基于超像元词包特征和主动学习的高分遥感影像变化检测     

杨进一  徐伟铭  王成军  翁谦 《地球信息科学学报》2019,21(10):1594-1607

为解决高分辨率遥感影像变化检测中存在底层特征缺乏语义信息、像元级的检测结果存在“椒盐”现象以及监督分类中样本标注自动化程度较低,本文提出一种基于超像元词包特征和主动学习的变化检测方法。首先采用熵率分割算法获取叠加影像的超像元对象;其次提取两期影像像元点对间的邻近相关影像特征（相关度、斜率和截距）和顾及邻域的纹理变化强度特征（均值、方差、同质性和相异性）,经线性组合作为像元点对的底层特征;然后基于像元点对底层特征利用BOW模型构建超像元词包特征,并采用一种改进标注策略的主动学习方法从无标记样本池中优选信息量较大的样本,且自动标注样本类别;最后训练分类器模型完成变化检测。通过选用2组不同地区的GF-2影像和Worldview-Ⅱ影像作为数据源进行实验,实验结果中2组数据集的F1分数分别为0.8714、0.8554,正确率分别为0.9148、0.9022,漏检率分别为0.1681、0.1868,误检率分别为0.0852、0.0978。结果表明,该法能有效识别变化区域、提高变化检测精度。此外,传统主动学习方法与改进标注策略的主动学习方法的学习曲线对比显示,改进的标注策略可在较低精度损失下,有效提高样本标注自动化程度。  相似文献   

Extracting water-related features using reflectance data and principal component analysis of Landsat images     

Boglárka Balázs  Tibor Bíró  Gareth Dyke  Sudhir Kumar Singh 《水文科学杂志》2018,63(2):269-284

This study aimed to map water features using a Landsat image rather than traditional land cover. We involved the original bands, spectral indices and principal components (PCs) of a principal component analysis (PCA) as input data, and performed random forest (RF) and support vector machine (SVM) classification with water, saturated soil and non-water categories. The aim was to compare the efficiency of the results based on various input data. Original bands provided 93% overall accuracy (OA) and bands 4–5–7 were the most informative in this analysis. Except for MNDWI (modified normalized differenced water index, with 98% OA), the performance of all water indices was between 60 and 70% (OA). The PCA-based approach conducted on the original bands resulted in the most accurate identification of all classes (with only 1% error in the case of water bodies). We therefore show that both water bodies and saturated soils can be identified successfully using this approach.  相似文献   

基于多时相Landsat影像的中亚地区植被覆盖遥感监测          下载免费PDF全文

王华  杨乾鹏  田云杰  郭山川  唐鹏飞 《干旱区地理》2020,43(4):1023-1032

针对中亚地区的强生态脆弱性、高敏感性特征,有必要开展广域、长期的植被覆盖监测以匹配“绿色丝绸之路”的可持续发展目标。鉴于此,联合Landsat 5和Landsat 8卫星数据集,利用Google Earth Engine（GEE）地理空间数据云计算平台,估算了中亚地区1993—2018年间共12期的植被覆盖度。结果表明：（1）中亚地区植被覆盖总体水平较低,但也具有较为显著的空间异质性。（2）中亚地区1993—2018年间多数区域植被覆盖趋势较为稳定,哈萨克斯坦丘陵、费尔干纳盆地等区域植被覆盖度呈增加趋势,乌拉尔河流域和锡尔河流域等区域植被覆盖趋势为负。（3）植被覆盖度时序特征上,中亚地区1993—2018年间总体植被覆盖度累积增加3%,其中吉尔吉斯斯坦和塔吉克斯坦植被覆盖分别增加3.96%和5.86%。（4）裸土区呈退缩趋势,面积总计减少25.9×10⁴ km²,低植被覆盖区、中植被覆盖区和高植被覆盖区范围在呈现出的振荡式增加。研究结合遥感大数据和地理云计算对中亚地区进行区域尺度的植被覆盖动态监测,能对中亚地区生态评估和演替分析提供技术支持和定量数据。  相似文献   

Wall-to-wall spatial prediction of growing stock volume based on Italian National Forest Inventory plots and remotely sensed data     

