Landsat TM versus MSS data for forest type identification

Landsat Thematic Mapper (TM) and Multispectral Scanner (MSS) data were digitally analyzed for forest type identification in the Kisatchie Ranger District, Kisatchie National Forest, Louisiana. Ground‐verification maps were produced from field surveys and interpretation of 1.12,000 and 1: 58,000 color‐infrared (CIR) aerial photography of nine compartments. Stand boundary and soils maps were input to a digital Geographic Information System (GIS) with the Landsat and ground‐verification data.

‐ Unsupervised classifications of the Landsat data did not identify the above cover types well. Supervised classifications were tested by stand agreement to the ground verification. The highest four‐class agreement was obtained for the TM classification (76 percent). Three‐class (open, pine, and hardwoods) stand agreements (81 (MSS) and 85 (TM) percent) were not significantly different as tested by analysis of variance (alpha level 0.1).     

Evaluation of directional normalization methods for Landsat TM/ETM+ over primary Amazonian lowland forests

Biodiversity mapping in extensive tropical forest areas poses a major challenge for the interpretation of Landsat images, because floristically clearly distinct forest types may show little difference in reflectance. In such cases, the effects of the bidirectional reflection distribution function (BRDF) can be sufficiently strong to cause erroneous image interpretation and classification. Since the opening of the Landsat archive in 2008, several BRDF normalization methods for Landsat have been developed. The simplest of these consist of an empirical view angle normalization, whereas more complex approaches apply the semi-empirical Ross–Li BRDF model and the MODIS MCD43-series of products to normalize directional Landsat reflectance to standard view and solar angles. Here we quantify the effect of surface anisotropy on Landsat TM/ETM+ images over old-growth Amazonian forests, and evaluate five angular normalization approaches. Even for the narrow swath of the Landsat sensors, we observed directional effects in all spectral bands. Those normalization methods that are based on removing the surface reflectance gradient as observed in each image were adequate to normalize TM/ETM+ imagery to nadir viewing, but were less suitable for multitemporal analysis when the solar vector varied strongly among images. Approaches based on the MODIS BRDF model parameters successfully reduced directional effects in the visible bands, but removed only half of the systematic errors in the infrared bands. The best results were obtained when the semi-empirical BRDF model was calibrated using pairs of Landsat observation. This method produces a single set of BRDF parameters, which can then be used to operationally normalize Landsat TM/ETM+ imagery over Amazonian forests to nadir viewing and a standard solar configuration.     

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.)     

Agriculture field characterization using aerial photograph and satellite imagery

Successfully delineating management zones that differ in crop productivity is an important component of site-specific management. We compared the effectiveness of the digitally scanned color aerial infrared photographs and digital Landsat Thematic Mapper (TM) data for delineating within-field zones. The zones delineated using normalized difference vegetation index (NDVI) from TM data explained 34% of field yield variance compared with 9% for that from digitally scanned color aerial infrared photograph data. The zones from NDVI using Landsat TM were better able to capture spatial differences in plant growth and relatively stable soil attribute of surface soil organic carbon.     

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%.     

Classification of Satellite Images in Assessing Urban Land Use Change Using Scale Optimization in Object-Oriented Processes (A Case Study: Ardabil City,Iran)

By using satellite imagery, the recognition and evaluation of various phenomena and extraction of information necessary for the planning of land resources or other purposes are easily accomplished. The purpose of this study is to compare the efficiency of seven commonly used methods of monitored classification of satellite data to evaluate land use changes using TM and OLI Landsat, IRS, Spot5 and Quick Bird bands as well as different color combinations of these images to detect agricultural land, residential areas and aquatic areas using object-oriented processing. Digital processing of satellite images was carried out in 1998 and 2016 using advanced methods. Training samples were extracted in five user classes by eCognition software using segmentation scale optimization, different color combinations and coefficients of shape and compression. The appropriate segmentation scale for arable land, human complications and the blue areas were, respectively, 50, 8 and 10. Then each image was classified separately using seven methods and extracted samples, and efficiency of each classification method was obtained by calculating two general health and Kappa coefficients. The results show that the accuracy of each classification method and the neural network with a total accuracy of 94.475 and Kappa coefficient of 92.095 were selected as the most accurate classification method. These results show that the sampling of educational samples with proper precision of the classes in the images and dependency probability of each satellite images pixel can be useful in classifying group available in helpful area.     

