Mapping land use/cover distribution on a mountainous tropical island using remote sensing and GIS

The land use/cover distribution on Langkawi Island, Malaysia was mapped using remote sensing and a Geographic Information System (GIS). A Landsat Thematic Mapper (TM) satellite image taken in March 1995 was processed, geocorrected and analysed using IDRISI, raster-based GIS software. An unsupervised classification was performed based on spectral data from a composite image of the bands TM3, TM4 and TM5. Using this output, field data together with available secondary data consisting of topography, land use and soil maps were used to perform a maximum likelihood supervised classification. The overall accuracy of the output image was 90% and individual class accuracy ranged from 74% for rubber to 100% for paddy fields. The classified areas on the image were mainly confined to the mountainous and hilly regions on the island. A shaded relief map, simulating sunshine conditions, showed that the unclassified areas are located in the shadowed slopes, i.e. the slopes facing west. Consequently, the imagery was subdivided on the basis of slope aspect and a stratified classification was performed. As a result of this procedure, the overall accuracy increased to 92% and the individual class accuracy for the inland forest class increased by 9% to 90% . Using IDRISI, individual class areas as well as percentages were calculated. The kappa coefficient for the classified image was 0.90. Qualitative analysis indicates that topography is the main control on the spatial distribution of land use/cover types on the island. As Langkawi Island has been developing rapidly over the last decade, successful planning will require reliable information about land use/cover distribution and change. This study illustrates that remote sensing and GIS techniques are capable of providing such information.     

Detecting war‐induced abandoned agricultural land in northeast Bosnia using multispectral,multitemporal Landsat TM imagery

The use of satellite technology by military planners has a relatively long history as a tool of warfare, but little research has used satellite technology to study the effects of war. This research addresses this gap by applying satellite remote sensing imagery to study the effects of war on land‐use/land‐cover change in northeast Bosnia. Although the most severe war impacts are visible at local scales (e.g. destroyed buildings), this study focuses on impacts to agricultural land. Four change detection methods were evaluated for their effectiveness in detecting abandoned agricultural land using Landsat Thematic Mapper (TM) data from before, during and after the 1992–95 war. Ground reference data were collected in May 2006 at survey sites selected using a stratified random sampling approach based on the derived map of abandoned agricultural land. Fine‐resolution Quickbird imagery was also used to verify the accuracy of the classification. Results from these analyses show that a supervised classification of the Landsat TM data identified abandoned agricultural land with an overall accuracy of 82.5%. The careful use of freely available Quickbird imagery, both as training data for the supervised classifier and as supplementary ground reference data, suggests that these methods are applicable to other civil wars too dangerous for researchers' fieldwork.     

Image segmentation for humid tropical forest classification in Landsat TM data

Humid tropical forest types have low spectral separability in Landsat TM data due to highly textured reflectance patterns at the 30m spatial resolution. Two methods of reducing local spectral variation, low-pass spatial filtering and image segmentation, were examined for supervised classification of 10 forest types in TM data of Peruvian Amazonia. The number of forest classes identified at over 90% accuracy increased from one in raw imagery to three in filtered imagery, and six in segmented imagery. The ability to derive less generalised tropical forest classes may allow greater use of classified imagery in ecology and conservation planning.     

Improving tropical forest mapping using multi-date Landsat TM data and pre-classification image smoothing

The present study explores the possibility of using Landsat imagery for mapping tropical forest types with relevance to forest ecosystem services. The central part in the classification process is the use of multi-date image data and pre-classification image smoothing. The study argues that multi-date imagery contains information on phenological and canopy structural properties, and shows how the use of multi-date imagery has a significant impact on classification accuracy. Furthermore, the study shows the value of applying small kernel smoothing filters to reduce in-class spectral variability and enhance between-class spectral separability. Making use of these approaches and a maximum likelihood algorithm, six tropical forest types were classified with an overall accuracy of 90.94%, and with individual forest classes mapped with accuracies above 75.19% (user's accuracy) and above 74.17% (producer's accuracy).     

A hybrid pansharpening approach and multiscale object-based image analysis for mapping diseased pine and oak trees

We developed a multiscale object-based classification method for detecting diseased trees (Japanese Oak Wilt and Japanese Pine Wilt) in high-resolution multispectral satellite imagery. The proposed method involved (1) a hybrid intensity–hue–saturation smoothing filter-based intensity modulation (IHS-SFIM) pansharpening approach to obtain more spatially and spectrally accurate image segments; (2) synthetically oversampling the training data of the ‘Diseased tree’ class using the Synthetic Minority Over-sampling Technique (SMOTE); and (3) using a multiscale object-based image classification approach. Using the proposed method, we were able to map diseased trees in the study area with a user's accuracy of 96.6% and a producer's accuracy of 92.5%. For comparison, the diseased trees were mapped at a user's accuracy of 84.0% and a producer's accuracy of 70.1% when IHS pansharpening was used alone and a single-scale classification approach was implemented without oversampling the ‘Diseased tree’ class.     

