Remote Sensing Classification of Marsh Wetland with Different Resolution Images

Transcription

1 March, 2016 Journal of Resources and Ecology Vol. 7 No.2 J. Resour. Ecol (2) DOI: /j.issn x Article Remote Sensing Classification of Marsh Wetland with Different Resolution Images LI Na 1,2, XIE Gaodi 1, ZHOU Demin 3, ZHANG Changshun 1, JIAO Cuicui 1,2 1. Institute of Geographic Sciences and Natural Resources Research, C A S, Beijing , China; 2. Graduate University of Chinese Academy of Sciences, Beijing , China; 3. Base of State Key Laboratory of Urban Environmental Process and Digital Modeling, Capital Normal University, Beijing , China Abstract: Successful biological monitoring depends on judicious classification. An attempt has been made to provide an overview of important characteristics of marsh wetland. Classification was used to describe ecosystems and land cover patterns. Different spatial resolution images show different landscape characteristics. Several classification images were used to map and monitor wetland ecosystems of Honghe National Nature Reserve (HNNR) at a plant community scale. HNNR is a typical inland wetland and fresh water ecosystem in the North Temperate Zone. SPOT-5 10 m 10 m, 20 m 20 m, and 30 m 30 m images and Landsat -5 Thematic Mapper (TM) images were used to classify based on maximum likelihood classification (MLC) algorithms. In order to validate the precision of the classifications, this study used aerial photography classification maps as training samples because of their high accuracy. The accuracy of the derived classes was assessed with the discrete multivariate technique called KAPPA accuracy. The results indicate: (1) training samples are important to classification results. (2) Image classification accuracy is always affected by areal fraction and aggregation degree as well as by diversities and patch shape. (3) The core zone area is protected better than buffer zone and experimental zone wetland. The experimental zone degrades fast because of irrational development by humans. Key words: Remote sensing classification, Marsh wetland, HNNR, aerial photography image, SPOT-5, TM 1 Introduction Scale is a fundamental concept in geography, and it creates fundamental problems for geographers. Within geography, spatial scales of inquiry range conservatively from 10 2 to 10 6 m (Atkinson and Tate 2004). The fundamental reason for the continuing interest in scale in remote sensing is that spatial resolution is the primary scale of measurement (Atkinson and Aplin 2004). Hence, scale refers to spatial resolution (pixel size) the size of the smallest distinguishable part of a spatial dataset (Bian 1997). The advance of remote sensing technology in the 20th century has provided a powerful means to conduct regional and global measurements and, today, remote sensing technology has entered an era of quantitative analysis (Wu and Li 2009). Thus, scale effects and scaling have already become one of the most important research in remote sensing (Quattrochi and Goodchild 1997). And the availability of new tools such as GIS and enhanced computing power has facilitated multivariable and multiscale analysis and integration of spatial data (Atkinson and Tate 2004). However, data aggregation can significantly affect outputs and thus decision making. Remote sensing images and ground data need to be scaled up to a consistent scale to analyze data and map out. For these reasons, it is necessary to infer spatial data from one scale to another. Received: Accepted: Foundation: This study was jointly supported by the National Science and Technology Support Program (No. 2013BAC03B05), Ecological environment evaluation of disaster area(no. O7M73120AM) *Corresponding author: Xie Gaodi,Tel, , xiegd@igsnrr.ac.cn Citation: LI Na, XIE Gaodi, ZHANG Changshun, et al Remote Sensing Classification of Marsh Wetland with Different Resolution Images. Journal of Resources and Ecology. 7(2):