Use of landsat ETM+ SLC-off segment-based gap-filled imagery for crop type mapping

Failure of the Scan Line Corrector (SLC) on the Landsat ETM+ sensor has had a major impact on many applications that rely on continuous medium resolution imagery to meet their objectives. The United States Department of Agriculture (USDA) Cropland Data Layer (CDL) program uses Landsat imagery as the primary source of data to produce crop-specific maps for 20 states in the USA. A new method has been developed to fill the image gaps resulting from the SLC failure to support the needs of Landsat users who require coincident spectral data, such as for crop type mapping and monitoring. We tested the new gap-filled method for a CDL crop type mapping project in eastern Nebraska. Scan line gaps were simulated on two Landsat 5 images (spring and late summer 2003) and then gap-filled using landscape boundary models, or segment models, that were derived from 1992 and 2002 Landsat images (used in the gap-fill process). Various date combinations of original and gap-filled images were used to derive crop maps using a supervised classification process. Overall kappa values were slightly higher for crop maps derived from SLC-off gap-filled images compared to crop maps derived from the original imagery (0.3–1.3% higher). Although the age of the segment model used to derive the SLC-off gap-filled product did not negatively impact the overall agreement, differences in individual cover type agreement did increase (?0.8%–1.6% using the 2002 segment model to ?5.0–5.1% using the 1992 segment model). Classification agreement also decreased for most of the classes as the size of the segment used in the gap-fill process increased.     

Predictive GIS model for potential mapping of Cu,Pb, Zn mineralization

The geologic features indicative of Cu, Pb, Zn mineral deposits in a area are fractures (structure), and host rock sediments. Datasets used include Cu, Pb, Zn deposit points record, geological data, remote sensing imagery (Landsat TM5). The mineral potential of the study area is assessed by means of GIS based geodata integration techniques for generating predictive maps. GIS predictive model for Cu, Pb, Zn potential was carried out in this study area (Weixi) using weight of evidence.     

Multi‐Temporal Study and Detailed Photogeological Mapping of the Armero Debris Flow (Colombia), Using Landsat TM 5 Images

Abstract

On November the 13^th of 1985, the City of Armero (Colombia) was destroyed by debris flows generated by a reactivation of the Nevado del Ruiz Volcano. The flows ocurred in at least three principal pulses, as was observed by the disater's survivors. Landsat TM 5 data processing was carried out in subscenes taken before and after the lahar sedimentation.

False color composites were generated and combined with the geological information available in order to visualize the magnitude of the catastrophe and the flow characteristics. Taking advantage of Landsat TM 5 images with high spectral resolution, a detailed photogeological mapping of the three principal pulses of the debris flows was carried out. Landsat TM 5 proved to be a powerful complementary source of information for hazard assesment of these catastrophic debris flows. The images were used in addition to ground‐based information, and were an easy way to help ordinary people and decision makers understand such hazardous volcanic situations.     