Automated tree recognition in old growth conifer stands with high resolution digital imagery

Automated individual tree isolation and species determination with high resolution multispectral imagery is becoming a viable forest survey tool. Application to old growth conifer forests offer unique technical issues including high variability in tree size and dominance, strong tree shading and obscuration, and varying ages and states of health. The capabilities of individual tree analysis are examined with two acquisitions of 70-cm resolution CASI imagery over a hemlock, amabilis fir, and cedar dominated old growth site on the west coast of Canada. Trees were delineated using the valley following approach of the Individual Tree Crown (ITC) software suite, classified according to species (hemlock, amabilis fir, and cedar) using object-based spectral classification and tested on a tree-for-tree basis against data derived from ground plots.Tree-for-tree isolation and species classification accuracy assessment, although often sobering, is important for portraying the overall effectiveness of species composition mapping using single tree approaches. This accuracy considers not only how well each tree is classified, but how well each automated isolation represents a true tree and its species. Omissions and commissions need to be included in overall species accuracy assessment. A structure of rules for defining isolation accuracy is developed and used. An example is given of a new approach to accuracy analysis incorporating both isolation and classification results (automated tree recognition) and the issues this presents.The automated tree isolation performed well on those trees that could be visually identified on the imagery using ground measured stem maps (approximately 50-60% of trees had a good match between manual and automated delineations). There were few omissions. Commission errors, i.e., automated isolations not associated with a delineated ground reference tree, were a problem (25%) usually associated with spurious higher intensity areas within shaded regions, which get confused in the process of trying to isolate shaded trees. Difficulty in classifying species was caused by: variability of the spectral signatures of the old growth trees within the same species, tree health, and trees partly or fully shaded by other trees. To accommodate this variability, several signatures were used to represent each species including shaded trees. Species could not be determined for the shaded cases or for the unhealthy trees and therefore two combined classes, a shaded class and unhealthy class with all species included, were used for further analysis. Species classification accuracy of the trees for which there was a good automated isolation match was 72%, 60%, and 40% for the non-shaded healthy hemlock, balsam, and cedar trees for the 1996 data. Equivalent accuracy for the 1998 imagery was 59% for hemlock, 80% for balsam, with only a few cedar trees being well isolated. If all other matches were considered an error in classification, species classification was poor (approximately 45% for balsam and hemlock, 25% for cedar). However, species classification accuracies incorporating the good isolation matches and trees for which there was a match of an isolations and reference tree but the match was not considered good were moderate (60%, 57%, and 38% for hemlock, balsam, and cedar from the 1996 data; 62%, 61%, and 89%, respectively, for the 1998 imagery).Automated tree isolation and species classification of old growth forests is difficult, but nevertheless in this example useful results were obtained.     

A performance evaluation of a burned area object-based classification model when applied to topographically and non-topographically corrected TM imagery

Operational use of remote sensing as a tool for post-fire Mediterranean forest management has been limited by problems of classification accuracy arising from confusion between burned and non-burned land, especially within shaded areas. Object-oriented image analysis has been developed to overcome the limitations and weaknesses of traditional image processing methods for feature extraction from high spatial resolution images. The aim of this work was to evaluate the performance of an object-based classification model developed for burned area mapping, when applied to topographically and non-topographically corrected Landsat Thematic Mapper (TM) imagery for a site on the Greek island of Thasos. The image was atmospherically and geometrically corrected before object-based classification. The results were compared with the forest perimeter map generated by the Forest Service. The accuracy assessment using an error matrix indicated that the removal of topographic effects from the image before applying the object-based classification model resulted in only slightly more accurate mapping of the burned area (1.16% increase in accuracy). It was concluded that topographic correction is not essential prior to object-based classification of a burned Mediterranean landscape using TM data.     