2 108 Journal of Resources and Ecology Vol. 7 No. 2, 2016 Many methods have been combined in optimal scale modeling, including those techniques such as local variance, variogram and transformed divergence that are commonly incorporated in software GIS and image analysis packages. Spatial data aggregation is widely used in many fields such as environmental science, ecology and hydrology that have moved from local scaled modeling to larger regions (Gupta et al. 2009). However, existing modeling is limited mainly to just changing the size of pixels. The effect of data transformations made by varying the data resolution can only evaluated by comparing the resultant statistical and spatial characteristics. This method is unable to apply coarser images that are transformed to classifications that have a greater number of finer classes. Wetlands are areas of marsh, fen, peatland or water, whether natural or artificial, permanent or temporary, with water that is static or flowing, fresh, brackish or salt, including areas of marine water the depth of which at low tide does not exceed six meters (Nature and Bureau 1984). Wetlands are characterized by rich biological resources and as areas that are capable of high food production (Zhou et al. 2009). However, the degradation of wetlands leads to natural land ecosystems becoming much less stable, so it is very important to inventory and monitor wetlands. The development of remote sensing and GIS provides a good platform to monitor wetland resources. Satellite remote sensing can also provide information on surrounding land uses and how these change over time (Ozesmi and Bauer 2002). And wetland classification displays the distribution of different types of wetland vegetation that contribute to ecological evolution. Therefore, high spatial resolution imagery is generally preferred for detailed mapping of wetlands, especially if many different vegetation types must be extracted from the images. However, classification systems, to a certain extent, are limited by the spatial resolution of remote sensing imagery. In this respect, our work is an attempt to apply high classification vector-based mapping as the training samples to classify coarse images, and to study the potential use of the coarse images for determining the distribution of invasive plants at the community level. It should be noted that it is not very easy to map TM classifications at a community level in a estuarine wetland because of the composite mixed pixels of the images and the complicated wetlands plant types with cross-growth characteristics. So the critical step for classification to TM image is to select good training samples. Therefore, we selected high spatial resolution imagery and its high accuracy assessment classification mapping as the training samples. Hence, this study transformed from finer image to coarser ones in order to achieve a higher number of classes on coarser images. 2 Materials and methods 2.1 The study area Honghe National Nature Reserve (HNNR; E and N) is located in the Sanjiang plain, a low alluvial plain of the Heilongjiang River, Songhua River and Wusuli River, and it has a total area of 251 km 2 (Fig.1). HNNR is in the upper and middle reaches of Nongjiang River, and the sediment area of the Nongjiang River and Woyalan River. The soil has poor water permeability thus resulting in the formation of a large area of wetlands (Liu and Ma, 2002). HNNR is on the International Important Wetland list for being a typical inland wetland and fresh water ecosystem in the North Temperate Zone and was also entered on the Ramsar List of Wetlands in 2001 (Ramsar site no. 1149) (Zhou et al. 2009). HNNR has more than one thousand species of plants, as well as rare species of flora and three of avifauna. Some examples of the principal types of vegetation are Carex pseudocuraica, Carex lasiocarpa, Carex spiculosa, Calamagrostis angustifolia, Ass.B.platyphylla, Ass.P.davidiana. 2.2 Scale transformation of wetlands classification using aerial photography, SPOT-5 and Landsat Thematic Mapper (TM) image To infer vegetation classification of wetlands from a coarser to a finer classification, we proposed use of the up-scaling Fig.1 Location of HNNR

