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.)     

Exploring image fusion of ALOS/PALSAR data and LANDSAT data to differentiate forest area

Remote sensing data utilize valuable information via various satellite sensors that have different specifications. Image fusion allows the user to combine different spatial and spectral resolutions to improve the information for purposes such as forest monitoring and land cover mapping. In this study, I assessed the contribution of dual-polarized Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar data to multispectral Landsat imagery. The research investigated the separability of forested areas using different image fusion techniques. Quality analysis of the fused images was conducted using qualitative and quantitative analyses. I applied the support vector machine image classification method for land cover mapping. Among all methods examined, the à trous wavelet transform method best differentiated the forested area with an overall accuracy (OA) of 94.316%, while Landsat had an OA of 92.626%. The findings of this study indicated that optical-SAR-fused images improve land cover classification, which results in higher quality forest inventory data and mapping.     

Vegetation type discrimination on Landsat TM data in heterogeneous forested landscape of Western Ghats—Accuracy evaluation from large scale aerial photo maps

In the present study, forest type classification using Landsat TM False Colour Composite (FCC) bands 2, 3, 4 has been evaluated for mapping highly heterogeneous forest environment of Western Ghats (Kerala). Visual interpretation of Landsat TM FCC has been carried out to identify bioclimatic vegetation types. For accuracy estimation maps prepared from 1∶15,000 scale black-and-white aerial photographs have been used as ground check data. For comparison aerial photomap classes have been aggregated to match with Landsat-TM-derived map. The classification accuracy of ten major bioclimatic and landcover types was estimated using systematic sampling procedure. The overall classification accuracy of the forest types for the study area was 88.33%.     

Phenological metrics-based crop classification using HJ-1 CCD images and Landsat 8 imagery

Crop type data are an important piece of information for many applications in agriculture. Extracting crop type using remote sensing is not easy because multiple crops are usually planted into small parcels with limited availability of satellite images due to weather conditions. In this research, we aim at producing crop maps for areas with abundant rainfall and small-sized parcels by making full use of Landsat 8 and HJ-1 charge-coupled device (CCD) data. We masked out non-vegetation areas by using Landsat 8 images and then extracted a crop map from a long-term time-series of HJ-1 CCD satellite images acquired at 30-m spatial resolution and two-day temporal resolution. To increase accuracy, four key phenological metrics of crops were extracted from time-series Normalized Difference Vegetation Index curves plotted from the HJ-1 CCD images. These phenological metrics were used to further identify each of the crop types with less, but easier to access, ancillary field survey data. We used crop area data from the Jingzhou statistical yearbook and 5.8-m spatial resolution ZY-3 satellite images to perform an accuracy assessment. The results show that our classification accuracy was 92% when compared with the highly accurate but limited ZY-3 images and matched up to 80% to the statistical crop areas.     

面向对象方法的时间序列MODIS数据湿地信息提取——以洞庭湖流域为例

以洞庭湖流域为研究区,对大范围湿地信息遥感提取方法进行了研究。先基于时间序列MODIS EVI及物候特征参数,通过J-M(Jeffries-Matusita distance)距离分析,构建了MODIS(250 m)最佳时序组合分类数据;其次,通过Johnson指数确定了最佳分割尺度,采用面向对象的遥感分类方法(Random tree分类器)提取了洞庭湖流域的湿地信息,并验证该方法的适用性。研究结果表明,基于时序数据与面向对象的Random tree分类的总体精度和Kappa系数分别为78.84%和0.71,较之基于像元的相同算法的总体分类精度和Kappa系数分别提高了5.79%和0.04。同时,基于面向对象方法的湿地整体的用户精度与生产者精度较基于像元方法分别提高了4.56%和6.21%,可有效提高大区域湿地信息提取的精度。     

Integrating RapidEye and ancillary data to map alpine habitats in South Tyrol,Italy

In this paper, we present a two-stage method for mapping habitats using Earth observation (EO) data in three Alpine sites in South Tyrol, Italy. The first stage of the method was the classification of land cover types using multi-temporal RapidEye images and support vector machines (SVMs). The second stage involved reclassification of the land cover types to habitat types following a rule-based spatial kernel. The highest accuracies in land cover classification were 95.1% overall accuracy, 0.94 kappa coefficient and 4.9% overall disagreement. These accuracies were obtained when the combination of images with topographic parameters and homogeneity texture was used. The habitat classification accuracies were rather moderate due to the broadly defined rules and possible inaccuracies in the reference map. Overall, our proposed methodology could be implemented to map cost-effectively alpine habitats over large areas and could be easily adapted to map other types of habitats.     

