基于Sentinel-1与Landsat 8的巢湖地区洪水淹没灾害分析

为及时准确得到阴雨天气下的洪水淹没区域及受灾情况,以巢湖流域作为研究区域,选择灾前、灾后的Sentinel-1影像作为数据源,基于SDWI指数及直方图双峰法提取水体信息,结合Landsat 8数据对山体阴影进行去除,快速提取洪水淹没范围,分析洪涝灾害前后时空变化特征,并结合Landsat 8数据获取的土地利用类型分类图,对受灾地区进行灾情评估。结果表明：2020年6—9月巢湖流域重灾区域集中于巢湖西部、东部、北部,耕地、居民地受灾程度严重;利用多时相Sentinel-1影像快速提取洪涝受灾情况,可为国家减灾备灾、应急救灾、恢复重建工作提供有力的数据支撑。     

基于多时相光谱和物候特征的陕西省神木县地物遥感分类研究

开展了时间序列Landsat TM/ETM遥感影像定量化处理与相对辐射校正,提取了陕西神木县不同地物光谱和NDVI物候特征,结合时间序列NDVI物候特征和多时相光谱信息,采用了地表覆盖的决策树分类算法,实现了陕西神木县地物的高精度遥感分类,包括水体、沙地、城镇、耕地、林地、草地及灌丛等7类地物,分类总体精度达95.77%,Kappa系数达0.93。研究结果表明,基于多时相光谱和物候特征的决策树分类算法能够有效集成多时相、多光谱信息,从而克服了单时相影像分类的缺陷,实现了地物的分类。论文研究方法和结果能够为三北防护林区域的生态环境监测与评估提供技术支持。     

基于季相变化特征的撂荒地遥感提取方法研究

在我国西南地区耕种条件差,地块比较破碎,地块类型比较复杂,中低分辨率遥感数据难以满足撂荒地提取的需要。选取贵州修文县为试验区,基于高分辨率卫星遥感数据（哨兵2号）,探索单期或多期影像在中国西南地区的撂荒地检测能力,构建撂荒地遥感监测方法,为今后我国西南地区撂荒地统计调查提供参考。结合野外调查数据,在划分不同撂荒地类型基础上,综合遥感影像的光谱特征、植被指数特征以及多时相植被指数变化特征分析,优选不同类别撂荒地遥感提取敏感特征集,利用CART决策树分类方法,提取不同类型的撂荒地。结果表明：①单个时相对不同类型的撂荒地识别能力差异显著,基于单时相影像,难以开展撂荒地高精度遥感监测提取;②不同时相的植被指数变化特征对撂荒地的识别能力较强,其中比值植被指数优于差值植被指数和归一化植被指数;③以贵州修文县为例,开展了撂荒地空间分布制图及撂荒面积统计分析,修文县撂荒地面积约为6 460 hm²,占修文县耕地面积的13%;④基于多时相高分辨遥感数据,通过季相变化特征构建的撂荒地检测方法,能够满足我国西南地区撂荒地高精度遥感监测提取,为大范围撂荒地遥感调查和制图提供技术参考。     

南方地区复杂条件下的耕地面积遥感提取方法

针对我国南方地区植被类型复杂、地形复杂和地块破碎等原因导致耕地信息提取精度较低问题,提出了一种面向对象和CART决策树结合的复杂条件下耕地面积提取方法。以广西南宁市隆安县与武鸣县地区为研究区,采用Sentinel-2A影像,结合数字高程数据（Digital Elevation Model,DEM）及归一化植被指数（Normalized Difference Vegetation Index,NDVI）等多源数据,利用面向对象分割技术识别地块信息,然后以地块为单位采用CART（Classification And Regression Tree,CART）决策树分类法,依据不同地类的形状、光谱特征,提取研究区的耕地。结果表明：面向对象的CART决策树分类方法分类总体精度和Kappa系数分别为96.1%和0.94,相比较于未加入面向对象分割的CART决策树耕地信息提取总体精度提高Kappa系数提高0.54,面向对象的分割方法有利于减少复杂背景对耕地提取的影响。基于面向对象的CART决策树分类方法相比较于传统方法对研究区耕地信息的提取有较好的精确性,能够提高耕地信息的提取精度。     