3 LI Na, et al.: Remote Sensing Classification of Marsh Wetland with Different Resolution Images 109 vector-based method. Despite ongoing research efforts directed at vegetation classification of wetlands and up-scaling methods, there remains a need to complement up-scaling of wetlands classification at a community scale with coarse resolution images. The professional remote sensing ENVI 4.7 software was mainly used in our work for image data preprocessing, classification and accuracy assessment. ARCGIS9.3 (Geographical Information System software) was used for data analysis and mapping out. There were three steps to the scale transformation process: 1) data acquisition and preprocessing which included image geo-referencing, image correction, image clip and bands composition; 2) use the aerial photography classification vectors as training samples; 3) classification of TM and SPOT-5 images by aerial classification features and the making of accuracy assessments. 2.3 Data sources Three different sets of remotely sensed data, including truecolor aerial photography (the classification map), Landsat TM, and SPOT-5, were used to scale transform vegetation classification in the HNNR marsh study area. Aerial photographs with spatial resolution of 0.13 m 0.13 m were acquired on 1 August, The SPOT-5 is a 1A Level image obtained on 15 August, 2005, and comprised of four bands: (i) panchromatic band ( μm) with a spatial resolution of 2.5 m; (ii) green band range is μm, 10 m 10 m resolution; (iii) red band range is μm, 10 m 10 m resolution; (iv) infrared band ( μm) with 20 m spatial resolution. Landsat TM was acquired on 31 August, Six bands with 30 m 30 m resolution (TM-1: μm; TM-2: μm; TM-3: μm; TM-4: μm; TM-5: μm; and TM-7: μm) were extracted from the Landsat TM scene. The original digital number (DN) data were calibrated and transformed into reflectance values with a processing module in the ENVI v4.7 software. Cloud-free scenes for 1 August, 2009 (Aerial photography), 31 August, 2009 (Landsat TM), and SPOT image were used for scale transformation. All of these images were acquired under clear atmospheric conditions in August, which is during the growing season and the best time for marsh vegetation research. Weather conditions were clear when the images were acquired. The aerial classification map shows 9 types of land cover classifications, and these include almost all of the vegetation types found in the entire study area. The aerial photographic classification map using ecoginition can achieve a high level of classification accuracy (above 85%) (Li et al, 2011). Because it offers a reasonably accurate approximation of the real wetlands surface cover types, it is considered a good proxy to use as a classification training sample for coarse resolution imagery. 2.4 Data pre-processing Prior to the analysis, original digital number data recorded at the top of the atmosphere were converted to total radiance values at the satellite level and corrected for atmospheric effects with a processing module in the ENVI v4.7 software package. It was necessary to geo-reference the images as the imagery contained geometric errors from sources that ranged from variations in the altitude, attitude, and velocity of the sensor platform, to factors such as panoramic distortion, earth curvature, earth rotation, relief displacement, and nonlinearities in the sweep of the sensor s instantaneous field of view (IFOV) (Lilesand and Kiefer 1994). Aerial photography, SPOT-5 and Landsat TM images were rectified for the UTM projection and WGS-84 coordination system using ArcGIS. And the aerial photographs were used as reference images to correct SPOT-5 and Landsat TM images. An adequate number of ground control points (GCP=((n+1) (n+2))/2) were selected and the registration procedure achieved a high level of accuracy with less than TM pixels mean square error (RMSE) for images in SPOT-5 and Landsat TM images cover the entire study area. The aerial photographs, on the other hand, cover an area of 1.64 km 2, which is only the part of study area but has typical vegetation features for HNNR. The aerial classification image includes 9 types of wetlands land covers at a vegetation community scale. All types of features from aerial classification image were used to clip Landsat TM and SPOT-5 images. 