A hybrid data fusion approach to the hierarchical mapping of land-cover types in the urban core

It is difficult to map land covers in the urban core due to the close proximity of high-rise buildings. This difficulty is overcome with a proposed hybrid, the hierarchical method via fusing PAN-sharpened WorldView-2 imagery with light detection and ranging (lidar) data for central Auckland, New Zealand, in two stages. After all features were categorized into ‘ground’ and ‘above-ground’ using lidar data, ground features were classified from the satellite data using the object-oriented method. Above-ground covers were grouped into four types from lidar-derived digital surface model (nDSM) based on rules. Ground and above-ground features were classified at an accuracy of 94.1% (kappa coefficient or κ = 0.913) and 93.7% (κ = 0.873), respectively. After the two results were merged, the nine covers achieved an overall accuracy of 93.7% (κ = 0.902). This accuracy is highly comparable to those reported in the literature, but was achieved at much less computational expense and complexity owing to the hybrid workflow that optimizes the efficiency of the respective classifiers. This hybrid method of classification is robust and applicable to other scenes without modification as the required parameters are derived automatically from the data to be classified. It is also flexible in incorporating user-defined rules targeting hard-to-discriminate covers. Mapping accuracy from the fused complementary data sets was adversely affected by shadows in the satellite image and the differential acquisition time of imagery and lidar data.     

基于ENVISat ASAR影像的湿地植被冠层下淹水范围提取以扎龙保护区为例

以扎龙自然保护区湿地为例,结合ENVISat ASAR多极化(HH/HV)雷达影像与传统的光学影像Landsat TM (band1~5,7),分析雷达影像后向散射系数与Landsat TM影像不同波段反射率在淹水植被、非淹水植被、明水面和裸土不同地表覆被类型的差异。选择训练样本,采用分类回归树(Classification and Regression Tree,CART)模型,分别对两种影像进行分类,可视化表达湿地植被淹水范围空间分布情况。基于实测的植被冠层下淹水范围与非淹水范围样本点对两种数据源的分类结果进行精度验证。结果表明:HH/HV极化影像中,植被覆盖下水体的后向散射系数与其他地表覆被类型有明显区别,分类结果总精度为79.49%,Kappa系数为0.70,湿地植被淹水范围提取精度较高。而TM影像分类结果中,由于部分地区植被覆盖水体,淹水植被分类误差较高。将雷达影像引入沼泽湿地研究,提高了植被淹水范围提取效果,为有效分析湿地生态水文过程提供基础,对湿地水资源合理利用及生物多样性保护具有重要意义。     

Efficiencies of remotely sensed data and sensitivity of grid spacing in sampling and mapping a soil erosion relevant cover factor by cokriging

This study investigates applications and efficiencies of remotely sensed data and the sensitivity of grid spacing for the sampling and mapping of a ground and vegetation cover factor in a monitoring system of soil erosion dynamics by cokriging with Landsat Thematic Mapper (TM) imagery based on regionalized variable theory. The results show that using image data can greatly reduce the number of ground sample plots and sampling cost required for collection of data. Under the same precision requirement, the efficiency gain is significant as the ratio of ground to image data used varies from 1: 1 to 1: 16. Moreover, we proposed and discussed several modifications to the cokriging procedure with image data for sampling and mapping. First, directly using neighbouring pixels for image data in sampling design and mapping is more efficient at increasing the accuracy of maps than using sampled pixels. Although information among neighbouring pixels might be considered redundant, spatial cross-correlation of spectral variables with the cover factor can provide the basis for an increase in accuracy. Secondly, this procedure can be applied to investigate the appropriate spatial resolution of imagery, which, for sampling and mapping the cover factor, should be 90 m?×?90 m – nearly consistent with the line transect size of 100 m used for the ground field survey. In addition, we recommend using the average of cokriging variance to determine the global grid spacing of samples, instead of the maximum cokriging variance.     

Detection of forest harvest type using multiple dates of Landsat TM imagery

A simple and relatively accurate technique for classifying time-series Landsat Thematic Mapper (TM) imagery to detect levels of forest harvest is the topic of this research. The accuracy of multidate classification of the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI) were compared and the effect of the number of years (1–3, 3–4, 5–6 years) between image acquisition on forest change accuracy was evaluated. When Landsat image acquisitions were only 1–3 years apart, forest clearcuts were detected with producer's accuracy ranging from 79% to 96% using the RGB-NDMI classification method. Partial harvests were detected with lower producer's accuracy (55–80%) accuracy. The accuracy of both clearcut and partial harvests decreased as time between image acquisition increased. In all classification trials, the RGB-NDMI method produced significantly higher accuracies, compared to the RGB-NDVI. These results are interesting because the less common NDMI (using the reflected middle infrared band) outperformed the more popular NDVI. In northern Maine, industrial forest landowners have shifted from clearcutting to partial harvest systems in recent years. The RGB-NDMI change detection classification applied to Landsat TM imagery collected every 2–3 years appears to be a promising technique for monitoring forest harvesting and other disturbances that do not remove the entire overstory canopy.     