Forest cover classification using Landsat ETM+ data and time series MODIS NDVI data

Forest cover plays a key role in climate change by influencing the carbon stocks, the hydrological cycle and the energy balance. Forest cover information can be determined from fine-resolution data, such as Landsat Enhanced Thematic Mapper Plus (ETM+). However, forest cover classification with fine-resolution data usually uses only one temporal data because successive data acquirement is difficult. It may achieve mis-classification result without involving vegetation growth information, because different vegetation types may have the similar spectral features in the fine-resolution data. To overcome these issues, a forest cover classification method using Landsat ETM+ data appending with time series Moderate-resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data was proposed. The objective was to investigate the potential of temporal features extracted from coarse-resolution time series vegetation index data on improving the forest cover classification accuracy using fine-resolution remote sensing data. This method firstly fused Landsat ETM+ NDVI and MODIS NDVI data to obtain time series fine-resolution NDVI data, and then the temporal features were extracted from the fused NDVI data. Finally, temporal features combined with Landsat ETM+ spectral data was used to improve forest cover classification accuracy using supervised classifier. The study in North China region confirmed that time series NDVI features had significant effects on improving forest cover classification accuracy of fine resolution remote sensing data. The NDVI features extracted from time series fused NDVI data could improve the overall classification accuracy approximately 5% from 88.99% to 93.88% compared to only using single Landsat ETM+ data.     

Remote sensing classification approach to large-scale crop cultivation identification: A case study of the Aral Sea Basin

Since the collapse of the Soviet Union, the crop cultivation structure in the Aral Sea Basin has changed dramatically, and these changes are worth studying. However, historical crop remote sensing mapping at the watershed scale remains challenging, especially crop misclassification at the cropland edge due to mixed pixels. Therefore, we proposed a field segmentation approach to constrain field edges based on time-series Sentinel-2 remote sensing images and the Google Earth Engine platform and then employed the random forest algorithm to perform crop classification based on time series Landsat/Sentinel-2 images and crop phenology information to produce historical crop maps in the Aral Sea Basin from the 1990s onward. The results showed that the intersection over union between the extracted field edges and in situ-measured field size data was 0.65. The overall accuracy of crop mapping was 95.2% in 2019. Then, we extended our method to historical mapping over the 1991–2015 period with accuracies ranging from 82.8% to 91.3%. Moreover, our method applied to historical mapping works well in terms of accuracy and policy matching. These findings indicate that our method can accurately distinguish cropland edges to reduce classification errors due to mixed pixels. This method is promising for solving the cropland edge problem for historical crop mapping in the Aral Sea Basin and can potentially provide a reference for historical crop classification in other watersheds of the world.     

Crop classification and acreage estimation in North Korea using phenology features

In North Korea, reliable and timely information on crop acreage and spatial distribution is hard to obtain. In this study, we developed a fast and robust method to estimate crop acreage in North Korea using time-series normalized difference vegetation index (NDVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data. We proposed a method to identify crop type based on NDVI phenology features using data collected in other areas with similar agri-environmental conditions to mitigate the shortage of ground truth data. Eventually the classification map (MODIScrop) was assessed using the Food and Agriculture Organization (FAO) statistical data and high-resolution crop classification maps derived from one Landsat scene (LScrop). The Pareto boundary method was used to assess the accuracy and crop distribution of the MODIScrop maps. Results showed that acreage derived from the MODIScrop maps was generally consistent with that reported in the FAO data (a relative error <4.1% for rice and <6.1% for maize, and <9.0% for soybean except for in 2004, 2008, and 2009) and the maps derived from the LScrop (a relative error about 5% in 2013, and 7% in 2008 and 2014). The classification accuracy reached 74.4%, 69.8%, and 73.1% of the areas covered by the Landsat images in 2008, 2013, and 2014, respectively. This indicates that features derived from NDVI profiles were able to characterize major crops, and the approaches developed in this study are feasible for crop mapping and acreage estimation in regions with limited ground truth data.     

