Geomorphic interpretation of the Landsat images in Tamilnadu

Landsat imagery were used to interpret the geomorphic festures in the northern part of Tamilnadu between latitudes 11° and 12°30′ N and longitudes 77° and 80° E. Different geomorphic regions were identified with the aid of ground truths. These geomorphic regions, in general, are controlled by the underlying rock types. As the different geomorphic regions have different signatures in the Landsat images, the geomorphic interpretation, which helps to bring out the regional geology is possible with the aid of ground truths.     

Support Vector Machines for Automatic Multi-class Change Detection in Algerian Capital Using Landsat TM Imagery

Recently, Support Vector Machines (SVMs) have shown a practical relevance in various image processing applications. This paper investigates their applicability for land cover and land use change detection using multi-sensor images of remote sensing. Then, the most widely used approaches for multi-class SVMs, which are the One-Against-All and the One-Against-One with both Max-Win and DDAG decision rules are implemented to perform multi-class change detection. SVMs are evaluated in comparison with artificial neural networks using different accuracy indicators. The results obtained showed that SVMs are much more efficient than artificial neural networks and highlighted their suitability for land cover change detection.     

九龙江下游水质遥感反演及时空变化分析

在同步历史实测数据较为缺乏的条件下,基于波谱特征的比值法可以有效进行水体叶绿素a(Chla)和悬浮颗粒物(Tss)浓度的反演。利用不同时期的Landsat遥感卫星影像对九龙江下游河段的叶绿素a和悬浮颗粒物浓度进行了年际变化分析及季节变化分析发现:较高的叶绿素a浓度主要出现在北溪浦南段(北8北9)以及石龟头至北11段,叶绿素a在枯水期呈现浓度增大的趋势;高悬浮颗粒物浓度较易出现在龙津溪入口(北9郭坑公路桥)河段,高悬浮颗粒物浓度季节主要发生在丰水季节。     

Landsat 8全色影像阴影的扁平冰山出水高度提取

冰山出水高度是测量冰山厚度进而估算冰山体积的一个重要几何参数,是定量评估冰山对海洋的淡水输入量的基础。冬季冰山在海冰上成影且阴影较长,本文提出利用阴影测高模型高精度测量冰山出水高度的方法。试验选择2016年8月29日、9月7日和9月16日中心太阳高度角分别为5.43°、7.49°和11.01°的3期Landsat 8全色15 m影像,以独立扁平冰山为例,自动提取冰山在海冰上的阴影长度计算冰山出水高度,并利用不同时相同名成影点进行交叉验证评估测量精度。结果显示：阴影长度测量误差优于1个像元,在太阳高度角低于11.01°时,全色15 m影像提取的冰山出水高度均方根误差（RMSE）低于2.0 m,平均绝对误差（MAE）低于1.5 m。由此表明：在冬季低太阳高度角下,Landsat 8全色15 m影像可用于高精度测量冰山出水高度,具有大范围测量南极冰山出水高度的潜力。     

Empirical models for estimating the suspended sediment concentration in Amazonian white water rivers using Landsat 5/TM

Suspended sediment yield is a very important environmental indicator within Amazonian fluvial systems, especially for rivers dominated by inorganic particles, referred to as white water rivers. For vast portions of Amazonian rivers, suspended sediment concentration (SSC) is measured infrequently or not at all. However, remote sensing techniques have been used to estimate water quality parameters worldwide, from which data for suspended matter is the most successfully retrieved. This paper presents empirical models for SSC retrieval in Amazonian white water rivers using reflectance data derived from Landsat 5/TM. The models use multiple regression for both the entire dataset (global model, N = 504) and for five segmented datasets (regional models) defined by general geological features of drainage basins. The models use VNIR bands, band ratios, and the SWIR band 5 as input. For the global model, the adjusted R² is 0.76, while the adjusted R² values for regional models vary from 0.77 to 0.89, all significant (p-value < 0.0001). The regional models are subject to the leave-one-out cross validation technique, which presents robust results. The findings show that both the average error of estimation and the standard deviation increase as the SSC range increases. Regional models were more accurate when compared with the global model, suggesting changes in optical proprieties of water sampled at different sampling stations. Results confirm the potential for the estimation of SSC from Landsat/TM historical series data for the 1980s and 1990s, for which the in situ database is scarce. Such estimates supplement the SSC temporal series, providing a more comprehensive SSC temporal series which may show environmental dynamics yet unknown.     