3 Method 3.1 Image up-scaling To infer spatial information when going from a finer to a coarser spatial resolution, we proposed and compared four up-scaling methods (nearest, bilinear, cubic, majority), all part of the widely used Window Averaging (WA) method. We applied and compared these methods in a case study in which SPOT-5 images were aggregated from 10 m 10 m to 20 m 20 m, and 30 m 30 m images (Fig. 2). At each level, data are aggregated directly from the original images (e.g. from 10 m 10 m to 30 m 30 m) instead of a precious aggregation (e.g. from 20 m 20 m to 30 m 30 m). The spatial details lost during aggregation are considered to be aggregation errors (Ling Blan and Rachael Butler, 1999). In our study, the proposed images were evaluated by 5 indices Maximum, Mean, Standard deviation (Std), Mean Rate of change (Mean-R), Std Rate of change (Std-R). In nearest neighbor (NN), or zero-order interpolation, the DN of the pixel closest to the location of the original input pixel is assigned as the DN value at the output pixel s location (Hay et al. 1997). In bilinear (BIL), or first-order interpolation, a DN is assigned to an output pixel by interpolating DNs in two orthogonal directions within the input image. Essentially, a plane is fit to the four pixel values nearest the location of the pixel in the input image, then a new output DN is computed based on the weighted distances to

4 110 Journal of Resources and Ecology Vol. 7 No. 2, 2016 (a) Original (b) 20 m 20 m (c) 30 m 30 m Fig.2 Illustration of aggregated images. (a) is an original image (10 m 10 m), (b) an aggregated image 20 m 20 m, and (c) an aggregated image 30 m 30 m these points (Hay et al. 1997). In cubic convolution (CC), reassembling occurs in much the same manner as bilinear interpolation, except that the weighted values of 16 pixels surrounding the location of the pixel in the input image are used to determine the value of the output pixel (Jensen 2004). In majority (MAJ), majority assigns the most popular value within the filter window, giving a smoother look. Meanoriginal Meani Mean-R 100% Mean where original Mean original is the mean of SPOT-5 image original, Mean i is the mean of SPOT-5 aggregated image, i indicates different aggregated images. Stdoriginal Stdi Std-R= 100% Std original where Std original is the Std of the SPOT-5 image original, Std i is the Std of SPOT-5 aggregated image, i indicates different aggregated images. The assessment of image aggregation images of the four Window Averaging (WA) methods is as follows (SPOT-5 band 1 is used as an example): As we can see from Table 1, referring to the indices Mean-R and Std-R, the nearest neighbor samples are better than other samples. And when image were aggregated to 30m 30m resolution image by NN, BIL and CC methods, the indices showed no further Table 1 Performance assessment of image aggregation processing using the WA method Maximum Mean Standard deviation Mean-R Std-R Original image m-NN % 0.03% 20m-BIL % 0.21% 20m-CC % 0.17% 20m-MAJ % 1.30% 30m-NN % 0.03% 30m-BIL % 0.03% 30m-CC % 0.03% 30m-MAJ % 1.32% changes. The worst up-scaling method is majority, which results in big aggregation errors for the five indices when compared to the original image. For these reasons, we chose the NN method to aggregate images. 3.2 Classification method In order to derive classes with greater accuracy, a supervised classification with the maximum likelihood algorithm (MLC) was applied to classify SPOT and TM images. The ground truth maps combined with field knowledge and unsupervised classification results were the basis for selecting accurate and reliable training samples for performing MLC classification (Shanmugam et al. 2006). Before performing the MLC classification, we applied aerial photography classification maps (vector data) as training samples. As Table 2 shows, the maps did not include Crops as a plant type because the aerial photography area did not include this type of plant or crops were distributed on the edge of image or along the road. To address this issue, we used field knowledge to select as training samples images that included crops. And because the number of forest and shrub wetland training samples were particularly low, we selected more of these from the images. Following the application of selection training samples, wetland information classes were derived from three resolution SPOT-5 images, 10 m 10 m, 20 m 20 m, and 30 m 30 m of HNNR using the MLC method. The accuracy of these classes was assessed by using KAPPA accuracy. Table 2 Land cover categories for wetland classification in HNNR Ecosystem type Community type Code Shallow water Pond 1 Shrub wetland Betula fruticasa- Salix brachypoda- Alnus 2 sibirica Forest Quercus mongolica Populus davidiana Betula platyphylla 3 Marsh Carex pseudocuraica 4 Carex lasiocarpa 5 Wet meadow Carex spiculosa 6 Calamagrostis angustifolia 7 Cultivated land Crops 8