《International Journal of Applied Earth Observation and Geoinformation》2020

Spatial predictions of forest variables are required for supporting modern national and sub-national forest planning strategies, especially in the framework of a climate change scenario. Nowadays methods for constructing wall-to-wall maps and calculating small-area estimates of forest parameters are becoming essential components of most advanced National Forest Inventory (NFI) programs. Such methods are based on the assumption of a relationship between the forest variables and predictor variables that are available for the entire forest area. Many commonly used predictors are based on data obtained from active or passive remote sensing technologies. Italy has almost 40% of its land area covered by forests. Because of the great diversity of Italian forests with respect to composition, structure and management and underlying climatic, morphological and soil conditions, a relevant question is whether methods successfully used in less complex temperate and boreal forests may be applied successfully at country level in Italy.For a study area of more than 48,657 km² in central Italy of which 43% is covered by forest, the study presents the results of a test regarding wall-to-wall, spatially explicit estimation of forest growing stock volume (GSV) based on field measurement of 1350 plots during the last Italian NFI. For the same area, we used potential predictor variables that are available across the whole of Italy: cloud-free mosaics of multispectral optical satellite imagery (Landsat 5 TM), microwave sensor data (JAXA PALSAR), a canopy height model (CHM) from satellite LiDAR, and auxiliary variables from climate, temperature and precipitation maps, soil maps, and a digital terrain model.Two non-parametric (random forests and k-NN) and two parametric (multiple linear regression and geographically weighted regression) prediction methods were tested to produce wall-to-wall map of growing stock volume at 23-m resolution. Pixel level predictions were used to produce small-area, province-level model-assisted estimates. The performances of all the methods were compared in terms of percent root mean-square error using a leave-one-out procedure and an independent dataset was used for validation. Results were comparable to those available for other ecological regions using similar predictors, but random forests produced the most accurate results with a pixel level R² = 0.69 and RMSE_% = 37.2% against the independent validation dataset. Model-assisted estimates were more precise than the original design-based estimates provided by the NFI.  相似文献   

1990—2018年中巴经济走廊冰湖时空变化特征     

姚超  王欣  赵轩茹  魏俊锋  张勇 《冰川冻土》2020,42(1):33-42

中巴经济走廊是贯通南北丝路的关键枢纽。在全球变暖的背景下， 区域内冰川变化情况复杂， 部分冰川出现前进或跃动现象， 冰湖溃决的风险在不断上升， 进而威胁中巴经济走廊的建设与民生安全。基于1990—2018年Landsat TM/ETM+/OLI遥感影像， 利用目视解译方法提取了中巴经济走廊3期冰湖编目数据， 并分析了28年来该区域内冰湖的总体变化趋势、 空间异质性以及成因。结果表明： 中巴经济走廊目前共发育有2 380个冰湖， 总面积为（131.76±19.08） km²， 集中分布于喀喇昆仑山脉和喜马拉雅山脉； 1990—2018年期间， 冰湖总体面积扩张速度为0.48%·a^-1， 但各个山脉不同规模冰湖面积变化差异较大。中巴经济走廊在气温和降水的共同作用下， 区内冰湖面积呈扩张趋势， 同时气温和降水变化率的空间差异使得冰湖面积变化存在空间差异； 冰川的快速退缩增加了区内冰湖溃决的风险。  相似文献   

Inter-comparison of remote sensing platforms for height estimation of mango and avocado tree crowns     

《International Journal of Applied Earth Observation and Geoinformation》2020

To support the adoption of precision agricultural practices in horticultural tree crops, prior research has investigated the relationship between crop vigour (height, canopy density, health) as measured by remote sensing technologies, to fruit quality, yield and pruning requirements. However, few studies have compared the accuracy of different remote sensing technologies for the estimation of tree height. In this study, we evaluated the accuracy, flexibility, aerial coverage and limitations of five techniques to measure the height of two types of horticultural tree crops, mango and avocado trees. Canopy height estimates from Terrestrial Laser Scanning (TLS) were used as a reference dataset against height estimates from Airborne Laser Scanning (ALS) data, WorldView-3 (WV-3) stereo imagery, Unmanned Aerial Vehicle (UAV) based RGB and multi-spectral imagery, and field measurements. Overall, imagery obtained from the UAV platform were found to provide tree height measurement comparable to that from the TLS (R² = 0.89, RMSE = 0.19 m and rRMSE = 5.37 % for mango trees; R² = 0.81, RMSE = 0.42 m and rRMSE = 4.75 % for avocado trees), although coverage area is limited to 1–10 km² due to battery life and line-of-sight flight regulations. The ALS data also achieved reasonable accuracy for both mango and avocado trees (R² = 0.67, RMSE = 0.24 m and rRMSE = 7.39 % for mango trees; R² = 0.63, RMSE = 0.43 m and rRMSE = 5.04 % for avocado trees), providing both optimal point density and flight altitude, and therefore offers an effective platform for large areas (10 km²–100 km²). However, cost and availability of ALS data is a consideration. WV-3 stereo imagery produced the lowest accuracies for both tree crops (R² = 0.50, RMSE = 0.84 m and rRMSE = 32.64 % for mango trees; R² = 0.45, RMSE = 0.74 m and rRMSE = 8.51 % for avocado trees) when compared to other remote sensing platforms, but may still present a viable option due to cost and commercial availability when large area coverage is required. This research provides industries and growers with valuable information on how to select the most appropriate approach and the optimal parameters for each remote sensing platform to assess canopy height for mango and avocado trees.  相似文献   

Estimating the starting time and identifying the type of urbanization based on dense time series of landsat-derived Vegetation-Impervious-Soil (V-I-S) maps – A case study of North Taiwan from 1990 to 2015     