基于多时相Landsat TM/ETM的阿尼玛卿山冰川变化监测

采用遥感及地理信息系统技术,利用多时相Landsat TM/ETM影像和数字高程模型(SRTMDEM),结合中国冰川目录,获得阿尼玛卿山地区不同年份的冰川范围,进行冰川变化监测。综合分析该冰川的变化情况,计算冰川进退变化速率,并对其中4个变化较大的冰川进行详细的分析统计。结果表明:从1991年至2009年,阿尼玛卿山地区既有退缩冰川也有前进冰川,其中冰川退缩面积为15.30km2,前进面积为4.46km2。总体面积持续退缩,其中退缩最大的冰川长度缩短了900m,其它冰川也存在不同程度的变化。     

基于Landsat TM数据的东平湖水体提取方法研究

文章以东平湖LANDSAT TM影像为例,根据影像各波段间的相关性以及地物在影像上的灰度差异,分别采用单波段和多波段阈值法对影像上的水体进行提取.在此基础上,结合(TM2 +TM3)-(TM4 +TM5)设定阈值和TM5单波段阈值法,提出了一种综合水体提取的方法,最后,对各方法提取的结果进行精度评价.试验结果表明:综合提取法在该研究区域具有较好的水体提取效果,极大地改善了原始单一方法的水体提取精度.     

Use of multi-temporal Landsat images for analyzing forest transition in relation to socioeconomic factors and the environment

Recently there have been reports of forest regrowth occurring in different regions across the world. There is also a growing recognition of the potential beneficial impact that secondary forests may have on the global environment: providing crucial ecosystem services such as soil conservation, stabilization of hydrological cycles, carbon sequestration, and support for forest dependent communities. Consequently, there is a growing awareness of the need to recognize that landscapes are complex shifting mosaics wherein forest clearing and reforestation take place. In this study, the rates of reforestation, deforestation, forest regrowth and degradation were measured using multi-temporal Landsat images of Danjiangkou, China. Landsat data from 1990, 1999 and 2007 were (1) classified as dense forest, open forest and non-forest areas and (2) compared between years to identify forest cutting, regeneration and degradation. The results showed that there was a net gain of 29,315 ha of forest area (including dense and open forest) from 1990 to 2007, showing a clear trend of reforestation in the study area. Forest modification (degradation and regrowth) and change categories (deforestation and reforestation) occurred simultaneously during the observation time period. Socioeconomic data from public statistics and environmental attributes allowed the assessment of the socioeconomic factors and the environmental conditions that caused these changes using non-metric multidimensional scaling (NMDS). The research showed that the socioeconomic factors due to different policies were major driving forces of forest transition, whereas environmental attributes of the underlying landscape constrained forest cover changes. These findings have led to a better understanding of forest transition at a local scale in our study region. Comprehensive knowledge of these relationships may be useful to reconstruct past forest transitions and predict future changes, and may help to enhance sustainable management practices aimed at preserving essential ecological functions.     

Forest aboveground biomass estimation in Zhejiang Province using the integration of Landsat TM and ALOS PALSAR data