Mapping multiple tree species classes using a hierarchical procedure with optimized node variables and thresholds based on high spatial resolution satellite data

ABSTRACT

Tree species distribution mapping using remotely sensed data has long been an important research area. However, previous studies have rarely established a comprehensive and efficient classification procedure to obtain an accurate result. This study proposes a hierarchical classification procedure with optimized node variables and thresholds to classify tree species based on high spatial resolution satellite imagery. A classification tree structure consisting of parent and leaf nodes was designed based on user experience and visual interpretation. Spectral, textural, and topographic variables were extracted based on pre-segmented images. The random forest algorithm was used to select variables by ranking the impact of all variables. An iterating approach was used to optimize variables and thresholds in each loop by comprehensively considering the test accuracy and selected variables. The threshold range for each selected variable was determined by a statistical method considering the mean and standard deviation for two subnode types at each parent node. Classification of tree species was implemented using the optimized variables and thresholds. The results show that (1) the proposed procedure can accurately map the tree species distribution, with an overall accuracy of over 86% for both training and test stages; (2) critical variables for each class can be identified using this proposed procedure, and optimal variables of most tree plantation nodes are spectra related; (3) the overall forest classification accuracy using the proposed method is more accurate than that using the random forest (RF) and classification and regression tree (CART). The proposed approach provides results with 3.21% and 7.56% higher overall land cover classification accuracy and 4.68% and 10.28% higher overall forest classification accuracy than RF and CART, respectively.     

Lithologic mapping of a forested montane terrain from Landsat 5 TM image

Thick forest cover and poor infrastructures are the major hindrances for detailed lithologic mapping in an inaccessible montane landscape. To overcome these limitations, we utilize a Landsat 5 TM image to map lithology using vegetation and drainage pattern as an indicator of underlying rock types in a heavily forested region of the Chittagong Hill Tracts area located in southeastern Bangladesh. We use supervised and unsupervised classifiers for a vegetation-based approach while on-screen digitization is used for drainage patterns-based mapping. Field observations were used for mapping lithology and evaluating accuracy. Overall, our results agree well with the current geologic map and improve it by providing a more spatially detailed distribution of the sandstone and shale. The performances of all approaches are good at the inner and outer flanks of anticlines located in the study area while the drainage pattern mapping performs best at the mid-flank area.     

A semi-ellipsoid-model based fuzzy classifier to map grassland in Inner Mongolia,China

Remote sensing techniques offer effective means for mapping plant communities. However, mapping grassland with fine vegetative classes over large areas has been challenging for either the coarse resolutions of remotely sensed images or the high costs of acquiring images with high-resolutions. An improved hybrid-fuzzy-classifier (HFC) derived from a semi-ellipsoid-model (SEM) is developed in this paper to achieve higher accuracy for classifying grasslands with Landsat images. The Xilin River Basin, Inner Mongolia, China, is chosen as the study area, because an acceptable volume of ground truthing data was previously collected by multiple research communities. The accuracy assessment is based on the comparison of the classification outcomes from four types of image sets: (1) Landsat ETM+ August 14, 2004, (2) Landsat TM August 12, 2009, (3) the fused images of ETM+ with CBERS, and (4) TM with CBERS, respectively, and by three classifiers, the proposed HFC-SEM, the tetragonal pyramid model (TPM) based HFC, and the support vector machine method. In all twelve classification experiments, the HFC-SEM classifier had the best overall accuracy statistics. This finding indicates that the medium resolution Landsat images can be used to map grassland vegetation with good vegetative detail when the proper classifier is applied.     

A spatial–temporal Hopfield neural network approach for super-resolution land cover mapping with multi-temporal different resolution remotely sensed images

The mixed pixel problem affects the extraction of land cover information from remotely sensed images. Super-resolution mapping (SRM) can produce land cover maps with a finer spatial resolution than the remotely sensed images, and reduce the mixed pixel problem to some extent. Traditional SRMs solely adopt a single coarse-resolution image as input. Uncertainty always exists in resultant fine-resolution land cover maps, due to the lack of information about detailed land cover spatial patterns. The development of remote sensing technology has enabled the storage of a great amount of fine spatial resolution remotely sensed images. These data can provide fine-resolution land cover spatial information and are promising in reducing the SRM uncertainty. This paper presents a spatial–temporal Hopfield neural network (STHNN) based SRM, by employing both a current coarse-resolution image and a previous fine-resolution land cover map as input. STHNN considers the spatial information, as well as the temporal information of sub-pixel pairs by distinguishing the unchanged, decreased and increased land cover fractions in each coarse-resolution pixel, and uses different rules in labeling these sub-pixels. The proposed STHNN method was tested using synthetic images with different class fraction errors and real Landsat images, by comparing with pixel-based classification method and several popular SRM methods including pixel-swapping algorithm, Hopfield neural network based method and sub-pixel land cover change mapping method. Results show that STHNN outperforms pixel-based classification method, pixel-swapping algorithm and Hopfield neural network based model in most cases. The weight parameters of different STHNN spatial constraints, temporal constraints and fraction constraint have important functions in the STHNN performance. The heterogeneity degree of the previous map and the fraction images errors affect the STHNN accuracy, and can be served as guidances of selecting the optimal STHNN weight parameters.     