基于面向对象的多源卫星遥感影像玉米倒伏面积提取

风灾引起的玉米倒伏可能导致玉米大量减产,利用遥感技术准确监测玉米倒伏面积与空间分布信息对灾情的评估非常重要。利用Planet和Sentinel-2影像分别结合面向对象与基于像元方法提取研究区玉米倒伏,同时评估了不同影像特征（光谱特征、植被指数和纹理特征）与不同分类方法（支持向量机法SVM、随机森林法RF和最大似然法MLC）对玉米倒伏提取精度的影响。结果表明：(1)使用高空间分辨率的Planet影像进行玉米倒伏提取的精度普遍高于Sentinel-2影像;(2)从分类精度和面积精度来看,Planet影像的光谱特征+植被指数+均值特征结合面向对象RF分类,总体精度和Kappa系数分别为93.77%和0.87,面积的平均误差最低为4.76%;(3)采用Planet和Sentinel-2影像结合面向对象分类提取玉米倒伏精度高于基于像元分类。研究不仅分析了面向对象方法的优势,还评估了使用不用影像数据结合面向对象方法的适用性,可以为遥感提取作物倒伏相关研究提供一定的借鉴。     

Sentinel-2A与Landsat-8影像在油菜识别中的差异性研究

油菜是中国最重要的农作物之一,准确、及时掌握高精度的油菜面积具有重要意义。与Landsat-8数据相比,新一代光学卫星Sentinel-2A数据具有众多优点,但是Sentinel-2A数据在农作物识别方面的应用效果是否一定优于Landsat-8数据仍然是个未知的问题。因此,以油菜最佳识别期内的Sentinel-2A和Landsat-8影像各一景为数据源,选取种植结构复杂的小尺度都市农业区为研究区,基于影像的光谱特征与植被指数信息利用不同分类方法提取油菜种植面积。通过比较不同分类条件、不同方法下的两种影像的油菜识别精度,结果表明:(1)Sentinel-2A影像中不同地物的光谱特征差异与植被指数可分离性高于Landsat-8影像;(2)支持向量机(SVM)分类器下,Sentinel-2A数据的光谱特征获得的油菜制图精度与用户精度最高,分别为89.7%和91.3%,比同等条件下的Landsat-8油菜识别精度分别高7.0%和6.2%;(3)加入纹理信息后,两种数据的总体精度和Kappa系数明显提高,但油菜的制图精度与用户精度并无明显提升。以上结果表明:与Landsat-8数据相比,Sentinel-2A数据能够在种植结构复杂的小尺度区域提取更高精度的作物分布信息。研究结果可以为Sentinel-2A数据的农作物识别与应用提供理论基础。     

基于Sentinel-1,2和Landsat 8时序影像的鄱阳湖湿地连续变化监测研究

湿地具有季节性特征,高时间分辨率遥感监测能够更为客观精准地认识其时空变化规律。选择季节性变化显著、我国第一大淡水湖生态湿地——鄱阳湖湿地为典型案例,利用Sentinel-1,2和Landsat 8卫星的2017～2019年所有可以获取的不同时相影像,采用随机森林分类(Random Forest,RF)方法,对研究区的湿地进行遥感分类和信息提取,发挥海量遥感影像在湿地宏观连续监测的优势,解析鄱阳湖湿地的年际、年内时空动态变化特征。研究结果表明:Sentinel-2影像为鄱阳湖湿地动态变化监测提供良好的数据基础,随机森林分类总体分类精度高于90%,提取效果具有比较优势。对3 a分类结果进行统计分析,各湿地类型在年内均呈现出动态变化的特点,在每年2月泥滩和草洲面积到达年内最大,水体面积为年内最小;每年6、7月份水域面积达到年内最大,泥滩和草洲面积最小,季节性变化明显;月度时间序列的分类结果,能更准确地说明湿地类型的月度和季度变化。因此,结合Seninel-1,2以及Landsat 8数据,基于RF算法,能及时、有效地对鄱阳湖等季节性变化强烈的湿地进行动态监测,对开展湿地资源高效调查工作具有重要意义。     