In remote sensing–based forest aboveground biomass (AGB) estimation research, data saturation in Landsat and radar data is well known, but how to reduce this problem for improving AGB estimation has not been fully examined. Different vegetation types have their own species composition and stand structure, thus they have different data saturation values in Landsat or radar data. Optical and radar data also have different characteristics in representing forest stand structures, thus effective use of their features may improve AGB estimation. This research examines the effects of Landsat Thematic Mapper (TM) and ALOS PALSAR L-band data and their integrations in forest AGB estimation of Zhejiang Province, China, and the roles of textural images from both datasets. The linear regression models of AGB were conducted by using (1) Landsat TM alone, (2) ALOS PALSAR data alone, (3) their combination as extra bands, and (4) their data fusion, based on non-stratification and stratification of vegetation types, respectively. The results show that (1) overall, Landsat TM data perform better than PALSAR data, but the latter can produce more accurate estimates for bamboo and shrub, and for forests with AGB values less than 60 Mg/ha; (2) the combination of TM and PALSAR data as extra bands can greatly improve AGB estimation performance, but their fusion using the modified high-pass filter resolution-merging technique cannot; (3) textures are indeed valuable in AGB estimation, especially for forests with complex stand structures such as mixed forests and pine forests with understories of broadleaf species; (4) stratification of vegetation types can improve AGB estimation performance; and (5) the results from the linear regression models are characterized by overestimation and underestimation for the smaller and larger AGB values, respectively, and thus, selecting non-linear models or non-parametric algorithms may be needed in future research.     

基于TM影像的德兴铜矿区生态环境变化

分别选取1992年、1996年、2000年、2004年、2009年和2013年6个时期遥感影像,从生态系统结构和生态系统景观格局角度研究德兴铜矿矿产资源开发区生态环境20 a间变化。结果表明:德兴铜矿矿产资源开发活动导致森林和草地等自然景观面积持续减小,采矿场、尾矿库及排土场等人工景观面积持续增加;区域生态系统质量朝变差的方向发展,总体表现为生态系统斑块数增加,斑块密度增大,聚集度指数下降,生态系统破碎程度加重;近20 a来矿产资源开采活动不断加强,生态系统破坏面积逐年增加,矿山生态恢复工程滞后。     

欧洲大陆遥感地质解译、诠释与矿产勘查战略选区

为研究欧洲大陆基础地质和矿产分布规律,以Landsat ETM+为主要信息源,应用遥感信息技术,研究了欧洲大陆的遥感影像特征,对欧洲大陆的地层、岩体、构造进行了遥感地质解译;结合相关的地质矿产资料,编制了1∶500万欧洲大陆地质矿产遥感解译图等系列图件,发现了一些新的地质体和地质现象;在此基础上,对欧洲大陆基础地质和矿产分布规律的科学问题提出了一些新认识;根据这些新认识,进行了矿产勘查的战略选区,筛选出俄罗斯科拉半岛穹窿地区、乌克兰地盾区和乌拉尔造山带北端沃尔库塔区等8处找矿有利地区,为境外投资战略决策和欧洲大陆矿产勘查战略选区研究提供了技术支持。     

一种利用多时相TM影像分析地表植被变化的新方法：——以敦煌地区绿洲植 …

变化分析是多时相遥感影像的主要应用领域。以敦煌绿洲为例,探讨一种应用多时相ＴＭ影像进行地表植被变化分析的新方法。结果证明,用不同时相的ＮＤＶＩ影像进行彩色合成可以直观地反映地表植被的变化,而利用色彩变换得到的各分量可以对变化特点进行定量分析。其中色度反映变化类型,饱和度反映变化强度,而亮度图像则反映地表植被多年来的总体长势。除了直观和定量的特性之外,该方法还可以用于其它类型的变化分析,具有可扩展性     

一种利用多时相TM影像分析地表植被变化的新方法--以敦煌地区绿洲植被变化分析为例

变化分析是多时相遥感影像的主要应用领域。以郭煌绿洲为例 ,探讨一种应用多时相TM影像进行地表植被变化分析的新方法。结果证明 ,用不同时相的NDVI影像进行彩色合成可以直观地反映地表植被的变化 ,而利用色彩变换得到的各分量可以对变化特点进行定量分析。其中色度反映变化类型 ,饱和度反映变化强度 ,而亮度图像则反映地表植被多年来的总体长势。除了直观和定量的特性之外 ,该方法还可以用于其它类型的变化分析 ,具有可扩展性     

Evaluating techniques for mapping island vegetation from unmanned aerial vehicle (UAV) images: Pixel classification,visual interpretation and machine learning approaches