Does spatial resolution matter? A multi-scale comparison of object-based and pixel-based methods for detecting change associated with gas well drilling operations

An implicit assumption of the geographic object-based image analysis (GEOBIA) literature is that GEOBIA is more accurate than pixel-based methods for high spatial resolution image classification, but that the benefits of using GEOBIA are likely to be lower when moderate resolution data are employed. This study investigates this assumption within the context of a case study of mapping forest clearings associated with drilling for natural gas. The forest clearings varied from 0.2 to 9.2 ha, with an average size of 0.9 ha. National Aerial Imagery Program data from 2004 to 2010, with 1 m pixel size, were resampled through pixel aggregation to generate imagery with 2, 5, 15, and 30 m pixel sizes. The imagery for each date and at each of the five spatial resolutions was classified into Forest and Non-forest classes, using both maximum likelihood and GEOBIA. Change maps were generated through overlay of the classified images. Accuracy evaluation was carried out using a random sampling approach. The 1 m GEOBIA classification was found to be significantly more accurate than the GEOBIA and per-pixel classifications with either 15 or 30 m resolution. However, at any one particular pixel size (e.g. 1 m), the pixel-based classification was not statistically different from the GEOBIA classification. In addition, for the specific class of forest clearings, accuracy varied with the spatial resolution of the imagery. As the pixel size coarsened from 1 to 30 m, accuracy for the per-pixel method increased from 59% to 80%, but decreased from 71% to 58% for the GEOBIA classification. In summary, for studying the impact of forest clearing associated with gas extraction, GEOBIA is more accurate than pixel-based methods, but only at the very finest resolution of 1 m. For coarser spatial resolutions, per-pixel methods are not statistically different from GEOBIA.     

基于遥感影像时间序列的冬小麦种植监测最佳时相选择研究

遥感影像植被分类的最佳时相对作物种植面积遥感监测非常重要。根据2005~2006年北京冬小麦不同物候期的Landsat TM影像和2006年Spot\|2影像,计算了各时期影像中主要植被类型的光谱可分性距离,分析了北京郊区主要植被物候差异和光谱可分性;对各生育期的遥感影像及其主要组合进行了监督分类,采用总体精度和分类效率指标两个参数,结合地面GPS调查数据,对分类结果进行了精度评价。结果表明：北京地区小麦监测最佳时相是4月上旬,影像分类的总体精度为92.9％,明显优于其它单时相影像的分类结果;发现北京郊区冬小麦光谱分类的最佳时相组合为4月上旬(起身期)和5月下旬(灌浆期),分类总体精度为94％。     

Mapping National Park landscape from ground,air and space

Aerial photograph interpretation and field mapping were used in a series of experiments to evaluate the use of Landsat and Système Probatoire pour l'Observation de la Terre (SPOT) satellite imagery for landscape mapping. The 'Monitoring Landscape Change in the National Parks' (MLCNP) Project mapped landscape in each of the National Parks of England and Wales in terms of 38 land cover classes with significant visual impact. The main source of data was aerial photography but satellite imagery for selected areas was also analysed. It was found that single-date multi-spectral imagery could be classified to an acceptable level of agreement with ground data only if the 38 sub-classes of the interpretation scheme were grouped into the seven main class headings. Visual interpretation of SPOT panchromatic imagery at the 38 sub-class level proved comparable with aerial photograph interpretation for an area of the North York Moors. This paper describes the approaches taken in data analysis and presents the main results obtained. The use of confusion matrices allowed measurements of agreement to be made between the three sources of data. A significant problem in mapping landscape was to arrive at unambiguous class definitions when many of the categories had no clear boundaries on the ground. Confusion matrix analysis, together with the use of a hierarchical classification scheme, allowed links to be made between data collected from ground, air and space. Some classification problems were attributable to all sources of data due to inherent difficulties with the classification system.     

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.     

基于面向对象方法的沙化土地遥感信息提取技术研究

针对基于像元光谱特征提取沙化土地信息分类精度偏低的问题,以Landsat\|5 TM为数据源,基于面向对象的方法对沙化土地遥感信息提取技术进行研究。首先采用多尺度分割法对影像进行分割以获得同质区域,然后结合野外调查数据制成不同地物类型的多种特征图,从而确定提取目标地物的特征并建立沙化和非沙化地物提取决策树,最后对影像进行模糊分类,并对分类结果进行精度评价。结果表明,基于面向对象提取沙化土地信息的总精度达84.89%,Kappa系数为0.8077。研究结果为后续深入研究奠定了基础。     

Probabilistic image classification using geological map units applied to land-cover change detection

This paper describes how probabilistic methods provide a means to integrate analysis of remotely sensed imagery and geo-information processing. In a case study from southern Spain, geological map units were used to improve land-cover classification from Landsat TM imagery. Overall classification accurracy improved from 76% to 90% (1984) and from 64% to 69% (1995) when using stratification according to geology combined with iterative estimation of prior probabilities. Differences between the two years were mainly due to extremely dry conditions during the 1995 growing season. Per-pixel probabilities of class successions and entropy values calculated from the classification's posterior probability vectors served to quantify uncertainty in a post-classification comparison. It is concluded that iterative estimation of prior probabilities provides a practical approach to improve classification accuracy. Posterior probabilities of class membership provide useful information about the magnitude and spatial distribution of classification uncertainty.     