基于多时相Landsat 8 OLI影像的农作物遥感分类研究

时序遥感数据及地物细微光谱特征对于提取作物分布有重要作用,基于此,利用多时相Landsat 8 OLI影像,结合光谱角填图和决策树分类提取大同市新荣区东部地区主要农作物分布情况,并与最大似然法提取的分布结果进行对比。研究发现:①研究区内春玉米、谷物、大豆和马铃薯种植面积依次减小并呈镶嵌式分布;②结合光谱角填图与决策树分类总体精度为85.34%,Kappa系数为0.76,与最大似然法结果相比,总体精度提高22.51%,Kappa系数增加0.31,分类结果与实际作物分布具有更好的一致性;③利用时序遥感影像进行作物分类的精度明显高于单时相遥感影像的分类精度,且从光谱角差异的角度分析时序数据可有效削弱中高分辨率影像物谱不一致现象的影响。研究结果验证了多时相遥感影像对农作物分类研究的积极作用,并发展了光谱角填图法结合决策树分类在中高分辨率遥感影像中进行农作物分类的用法,具有一定的应用前景。     

基于多时相Landsat8 OLI影像的作物种植结构提取

针对基于多时相遥感影像、多种特征量提取多种作物种植结构在我国研究较少的现状,利用多时相Landsat8OLI影像数据,根据温宿县不同作物的农事历,通过分析主要地物的光谱特征和归一化植被指数的时间变化信息,构建不同作物种植结构提取的决策树模型,实现了对温宿县多种作物种植结构信息的提取。结果表明:1水稻的最佳识别依据是5月20日影像的近红外波段和7月23日影像的NDVI值;棉花和春玉米的最佳识别依据是5月20日~9月9日影像的NDVI变化值;冬小麦—夏玉米和林果的最佳识别依据是5月20日~7月23日影像的NDVI变化值;2与单时相监督分类相比,多时相决策树法对多种作物种植结构的提取效果更理想,总体精度提高了7.90%,Kappa系数提高了0.10;3Landsat8OLI影像数据分辨率高、成本低、获取方便,是农作物遥感的良好数据源。     

利用指数熵的多时相遥感影像变化检测方法

针对像素级变化检测法中变化阈值的提取不够自动化和准确化,导致变化检测结果精度不高的问题,提出一种利用双阈值指数熵的多时相遥感影像变化检测方法。该方法首先采用差值法构造2个时相遥感影像的差异影像;其次采用双阈值指数熵的方法确定差异影像的最佳变化阈值,并将其用于分割差异影像,得到变化区域。采用客观评价法对变化检测结果进行精度评定。选择我国鄱阳湖局部区域2个时相的遥感影像进行试验,并与基于模糊C均值的变化检测方法进行对比。通过试验,所提出方法变化检测精度达94.22%,是一种有效、可行的变化检测方法。     

Study on Extraction Method of Abandoned Farmland based on the Seasonal Variation Characteristics of Remotely Sensed Images

In southwestern China, the cultivation conditions are poor, the plots are relatively fragmented, and the types of plots are complex. Therefore, the use of low and medium resolution remote sensing data is not able to satisfy the needs of abandoned farmland extraction. This paper explored the ability of single or multi-phased high resolution remotely sensed images in detecting abandoned farmland in southwest China, using Xiuwen County, Guizhou Province, China as a case study area. Remote sensing based monitoring methods for abandoned farmland were developed, providing a reference for the statistical survey of abandoned farmland in southwest China.The extraction method of abandoned farmland was proposed based on the field survey data, considering different types of abandoned farmland. Sensitive feature sets of different types of abandoned farmland were identified from a series of features including the spectral characteristics, vegetation indices and multi-temporal difference vegetation indices. The CART decision tree classification method was applied on the selected sensitive features to extract abandoned farmland. The results showed that:(1) There was a significant difference in the recognition ability of single-phase image in extracting different types of abandoned farmland, so it was difficult to use only single-phase image to extract abandoned farmland with high accuracy; (2) The vegetation index change characteristics of different time phases had strong recognition ability for abandoned farmland, and the ratio vegetation index was better than the difference vegetation index and normalized vegetation index; (3) The spatial distribution map of abandoned farmland and the statistical analysis of abandoned farmland area were carried out in Xiuwen County, Guizhou Province. The area of abandoned farmland in Xiuwen County was about 6,460 hectares, accounting for 13% of the cultivated land area.（4）Based on multi-temporal high-resolution remote sensing data, the method of detecting abandoned farmland using seasonal variation characteristics can meet the requirements of high-precision extraction of abandoned farmland in southwest China, and the results provided technical reference for remote sensing survey and mapping of abandoned farmland in large-scale.     