Forest cover trends from time series Landsat data for the Australian continent

In perennial and natural vegetation systems, monitoring changes in vegetation over time is of fundamental interest for identifying and quantifying impacts of management and natural processes. Subtle changes in vegetation cover can be identified by calculating the trends of a vegetation density index over time. In this paper, we apply such an index-trends approach, which has been developed and applied to time series Landsat imagery in rangeland and woodland environments, to continental-scale monitoring of disturbances within forested regions of Australia. This paper describes the operational methods used for the generation of National Forest Trend (NFT) information, which is a time-series summary providing visual indication of within-forest vegetation changes (disturbance and recovery) over time at 25 m resolution. This result is based on a national archive of calibrated Landsat TM/ETM+ data from 1989 to 2006 produced for Australia's National Carbon Accounting System (NCAS). The NCAS was designed in 1999 initially to provide consistent fine-scale classifications for monitoring forest cover extent and changes (i.e. land use change) over the Australian continent using time series Landsat imagery. NFT information identifies more subtle changes within forested areas and provides a capacity to identify processes affecting forests which are of primary interest to ecologists and land managers. The NFT product relies on the identification of an appropriate Landsat-based vegetation cover index (defined as a linear combination of spectral image bands) that is sensitive to changes in forest density. The time series of index values at a location, derived from calibrated imagery, represents a consistent surrogate to track density changes. To produce the trends summary information, statistical summaries of the index response over time (such as slope and quadratic curvature) are calculated. These calculated index responses of woody vegetation cover are then displayed as maps where the different colours indicate the approximate timing, direction (decline or increase), magnitude and spatial extent of the changes in vegetation cover. These trend images provide a self-contained and easily interpretable summary of vegetation change at scales that are relevant for natural resource management (NRM) and environmental reporting.     

热带森林植被的动态变化遥感监测

本文讨论了以热带森林植被为主体的再生资源的面积动态变化监测。研究中包括两个部分。首先,我们利用多时相遥感图像对大面积的西双版纳州进行地类判读,系统地分析了森林植被的动态变化。其次,利用Landsat MSS和TM数据对自然保护区的动态变化进行了包含无监督分类和归一化差值植被指数分析的数字图像处理,变化分类也相当符合实际。总的实验结果表明,这种监测方法是很有效的,可在再生资源监测中特别是在森林植被监测中加以推广应用。     

Landsat 8 vs. Landsat 5: A comparison based on urban and peri-urban land cover mapping

An image dataset from the Landsat OLI spaceborne sensor is compared with the Landsat TM in order to evaluate the excellence of the new imagery in urban landcover classification. Widely known pixel-based and object-based image analysis methods have been implemented in this work like Maximum Likelihood, Support Vector Machine, k-Nearest Neighbor, Feature Analyst and Sub-pixel. Classification results from Landsat OLI provide more accurate results comparing to the Landsat TM. Object-based classifications produced a more uniform result, but suffer from the absorption of small rare classes into large homogenous areas, as a consequence of the segmentation, merging and the spatial parameters in the spatial resolution (30 m) of Landsat images. Based exclusively on the overall accuracy reports, the SVM pixel-based classification from Landsat 8 proved to be the most accurate for the purpose of mapping urban land cover, using medium spatial resolution imagery.     

Comparative evaluation of Indian remote sensing satellite and landsat thematic mapper data for geological and geomorphological applications

In order to evaluate the potentials of IRS‐1A Linear Imaging Self‐scanning Sensor (LISS‐I) data for geological and geomorphological applications and also to compare the IRS‐1A LISS‐I data with Landsat Thematic Mapper (TM) data, a study has been attempted for parts of Uttar Pradesh and Madhya Pradesh in Northern India. The first four spectral bands of Landsat TM sensor data which are similar and close to IRS‐1A LISS‐I senor have been utilised for the comparative evaluation. Various techniques employed for both the data set to derive the required geology and geomorphology related information include (i) band combination (ii) spectral response analysis (iii) principal component analysis (iv) supervised classification techniques and (v) visual observation of various outputs generated by the above methods. The Optimum Index Factor (OIF) method adopted for selecting suitable band combinations showed similar OIF rankings for IRS‐1A LISS‐I data and Landsat TM data. It has been visually observed that the band combination 1, 3 & 4 offers relatively better feature display. The spectral responses derived for various major geologic rock units such as Deccan Trap, Vindhyan Formation, Bundelkhand Granite and for a few landcovers such as surface water bodies and black soil show striking similarity in pattern for both LISS‐I and TM. The Principal Component (PC) analysis of both data sets suggested that the total scene brightness tends to dominate in the first PC. The percentage information contributed by PCs 1&2 as also by PCs 1,2 & 3 in both the LISS‐I and TM are comparable. It was observed from the classified image generated by performing supervised classification with a maximum likelihood algorithm that major geomorphic landforms were clearly distinguishable. Thus the qualitative and quantitative evaluation of both IRS‐1A LISS‐I and Landsat TM data showed that significant similarities exist between them. The study also revealed that IRS‐1A LISS‐I data can be effectively used for deriving geology and geomorphology related details.     