5 LI Na, et al.: Remote Sensing Classification of Marsh Wetland with Different Resolution Images Results and analysis The MLC classification method was performed using SPOT-5 and Landsat TM imagery of the HNNR site. The purpose of the classification was to assess the performance of the MLC method and the rate of classification accuracy from images of different resolutions by using high-resolution imagery classification mapping as training samples. The results of wetland classification represent a generalization of real wetland landscape types; the wetland types are always fragmented patches. Therefore, it is necessary to check accuracy of any land cover classification with ground reference sample data (Ahmad et al. 1992). The assessment method is also important for evaluating the accuracy of the results of classification mapping. The error matrix provided a concise means of examining per class map errors by including both errors of commission and errors of omission (Dicks and Lo 1990). The accuracy assessment of MLC classification using error matrix was carried out on SPOT and Landsat TM image by KAPPA accuracy. The result of performing a KAPPA analysis is a KHAT statistic (an estimate of KAPPA), which is used as a measure of agreement or accuracy. The KHAT statistic is computed as: r N x ( x x ) K N x x r ii ii i i 1 i 1 r 2 ii( ii x i ) i 1 where r is the number of rows in the matrix, xii the number of observations in row i and column i, x +i the marginal totals of row i and column i, respectively, and N is the total number of observations. The classification accuracy of aerial photography was assessed with a confusion matrix. The assessment indicated a highly accurate and reliable classification result, with an overall classification accuracy of 91.77% and the overall kappa statistics of 0.91 (LI et al. 2011). As mentioned earlier, some manual checks and corrections were made to ensure that the classification result reached a higher level of accuracy. Thus, the aerial photography classification maps represented the real land-cover situation and could be used as training samples to classify SPOT and Landsat TM images. And in order to show a map with similar effect, we selected three bands, the Green, Red and Near Infrared bands from the limited band combinations of SPOT-5, and used these to classify by MLC. Using training samples and the land cover categories to perform the classification, the result is shown in Fig. 3. The accuracy of these classes was assessed by using KAPPA accuracy assessment (Table 3). Producer s accuracy of the KAPPA assessment was computed for four images (Table 3 and Fig. 3). It indicates that it is useful to classify lower resolution remote images using high resolution classification vector data as training samples at a plant community scale. Most producer s accuracy was over 75%. All the maps are able to show the typical spatial distribution of a marsh wetland landscape. From the classification maps we can see going from high terrain to low there are forest, shrub, wet meadow (Calamagrostis angustifolia and Carex spiculosa), marsh (Carex pseudocuraica and Carex lasiocarpa) and pond. This displays the degradation gradient of marsh wetland. As mentioned earlier, SPOT-5 20 m 20 m and 30 m 30 m is transformed by SPOT-5 10 m 10 m, and as the scale-up images get coarser, we can see from the three maps that coarser images show less detailed information than finer ones, and this is similar to what we learn from the accuracy data (Table 3). Coarser images contain more confused pixel areas, in which it is difficult to distinguish one type from another type. Moreover, the least accurate TM imagery is thought to result essentially from spectral confusion or overlapping between the following classes: forest, shrub and crop, pond and marsh in swampy areas, including marsh types: Carex pseudocuraica and Carex lasiocarpa, wet meadows and marsh types: Carex spiculosa and Carex lasiocarpa. The results also indicate the core zone area is protected better than buffer zone and experimental zone wetlands. The experimental zone degrades rapidly