Evaluation of segment‐based gap‐filled Landsat ETM+ SLC‐off satellite data for land cover classification in southern Saskatchewan,Canada

This paper describes single‐date and multi‐date land‐cover classification accuracy results using segment‐based, gap‐filled Landsat 7 Enhanced Thematic Mapper data compared with Landsat 5 Thematic Mapper data captured one day apart. Maximum likelihood and Decision tree classification algorithms were evaluated. The same training and verification sets of ground data were used for each classification evaluation. For the comparison with the single‐date classification, an average decrease of 2.8% in the classification accuracy was obtained with the use of the gap‐filled Landsat data. Area estimates for the mid‐summer images differed, on average, from 0.6% to 1.9% for a four‐class and eight‐class classification, respectively. A multi‐date land‐cover classification was also completed with the addition of a late spring Landsat 5 image, resulting in an average decrease in classification accuracy of 1.8%.     

A change detection method using spatial-temporal-spectral information from Landsat images

ABSTRACT

Remote sensing data and techniques are reliable tools for monitoring land cover and land-use change. For time-series change detection algorithms, detecting the breakpoints accurately is the key element. However, the current state-of-art algorithms are vulnerable to cloud/cloud shadow or noises in the time-series imagery. The objective of this study is to develop a new method to detect land cover change using Landsat imagery by integrating temporal, spectral and spatial information to increase the accuracy of breakpoints detection. In the temporal dimension, the time-series model is decomposed into seasonality and trend. Due to different land cover types corresponding to different seasonal characteristics, breakpoints exist only in the seasonal component. In the spectral dimension, two-step judgement is applied. The first judgement detects a change when the seasonal breakpoint positions are the same in different spectral bands. The second judgement involves detecting a changed pixel when the classification result indicates different types on either side of the breakpoint. In the spatial dimension, neighbour information is utilized to control the false-positive rate. Experimental results using all available Landsat images acquired between 2001 and 2006 in Kansas City, US, illustrate the effectiveness and stability of the proposed approach. All pixels were used for assessing the classification and change detection accuracy compared with National Land Cover Database products. The overall accuracy of classification into eight categories was about 81% and the accuracy of change detection was 88%. Maps of timing of breaks and change times are also provided in this article.     

基于GEE平台的海岛地表覆盖提取及变化监测——以苏拉威西岛为例

随着海洋在国家政治、经济、资源等方面重要性的提升,对海岛开发利用、管理和保护等具有重要意义的海岛地表覆盖研究逐渐受到关注.针对传统的大范围地表覆盖提取速度慢、效率低的问题,基于Google Earth Engine(GEE)地学大数据平台,以作为生态重点保护区的印度尼西亚的苏拉威西岛为研究区,开展海岛地表覆盖提取及变化...     

Comparison of Machine Learning Methods for Land Use/Land Cover Classification in the Complicated Terrain Regions

Aiming at the characteristics of varied and complex geomorphic types,crisscross network of ravines and broken terrain in high altitude complicated terrain regions,it is very important to study and find the rapid and effective land use/land cover classification method for obtaining and timely updating of land use information.Taking the Huangshui river basin located in the transitional zone between the Loess Plateau and the Qinghai-Tibet Plateau as acasestudy area,the objective of this study is to explore a kind of effective information extraction method from comparison of four kinds machine learning methods for complicated terrain regions.based on Landsat 8 OLI satellite data,DEM and combined with various thematic features,on the basis of geographical division of the study area,artificial neural network,decision tree,support vector machine and random forest four machine learning methods for land use information extraction were used to obtain land use data,and confusion matrix was constructed to evaluate classification accuracy.The results showed that the classification accuracies of random forest and decision tree are obviously higher than those of support vector machine and artificial neural network.The random forest method has the highest classification accuracy,the overall classification accuracy is 85.65%,the Kappa coefficient is 0.84.based on the above classification,Random forest classification method was chose to further classify Landsat 8 fusion datafrom panchromatic 15 meter and multispectral 30 meter image,the overall classification accuracy is 86.49% and the Kappa coefficient is 0.85.This indicated that the random forest classification method can obtain higher classification efficiency while ensuring the classification accuracy.It is very effective for the extraction of land use information in complicated terrain regions.Data fusion can improve the classification accuracy to a certain extent.     