专家系统在 TM 图像分类中的应用

提高TM图像的分类精度,是图像处理及应用领域中一个很重要的研究课题。本文在总结已有成果基础上,首先利用现有的统计分类技术,对待分类图像进行预分类,并检测出“不确定”像元。然后综合光谱、地理、土壤类型、早期判别结果、目视判读经验等各种知识和信息,充分发挥专家系统的推理判断能力,对“不确定”像元的类别作进一步判别,使得整幅图像的分类精度得到改善。并据此初步建立了一个土地利用的分类系统。试验证明,这种分类方法的精度比仅用单一多光谱信息的统计分类法(最大似然法)提高约8％。     

Updating Landsat-based forest cover maps with MODIS images using multiscale spectral-spatial-temporal superresolution mapping

With the high deforestation rates of global forest covers during the past decades, there is an ever-increasing need to monitor forest covers at both fine spatial and temporal resolutions. Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat series images have been used commonly for satellite-derived forest cover mapping. However, the spatial resolution of MODIS images and the temporal resolution of Landsat images are too coarse to observe forest cover at both fine spatial and temporal resolutions. In this paper, a novel multiscale spectral-spatial-temporal superresolution mapping (MSSTSRM) approach is proposed to update Landsat-based forest maps by integrating current MODIS images with the previous forest maps generated from Landsat image. Both the 240 m MODIS bands and 480 m MODIS bands were used as inputs of the spectral energy function of the MSSTSRM model. The principle of maximal spatial dependence was used as the spatial energy function to make the updated forest map spatially smooth. The temporal energy function was based on a multiscale spatial-temporal dependence model, and considers the land cover changes between the previous and current time. The novel MSSTSRM model was able to update Landsat-based forest maps more accurately, in terms of both visual and quantitative evaluation, than traditional pixel-based classification and the latest sub-pixel based super-resolution mapping methods The results demonstrate the great efficiency and potential of MSSTSRM for updating fine temporal resolution Landsat-based forest maps using MODIS images.     

Assessment of radargrammetric DSMs from TerraSAR-X Stripmap images in a mountainous relief area of the Amazon region

The Brazilian Amazon is a vast territory with an enormous need for mapping and monitoring of renewable and non-renewable resources. Due to the adverse environmental condition (rain, cloud, dense vegetation) and difficult access, topographic information is still poor, and when available needs to be updated or re-mapped. In this paper, the feasibility of using Digital Surface Models (DSMs) extracted from TerraSAR-X Stripmap stereo-pair images for detailed topographic mapping was investigated for a mountainous area in the Carajás Mineral Province, located on the easternmost border of the Brazilian Amazon. The quality of the radargrammetric DSMs was evaluated regarding field altimetric measurements. Precise topographic field information acquired from a Global Positioning System (GPS) was used as Ground Control Points (GCPs) for the modeling of the stereoscopic DSMs and as Independent Check Points (ICPs) for the calculation of elevation accuracies. The analysis was performed following two ways: (1) the use of Root Mean Square Error (RMSE) and (2) calculations of systematic error (bias) and precision. The test for significant systematic error was based on the Student’s-t distribution and the test of precision was based on the Chi-squared distribution. The investigation has shown that the accuracy of the TerraSAR-X Stripmap DSMs met the requirements for 1:50,000 map (Class A) as requested by the Brazilian Standard for Cartographic Accuracy. Thus, the use of TerraSAR-X Stripmap images can be considered a promising alternative for detailed topographic mapping in similar environments of the Amazon region, where available topographic information is rare or presents low quality.     