because of irrational development by humans. However, a comparison of SPOT-5 30 m 30 m classification maps with TM images shows they have the same spatial resolutions, the same training samples and even the same bands composition, but differences in classification results. We can get the results from the maps or use the accuracy assessment results. TM classification maps show visibly higher landscape fragmentation than SPOT-5 30m 30m, which have better classification results for landscape types (forest, shrub, water, crop) than TM, but have no advantage in terms of patch fragmentation. What causes this phenomenon? The decrease in classification accuracy of different kinds of images with the same resolution is thought to result from the fact that the different image types use different sensors. As we all know, Landsat TM uses a scanner mirror to scan the ground while SPOT-5 takes a CCD electronic scan that gathers more detailed information for big patches with a high degree of aggregation, but performs poorly when dealing with fragmented patches at the same time. Image classification accuracy is always affected by areal fraction and aggregation degree as well as diversities and patch shape. 5 Discussion and conclusions Marsh wetland mapping and monitoring is crucial for preserving valuable wetland ecosystems. Thus, it is urgent to develop remote sensing techniques for wetland monitoring. A special emphasis in this study was placed on the analysis of SPOT-5 images at three different resolutions and Landsat TM imagery because SPOT-5 and TM images provide higher resolution data and are the most commonly used images. The selection of training samples is necessary for most

6 112 Journal of Resources and Ecology Vol. 7 No. 2, 2016 Fig.3 (a-d) Maps of wetland classes derived from maximum likelihood classification using SPOT-5 10 m 10 m, 20 m 20 m, 30 m 30 m and TM image data of HNNR Table 3 Producer s accuracy of four images in HNNR Class Images SPOT-10 m Producer accuracy Forest Shrub Water Crop (1) (2) (3) (4) 90.24% 87.50% 94.12% 92.31% 82.86% 81.08% 83.33% 86.67% SPOT-20 m 89.36% 86.67% 93.02% 90.24% 78.13% 79.49% 77.42% 80.00% SPOT-30 m 88.10% 82.50% 90.00% 89.74% 72.73% 71.79% 73.53% 73.17% TM 83.3% 85.7% 86.5% 87.8% 74.3% 75.7% 75.0% 71.4% PS: (1): Carex lasiocarpa, (2): Carex spiculosa, (3): Carex pseudocuraica, (4): Carex lasiocarpa classification methods like MLC. The selection criteria are stringent and expert knowledge and detailed knowledge of the research area are needed to fulfill them. Moreover, we have to adjust the training samples according to the classification results. This study selected vector classification maps of high resolution images as training samples applied to SPOT-5 images and Landsat TM imagery for classifications at a plant community scale. The result showed that this kind of method provided detailed information about various marsh wetland features in a relatively quick and simple manner and seemed to be well-suited to map marsh wetland cover types. The advantage of this kind of training sample

7 LI Na, et al.: Remote Sensing Classification of Marsh Wetland with Different Resolution Images 113 lies in fact that the operator does not have to have extensive experience or knowledge. The classification did not depend on bands compositions or the time the image was acquired. As long as no great changes to the landscape occurrs, the images only need to be in the same area. The accuracy assessment based on KAPPA accuracy measures suggested that classification accuracy for the four maps was generally over 75%. However, owing to the natural characteristics of wetlands and particular scene characteristics, the vector data from high classification maps used as training samples applied to lower image was judged to be adequate for the purposes of the study. Moreover, as the scale-up of spatial resolution was applied to SPOT-5 images, accuracy decreased. It demonstrated that finer images include more information than coarser images. Comparing SPOT-5 30m 30m with Landsat TM imagery, images with the same spatial resolution but taken with different satellite image sensors, it was apparent that the transformed image did not lose much detail and retained good characteristics from the finer image. The result indicates that different