基于多源多时相遥感影像的山地森林分类决策树模型研究

山地是森林重要的分布区,然而山地多样的森林类型、高度异质化的景观格局、突出的地形效应以及云、雾的干扰均不同程度地影响了山地森林类型的遥感自动制图。多源多时相遥感影像提供的季相节律信息是当前提高土地覆被遥感制图精度的重要信息源之一。以岷江上游地区为研究区,以国产环境减灾卫星多光谱CCD(简称HJ CCD)影像和美国Landsat TM影像为数据源,以决策树为分类方法,根据参与分类影像的时相差异设计了5组对比实验(生长季单时相组、非生长季单时相组、生长季多时相组、非生长季多时相组、全时相组),对比论证多源多时相遥感影像对山地森林类型自动制图的贡献和作用。对比结果表明:生长季和非生长季相结合的多时相遥感影像较单时相或单一类型(生长季或非生长季)多时相遥感影像,更能显著提高山地森林类型自动制图精度,且能降低分类决策树的复杂程度,更有利于山地森林类型的自动提取。     

Land cover mapping of large areas using chain classification of neighboring Landsat satellite images

Satellite imagery is the major data source for regional to global land cover maps. However, land cover mapping of large areas with medium-resolution imagery is costly and often constrained by the lack of good training and validation data. Our goal was to overcome these limitations, and to test chain classifications, i.e., the classification of Landsat images based on the information in the overlapping areas of neighboring scenes. The basic idea was to classify one Landsat scene first where good ground truth data is available, and then to classify the neighboring Landsat scene using the land cover classification of the first scene in the overlap area as training data. We tested chain classification for a forest/non-forest classification in the Carpathian Mountains on one horizontal chain of six Landsat scenes, and two vertical chains of two Landsat scenes each. We collected extensive training data from Quickbird imagery for classifying radiometrically uncorrected data with Support Vector Machines (SVMs). The SVMs classified 8 scenes with overall accuracies between 92.1% and 98.9% (average of 96.3%). Accuracy loss when automatically classifying neighboring scenes with chain classification was 1.9% on average. Even a chain of six images resulted only in an accuracy loss of 5.1% for the last image compared to a reference classification from independent training data for the last image. Chain classification thus performed well, but we note that chain classification can only be applied when land cover classes are well represented in the overlap area of neighboring Landsat scenes. As long as this constraint is met though, chain classification is a powerful approach for large area land cover classifications, especially in areas of varying training data availability.     

Trajectory analysis of land cover change in arid environment of China

Remotely sensed data have been utilized for environmental change study over the past 30 years. Large collections of remote sensing imagery have made it possible for spatio‐temporal analyses of the environment and the impact of human activities. This research attempts to develop both conceptual framework and methodological implementation for land cover change detection based on medium and high spatial resolution imagery and temporal trajectory analysis. Multi‐temporal and multi‐scale remotely sensed data have been integrated from various sources with a monitoring time frame of 30 years, including historical and state‐of‐the‐art high‐resolution satellite imagery. Based on this, spatio‐temporal patterns of environmental change, which is largely represented by changes in land cover (e.g., vegetation and water), were analysed for the given timeframe. Multi‐scale and multi‐temporal remotely sensed data, including Landsat MSS, TM, ETM and SPOT HRV, were used to detect changes in land cover in the past 30 years in Tarim River, Xinjiang, China. The study shows that by using the auto‐classification approach an overall accuracy of 85–90% with a Kappa coefficient of 0.66–0.78 was achieved for the classification of individual images. The temporal trajectory of land‐use change was established and its spatial pattern was analysed to gain a better understanding of the human impact on the fragile ecosystem of China's arid environment.