We evaluate three approaches to mapping vegetation using images collected by an unmanned aerial vehicle (UAV) to monitor rehabilitation activities in the Five Islands Nature Reserve, Wollongong (Australia). Between April 2017 and July 2018, four aerial surveys of Big Island were undertaken to map changes to island vegetation following helicopter herbicide sprays to eradicate weeds, including the creeper Coastal Morning Glory (Ipomoea cairica) and Kikuyu Grass (Cenchrus clandestinus). The spraying was followed by a large scale planting campaign to introduce native plants, such as tussocks of Spiny-headed Mat-rush (Lomandra longifolia). Three approaches to mapping vegetation were evaluated, including: (i) a pixel-based image classification algorithm applied to the composite spectral wavebands of the images collected, (ii) manual digitisation of vegetation directly from images based on visual interpretation, and (iii) the application of a machine learning algorithm, LeNet, based on a deep learning convolutional neural network (CNN) for detecting planted Lomandra tussocks. The uncertainty of each approach was assessed via comparison against an independently collected field dataset. Each of the vegetation mapping approaches had a comparable accuracy; for a selected weed management and planting area, the overall accuracies were 82 %, 91 % and 85 % respectively for the pixel based image classification, the visual interpretation / digitisation and the CNN machine learning algorithm. At the scale of the whole island, statistically significant differences in the performance of the three approaches to mapping Lomandra plants were detected via ANOVA. The manual digitisation took a longer time to perform than others. The three approaches resulted in markedly different vegetation maps characterised by different digital data formats, which offered fundamentally different types of information on vegetation character. We draw attention to the need to consider how different digital map products will be used for vegetation management (e.g. monitoring the health individual species or a broader profile of the community). Where individual plants are to be monitored over time, a feature-based approach that represents plants as vector points is appropriate. The CNN approach emerged as a promising technique in this regard as it leveraged spatial information from the UAV images within the architecture of the learning framework by enforcing a local connectivity pattern between neurons of adjacent layers to incorporate the spatial relationships between features that comprised the shape of the Lomandra tussocks detected.     

Issues in the application of Digital Surface Model data to correct the terrain illumination effects in Landsat images

The accuracy of topographic correction of Landsat data based on a Digital Surface Model (DSM) depends on the quality, scale and spatial resolution of the DSM data used and the co-registration between the DSM and the satellite image. A physics-based bidirectional reflectance distribution function (BRDF) and atmospheric correction model in conjunction with a 1-second DSM was used to conduct the analysis in this paper. The results show that for the examples used from Australia, the 1-second DSM, can provide an effective product for this task. However, it was found that some remaining artefacts in the DSM data, originally due to radar shadow, can still cause significant local errors in the correction. Where they occur, false shadows and over-corrected surface reflectance factors can be observed. More generally, accurate co-registration between satellite images and DSM data was found to be critical for effective correction. Mis-registration by one or two pixels could lead to large errors of retrieved surface reflectance factors in gully and ridge areas. Using low-resolution DSM data in conjunction with high-resolution satellite images will also fail to correct significant terrain components where they occur at the finer scales of the satellite images. DSM resolution appropriate to the resolution of satellite image and the roughness of the terrain is needed for effective results, and the rougher the terrain, the more critical will be the accurate registration.     

Burned area mapping and post-fire impact assessment in the Kassandra peninsula (Greece) using Landsat TM and Quickbird data

The purpose of this study was to assess the environmental impacts of forest fires on part of the Mediterranean basin. The study area is on the Kassandra peninsula, prefecture of Halkidiki, Greece. A maximum likelihood supervised classification was applied to a post-fire Landsat TM image for mapping the exact burned area. Land-cover types that had been affected by fire were identified with the aid of a CORINE land-cover type layer. Results showed an overall classification accuracy of 95%, and 83% of the total burned area was ‘forest areas’. A normalized difference vegetation index threshold technique was applied to a post-fire Quickbird image which had been recorded six years after the fire event to assess the vegetation recovery and to identify the vegetation species that were dominant in burned areas. Four classes were identified: ‘bare soil’, ‘sparse shrubs’, ‘dense shrubs’ and ‘tree and shrub communities’. Results showed that ‘shrublands’ is the main vegetation type which has prevailed (65%) and that vegetation recovery is homogeneous in burned areas.     

