农业科学发展提供参考。     

西鄂尔多斯荒漠化动态分析

以20世纪80年代末和2005年两期TM影像作信息源,对西鄂尔多斯鄂托克旗(以下简称鄂旗)境内的荒漠化土地现状与动态变化进行了监测研究。结果表明:近30年来,鄂旗荒漠化土地总面积变化较大,增长6.9%,荒漠化程度也明显加重,中度荒漠化面积明显增加,其中中度沙质地荒漠化面积增长了20.80%,中度盐渍化荒漠化面积增加了35.65%。     

Comparison of illuminations to identify wheat classes using monochrome images

Wheat class identification using machine vision is an objective method which can be used for online testing to automate handling, binning and shipping operations in grain industry. The efficiencies of a monochrome camera-based vision system with three different illuminations (incandescent light (IL), fluorescent ring light (FRL), fluorescent tube light (FTL)) were determined to identify eight western Canadian wheat classes at four moisture levels (11%, 14%, 17% and 20%). The monochrome images of the bulk wheat samples were acquired at each moisture level (3 illuminations × 8 classes × 4 moisture contents × 100 replications = 9600 images). A linear discriminant function was used for the classification of wheat samples using 32 gray level textural features extracted from the monochrome images. The mean gray values of the wheat classes were in the ranges of 75–103, 73–115, and 107–143 for IL, FRL and FTL, respectively. The mean gray values of wheat classes were significantly different within each illumination and between different illuminations (α = 0.05). Mean gray value was the highest for FTL and the lowest for IL illumination. The moisture content of the wheat samples had significant effect on the mean gray values. The overall classification accuracies were 90%, 81% and 96% for IL, FRL and FTL, respectively, when all the wheat classes were at the same moisture levels. It was 66%, 53% and 85% for IL, FRL and FTL, respectively, when the wheat classes were at different moisture levels. The classification accuracies of a 2-stage classification system for the classes with different moisture levels were 68%, 56% and 90% for IL, FRL and FTL, respectively.     

遥感影像在三北防护林监测管理中的应用研究

通过选取甘肃省景泰县两个时段的卫星影像并经过加工处理,分析比较两个时期该区域的植被变化情况和土地利用变化情况,并计算出两个时段的具体数据,从该县近年各项林业工程建设任务的角度分析土地利用变化和森林消长的原因,首次尝试应用美国TM遥感卫星影像和法国SPOT卫星影像结合调查三北防护林的监测管理。通过该例研究和分析,为在三北工程建设区大面积推广应用遥感数据进行工程监测和管理提供有益的探索和实践。