光谱特征扩展的时间序列Landsat数据地表覆盖分类

时间序列遥感影像常用于地表覆盖监测及其变化监测。然而,利用时序遥感数据—尤其是中分辨率遥感数据监测地表覆盖变化,其方法基本是先对多期影像分别进行监督分类然后对比分类结果。由于这种方法需要对每期遥感影像单独选择分类训练样本,而对于历史影像,常常难以获得可靠的样本数据。本文基于遥感数据定量化处理,尝试利用光谱特征扩展方法对时间序列Landsat数据进行分类:首先,结合一种新的大气校正方法和相对辐射归一化方法,对时间序列Landsat数据进行定量化处理,以消除各期影像之间的辐射差异,获得地表反射率数据。然后,论文选择一期易于获得分类训练样本的反射率数据作为"参考影像",并结合样本数据提取不同地表覆盖类型的光谱特征。最后,将"参考影像"中提取的地物光谱特征,扩展到所有时间序列反射率数据进行分类。论文利用青藏高原玛多地区的5景Landsat数据对本文的方法进行了验证,结果显示:基于光谱特征扩展的分类方法,可有效对定量化处理后的Landsat数据进行分类,分类总体精度为88.35%—94.25%,分类结果和传统的单景监督分类结果具有较好的一致性。此外,研究也发现,"参考影像"和待分类图像获取时间的季相差异会影响其分类的精度。     

综合多特征的Landsat 8时序遥感图像棉花分类方法

传统的多时相遥感图像分类大多拘泥于单一特征,本文基于多时相的Landsat 8遥感数据,开展了综合多特征的特征提取与特征选择方法研究。综合了NDVI时间序列、最佳时相反射率光谱特征以及纹理特征作为初始分类特征,并采用基于属性重要度的粗糙集特征选择算法对其进行特征约简。分类结果表明:(1)利用初始分类特征,分类的总体精度达到92.81%,棉花提取精度达87.4%,与仅利用NDVI时间序列相比,精度分别提高5.53%和5.05%;(2)利用粗糙集选择后的特征分类,分类总体精度可达93.66%,棉花分类精度达92.73%,与初始分类特征提取结果相比,棉花分类精度提高5.33%。基于属性重要度的粗糙集特征选择不仅提高了分类精度,同时有效降低了分类器的计算复杂度。     

空间实验室图像的制图能力分析及其地学应用——以西藏日喀则地区和珠峰地区的试验为例

本文论述了空间实验室测量相机(MC)拍摄的彩色红外立体像对在中比例尺地形制图、正射影像地图以及土地资源调查中的应用可能性,给出1:10万和1:20万比例尺的制图例证,表明平面位置和高程精度能满足1:10万比例尺地形图规范要求;1:10万比例尺影像图可以满足专题制图精度要求。 应用数字图像处理技术,将二种来源(MC,MSS)的遥感数据做数字匹配,并进行信息提取、组合、增强和分类等处理,其结果与原始图像比较表明有独特的优越性,与MSS原始数据相比,图像的空间分辨率明显提高,各种类型界线更分明,可分辨类目增多;而与MC原始图像相比,其光谱特性获得改善且能保持原有的空间分辨率。土地利用类型解释精度与MSS和MC原始图像比较分别提高7—20％左右。     

Classifying historical remotely sensed imagery using a tempo-spatial feature evolution (T-SFE) model

Large and growing archives of orbital imagery of the earth’s surface collected over the past 40 years provide an important resource for documenting past and current land cover and environmental changes. However uses of these data are limited by the lack of coincident ground information with which either to establish discrete land cover classes or to assess the accuracy of their identification. Herein is proposed an easy-to-use model, the Tempo-Spatial Feature Evolution (T-SFE) model, designed to improve land cover classification using historical remotely sensed data and ground cover maps obtained at later times. This model intersects (1) a map of spectral classes (S-classes) of an initial time derived from the standard unsupervised ISODATA classifier with (2) a reference map of ground cover types (G-types) of a subsequent time to generate (3) a target map of overlaid patches of S-classes and G-types. This model employs the rules of Count Majority Evaluation, and Subtotal Area Evaluation that are formulated on the basis of spatial feature evolution over time to quantify spatial evolutions between the S-classes and G-types on the target map. This model then applies these quantities to assign G-types to S-classes to classify the historical images. The model is illustrated with the classification of grassland vegetation types for a basin in Inner Mongolia using 1985 Landsat TM data and 2004 vegetation map. The classification accuracy was assessed through two tests: a small set of ground sampling data in 1985, and an extracted vegetation map from the national vegetation cover data (NVCD) over the study area in 1988. Our results show that a 1985 image classification was achieved using this method with an overall accuracy of 80.6%. However, the classification accuracy depends on a proper calibration of several parameters used in the model.