高分辨率遥感图像港口检测的复合线索视觉注意模型

为提高高分辨率光学遥感图像港口自动检测的准确性,常需综合多类线索并进行复杂的特征提取、融合与分类推理,从而带来较高的计算复杂度。为此,仿生人类视觉注意机制,提出了一种复合线索视觉注意模型,综合利用高分辨率光学遥感图像港口多尺度底层特征和高层知识线索,实现了港口检测特征自然融合与综合分类推理。该方法在提高检测效果的同时较好地控制了计算量的增长,避免了复杂特征的大范围区域提取,采用多步快速算法降低了整个算法的计算复杂度,实现了计算资源受限条件下港口的快速定位与检测。同时,由于能将有限计算资源快速聚焦于最可能含有港口目标的区域,大大提高了目标检测方法响应的实时性。来自不同卫星的高分辨率光学遥感图像实验结果,验证了提出方法的有效性。     

Use of Field Spectroscopy for Exploring the Impact of Atmospheric Effects on Landsat 5 TM/7 ETM+ Satellite Images Intended for Hydrological Purposes in Cyprus

The spectroradiometric retrieved reflectance of a local crop, namely, beans (Phaseolus vulgaris), is directly compared to the reflectance of Landsat 5TM and 7ETM+ atmospherically corrected and uncorrected satellite images. Also, vegetation indices from the same satellite images—atmospherically corrected and uncorrected—are compared with the corresponding vegetation indices produced from field measurements using a spectroradiometer. Vegetation Indices are vital in the estimation of crop evapotransiration under standard conditions (ET_c) because they are used in stochastic or empirical models for describing crop canopy parameters such as the Leaf Area Index (LAI) or crop height. ET_c is finally determined using the FAO Penman-Monteith method adapted to satellite data, and is used to examine the impact of atmospheric effects. Regarding the reflectance comparison, the main problem was observed in Band 4 of Landsat 5TM and 7ETM+, where the difference, for uncorrected images, was more than 20% and statistically significant. Results regarding ET_c show that omission or ineffective atmospheric corrections in Landsat 5TM,/7ETM+ satellite images always results in a water deficit when estimating crop water demand. Diminished estimated crop water requirements can result in a reduction in output or, if critical, crop failure. The paper seeks to illustrate the importance of removing atmospheric effects from satellite images designated for hydrological purposes.     

Evaluation of Landsat TM data for forest cover type and landuse classification in subtropical forests of Kumaon Himalaya (U.P.)

LANDSAT-TM has been evaluated for forest cover type and landuse classification in subtropical forests of Kumaon Himalaya (U.P.) Comparative evaluation of false colour composite generated by using various band combinations has been made. Digital image processing of Landsat-TM data on VIPS-32 RRSSC computer system has been carried out to stratify vegetation types. Conventional band combination in false colour composite is Bands 2, 3 and 4 in Red/Green/Blue sequence of Landsat TM for landuse classification. The present study however suggests that false colour combination using Landsat TM bands viz., 4, 5 and 3 in Red/Green/Blue sequence is the most suitable for visual interpretation of various forest cover types and landuse classes. It is felt that to extract full information from increased spatial and spectral resolution of Landsat TM, it is necessary to process the data digitally to classify land cover features like vegetation. Supervised classification using maximum likelihood algorithm has been attemped to stratify the forest vegetation. Only four bands are sufficient enough to classify vegetaton types. These bands are 2,3,4 and 5. The classification results were smoothed digitaly to increase the readiability of the map. Finally, the classification carred out using digital technique were evaluated using systematic sampling design. It is observed that forest cover type mapping can be achieved upto 80% overall mapping accuracy. Monospecies stand Chirpine can be mapped in two density classes viz., dense pine (<40%) with more than 90% accuracy. Poor accuracy (66%) was observed while mapping pine medium dense areas. The digital smoothening reduced the overall mapping accuracy. Conclusively, Landsat-TM can be used as operatonal sensor for forest cover type mapping even in complex landuse-terrain of Kumaon Himalaya (U.P.)