《International Journal of Applied Earth Observation and Geoinformation》2020

Land cover and land use change (LCLUC) is a global phenomenon, and LCLUC in urbanizing regions has substantial impacts on humans and their environments. In this paper, a semi-automatic approach to identifying the type and starting time of urbanization was developed and tested based on dense time series of Vegetation-Impervious-Soil (V-I-S) maps derived from Landsat surface reflectance imagery. The accuracy of modeled V-I-S fractions and the estimated time of initial change in impervious cover were assessed. North Taiwan, one of the regions of the island of Taiwan that experienced the greatest urban LCLUC, was chosen as a test area, and the study period is 1990 to 2015, a period of substantial urbanization. In total, 295 dates of Landsat imagery were used to create 295 V-I-S fraction maps that were used to construct fractional cover time series for each pixel. Root Mean Square Error (RMSE)s for the modeled Vegetation, Impervious, and Soil were 25 %, 22 %, 24 % respectively. The time of Urban Expansion is estimated by logistic regression applied to Impervious cover time series, while the time of change for Urban Renewal is determined by the period of brief Soil exposure. The identified location and estimated time for newly urbanized lands were generally accurate, with 80% of Urban Expansion estimated within ±2.4 years. However, the accuracy of identified Urban Renewal was relatively low. Our approach to identifying Urban Expansion with dense time series of Landsat imagery is shown to be reliable, while Urban Renewal identification is not.  相似文献   

Satellite-based monitoring and statistics for raft and cage aquaculture in China’s offshore waters     

《International Journal of Applied Earth Observation and Geoinformation》2020

Based on Landsat 8 remote sensing images, a combination of an unsupervised classification algorithm and artificial review was used to extract areas for Chinese offshore raft and cage aquaculture in 2018. The results of the extraction showed that China’s 2018 coastal zone raft aquaculture area comprised 194,110 ha, of which the province having the largest raft aquaculture area was Jiangsu (28.77 %), followed by Fujian (20.42 %) and Shandong (13.11 %). The cage aquaculture area covered 57,847,799 square meters, of which the provinces with the largest cage aquaculture area were Fujian (64.81 %), Guangdong (17.45 %), and Liaoning (5.63 %). In addition, by combining high-resolution remote sensing image visual interpretation and field investigation, the classification of 1200 sample points in four regions was determined, and the accuracy of the aquaculture area extraction was found to be 87.35 %. The extraction results can be used not only to evaluate China’s aquaculture production but also offer significant reference value for scientific planning related to sea use, ecological environmental protection, and marine disaster prevention and mitigation.  相似文献   

多时相Landsat影像的平原河网区不透水面信息提取方法与应用     

周峰  廖钰冰  李娜 《测绘通报》2020,(1):94

不透水面是衡量城市生态环境的重要指标，针对平原河网区不透水面与水体、裸土等地物较难区分的问题，以苏北里下河平原快速城镇化区为研究对象，利用多时相Landsat影像为数据源，基于像元光谱特征及其季节性变化分析，构建了基于时序NDWI、NDVI和NDBI的决策树模型，并利用GIS空间分析技术对不透水面的时空变化特征进行了分析。结果表明：多时相光谱信息可有效改善水体、裸土和稀疏植被等像元与不透水面的混分，2005年和2016年的不透水面分类总体精度和Kappa系数均在0.85以上，合理反映了不透水面以向东和向南扩张为主的趋势特征，年均扩张速度约为6.7 km²。研究成果为该区城镇化下的生态环境效应研究提供数据基础，同时可为其他平原河网区不透水面信息提供借鉴和参考。  相似文献   

黄土丘陵沟壑区典型村域土地利用变化及对区域乡村转型发展的启示     

姜凯斯  刘正佳  李裕瑞  王永生  王昱 《地理科学进展》2019,38(9):1305-1315

深入开展乡村地域土地利用变化和聚落发展规律的研究对于探究乡村地域系统优化、推进城乡融合与乡村可持续发展具有重要意义。论文以延安市碾庄流域为例,基于ENVI和GIS等软件技术,结合Landsat卫星影像、马尔科夫链模型和实地调查,从土地利用转移与交换变化、建筑用地空间转移、社会响应等角度探讨了黄土丘陵沟壑区典型村域土地利用变化特征以及对区域乡村发展转型的影响。研究表明：① 在一系列生态工程措施作用下,碾庄流域生态覆被发生明显改观,主要表现为大量的坡耕地及稀疏生态用地转化为林地,其中,林地面积占比由32.34%(2009年)增加至50.88%(2018年);② 在社会经济发展和延安新城“边缘效应”的影响下,碾庄建设用地呈现向流域的东南和西南方向拓展的趋势,10 a间建设用地面积增加了近75%;③ 在人类活动的主导作用下,黄土丘陵沟壑区的乡村正在朝着生态化和集约化的方向转型发展。研究结果有望为深入理解黄土丘陵沟壑区乡村地域系统结构和优化提供新参考。  相似文献