Modelling and mapping potential hooded warbler (Wilsonia citrina) habitat using remotely sensed imagery

Modelling and mapping of hooded warbler (Wilsonia citrina) nesting habitat in forests of southern Ontario were conducted using Ikonos and Landsat data. The study began with an analysis of skyward hemispherical photography to determine canopy characteristics associated with nest sites. It showed that nest sites had significantly less overhead canopy cover and larger maximum gap size than in non-nest areas. These findings led to the hypothesis that brightness variability in high to moderate resolution remotely sensed imagery may be greater at nest sites than in non-nest areas due to larger shadows and greater shadow variability related to these gap characteristics. This was confirmed when, in addition to some spectral band brightness variables, several image texture and spectrally unmixed fraction (shadow, bare soil) variables were found to be significantly different for nest and non-nest sites in Ikonos and Landsat imagery. These significantly different variables were used in maximum likelihood classification (MLC) and logistic regression (LR) to produce maps of potential nesting habitat. Mapping was conducted with Ikonos and Landsat in a local area where most known nest sites occur, and regionally using Landsat data for almost all of the hooded warbler range in southern Ontario. For the local area mapping using Ikonos data, a posteriori probabilities for both the MLC and LR methods showed that about 62% of the nest sites set aside for validation had been classified with high probability (p > 0.70) in the nest class. MLC mapping accuracy was 70% for the validation nest sites and 87% of validation nest sites were within 10 m of classified nesting habitat, a distance approximately equivalent to expected positional error in the data. LR accuracy was slightly lower. Nest site MLC mapping accuracy in the local area using Landsat data was 87% but the map was much coarser due to the larger pixel size. Regional mapping with Landsat imagery produced lower classification accuracy due to high errors of commission for the habitat class. This resulted from a poor spatial distribution and low number of observations of nest sites throughout the region compared to the local area, while the non-nest site data distribution was too narrow, having been defined and assessed (using standard accepted methods) as areas with no ground shrubs. If either of these problems can be ameliorated, regional mapping accuracy may improve.     

TM和SAR遥感图像的不同层次融合分类比较

多传感器遥感图像为分类技术提供了更多的地物特征信息,有助于提高分类精度,增强计算机自动解译的能力,减少遥感图像的后处理时间。给出了ＴＭ 和ＳＡＲ遥感图像根据信息处理所在的不同层次融合分类特征的选取及分类方法,并从融合分类的精度和计算时间消耗上对哈尔滨市附近地区５１２×５１２ 大小的ＴＭ和ＪＥＲＳ１ 的ＳＡＲ图像的不同层次融合的分类进行比较,指出特征层融合是ＴＭ和ＳＡＲ遥感图像融合分类的最理想层次     

TM digital processing of a tropical forest region in southeastern Mexico

A humid forest in the neotropical area of Los Tuxtlas, in southeastern Mexico has been used as a test area (900km²) for classification of landscape and vegetation by means of Landsat Thematic Mapper (TM) data, aerial photography and 103 ground samples. The area presents altitudinal variations from sea level to 1640m, providing a wide variety of vegetation types. A hybrid (supervised/unsupervised) classification approach was used, defining spectral signatures for 14 clustering areas with data from the reflective bands of the TM. The selected clustering areas ranged from vegetation of the highlands and the rain forest to grassland, barren soil, crops and secondary vegetation. The digital classification compared favourably with results from aerial photography and with those from a multivariate analysis of the 103 ground data. The statistical evaluation (error matrix) of the classified image indicated an overall 84·4 per cent accuracy with a kappa coefficient of agreement of 0·83. A geographical information system (GIS) was used to compile a land unit and a vegetation map. The TM data allowed for delineation of boundaries in the land unit map, and for a finer differentiation of vegetation types than those identified during field work. Digital value patterns of several information classes are shown and discussed as an indirect guide of the spectral behaviour of vegetation of highlands, rain forest, secondary vegetation and crops. The method is considered applicable to the inventory of other forested areas, especially those with significant variations in vegetation.