satellite sensors show different land information. Although the use of the vector training samples on images offers the advantage of simplicity and the ability to apply the method for different scale images, the ability of the classification maps to show fragmentation patches accurately was inadequate. Image classification accuracy is always affected by areal fraction and aggregation degree, as well as by diversities and patch shape. This may be a limitation of pixel-based classifications and, as a result, soft classification methods such as Spectral Mixture Model or Object-based methods will be tried for the next experiment. From the classification maps we can see that the core zone area is better protected than buffer zone and experimental zone wetlands, and that the experimental zone degrades fast because of irrational development by humans. So protection of wetlands is necessary to prevent degradation of natural resources. References Ahmad, W., et al Land cover mapping in a rugged terrain area using Landsat MSS data. International Journal of Remote Sensing, 13(4): Atkinson, P.M., & Aplin, P Spatial variation in land cover and choice of spatial resolution for remote sensing. International Journal of Remote Sensing, 25(18): Atkinson, P.M., & Tate, N.J Spatial Scale Problems and Geostatistical Solutions: A Review. Professional Geographer, 52(4): Bian, L. (1997). Multiscale nature of spatial data in scaling up environmental models. In, In Scale in RCII/otc Sensing and GIS, eds, D.A. Quattrochi and Bian, L. Dicks, S.E., & Lo, T.H.C Evaluation of thematic map accuracy in a land-use and land-cover mapping program. Photogrammetric Engineering & Remote Sensing, 56(9): Gupta, V.K., et al Scale problems in hydrology. Contributions of the Robertson Workshop. Hydrological Processes, 9(3/4): Hay, G.J., et al Spatial thresholds, image-objects, and upscaling: A multiscale evaluation. Remote Sensing of Environment, 62(1): 1-19 Jensen, J.R Introductory Digital Image Processing. Series in Geographic Information Science, 2nd edn Lilesand, T., & Kiefer, R Remote Sensing and Image Interpretation. Remote Sensing & Image Interpretation Li Na, Zhou Demin, Zhao Kuiyi Marsh classification mapping at a community scale using high-resolution imagery. Acta Ecologica Sinica, 31(22): (in Chinese) Liu Xingtu, Ma Xuehui. Environmental Changes and Ecological Conservation in the Sanjiang Plain. Beijing: Science Press, (in Chinese) Nature, I.U.f.C.o., & Bureau, N.R.R.C. (1984). Convention on Wetlands of International Importance Especially as Waterfowl Habitat : proceedings of the second conference of the parties: Groningen, Netherlands, 7 to 12 May, Ramsar Convention Bureau, International Union for Conservation of Nature and Natural Resources Ozesmi, S.L., & Bauer, M.E Satellite Remote Sensing of Wetlands. Wetlands Ecology & Management, 10(5): Quattrochi, D.A., & Goodchild, M.F. (1997). Scale in remote sensing and GIS. Lewis Publishers Shanmugam, P., et al A comparison of the classification of wetland characteristics by linear spectral mixture modelling and traditional hard classifiers on multispectral remotely sensed imagery in southern India. Ecological Modelling, 194(4): Wu, H., & Li, Z.L Scale issues in remote sensing: a review on analysis, processing and modeling. Sensors, 9(3): Zhou, D.M., et al Driving Forces for the Marsh Wetland Degradation in the Honghe National Nature Reserve in Sanjiang Plain, Northeast China. Environmental Modeling & Assessment, 14(1):

8 114 Journal of Resources and Ecology Vol. 7 No. 2, 2016 李娜 1,2, 谢高地 1, 周德民 3, 张昌顺 1, 焦翠翠 中国科学院地理科学与资源研究所, 北京 ; 2 中国科学院研究生院, 北京 ; 3 首都师范大学城市环境过程与数值模拟国家重点实验室, 北京 摘要 : 成功的生态系统的监测取决于准确的分类结果 本文试图对沼泽湿地的生态特征进行监测 遥感分类常被用于描述生态系统或土地覆盖的格局情况 不同空间分辨率的的遥感影像反映的景观类型, 景观特征均存在差异 本文利用不同空间分辨率的遥感影像, 对洪河自然保护区湿地生态系统在群落尺度上进行监测 洪河湿地自然保护区是典型的内陆原始沼泽湿地生态系统的保护地, 以草本沼泽植被和水生植被为主 根据 SPOT-5 不同分辨率 10 m 10 m 20 m 20 m 30 m 30 m 的影像, 以及 30 m 空间分辨率的 Landsat TM 影像, 利用最大似然法 (MLC) 对洪河保护区进行分类 为了验证分类结果的精度, 本文利用航空影像分类数据作为样本, 对 SPOT-5 和 TM 分类结果进行 KAPPA 精度验证 由分类结果, 得出以下结论 :(1) 分类结果验证数据对结果的影响很重要 ;(2) 不同传感器的影像对地面生态系统的表达内容不一样, 即使是处于相同的光谱波段, 同一空间分辨率的情况下 分类结果不仅取决于生态系统类型的多样性和斑块形状, 还取决于面积维数和斑块的聚合度 (3) 洪河自然保护区核心区湿地保护最好, 其次是缓冲区, 最差的是实验区 实验区已经有大面积的湿地被人类开垦成农田, 湿地生态系统已经退化的很严重 因此, 对珍稀生态系统的保护应该与人类的活动区强制的隔离开, 才能保护原始湿地生态系统多样性 关键词 : 遥感分类 ; 沼泽湿地 ; 洪河自然保护区 ; 航空影像 ;SPOT-5;TM