Land use change detection based on multi‐date imagery from different satellite sensor systems

An empirical study was performed assessing the accuracy of land use change detection when using satellite image data acquired ten years apart by sensors with differing spatial resolutions. Landsat/Multi‐spectral Scanner (MSS) with Landsat/Thematic Mapper (TM) or SPOT/High Resolution Visible (HRV) multi‐spectral (XS) data were used as a multi‐data pair for detecting land use change. The primary objectives of the study were to: (1) compare standard change detection methods (e.g. multi‐date ratioing and principal components analysis) applied to image data of varying spatial resolution; (2) assess whether to transform the raster grid of the higher resolution image data to that of the lower resolution raster grid or vice‐versa in the registration process: and (3) determine if Landsat/TM or SPOT/ HRV(XS) data provides more accurate detection of land use changes when registered to historical Landsat/MSS data.

Ratioing multi‐sensor, multi‐date satellite image data produced higher change detection accuracies than did principal components analysis and is useful as a land use change enhancement technique. Ratioing red and near infrared bands of a Landsat/MSS‐SPOT/HRV(XS) multi‐date pair produced substantially higher change detection accuracies (～10%) than ratioing similar bands of a Landsat/MSS ‐ Landsat/TM multi‐data pair. Using a higher‐resolution raster grid of 20 meters when registering Landsat/MSS and SPOTZHRV(XS) images produced a slightly higher change detection accuracy than when both images were registered to an 80 meter raster grid. Applying a “majority”; moving window filter whose size approximated a minimum mapping unit of 1 hectare increased change detection accuracies by 1–3% and reduced commission errors by 10–25%.     

利用Landsat 5 TM影像估算沉水植物地上生物量的研究——以江西省鄱阳湖国家自然保护区为例

阐述了利用Landsat 5 TM影像在鄱阳湖国家自然保护区进行沉水植物地上生物量估算的方法和过程。研究结果显示，采用该影像，结合传统的采样策略和估算方法进行生物量的估算，在此研究区域中具有一定的局限性。分析了产生这一问题的原因，并对后续的研究工作提出了具体的建议。     

Assessing the potential of integrated Landsat 8 thermal bands,with the traditional reflective bands and derived vegetation indices in classifying urban landscapes

Reliable and up-to-date urban land cover information is valuable in urban planning and policy development. Due to the increasing demand for reliable land cover information there has been a growing need for robust methods and datasets to improve the classification accuracy from remotely sensed imagery. This study sought to assess the potential of the newly launched Landsat 8 sensor’s thermal bands and derived vegetation indices in improving land cover classification in a complex urban landscape using the support vector machine classifier. This study compared the individual and combined performance of Landsat 8’s reflective, thermal bands and vegetation indices in classifying urban land use-land cover. The integration of Landsat 8 reflective bands, derived vegetation indices and thermal bands overall produced significantly higher accuracy classification results than using traditional bands as standalone (i.e. overall, user and producer accuracies). An overall accuracy above 89.33% and a kappa index of 0.86, significantly higher than the one obtained with the use of the traditional reflective bands as a standalone data-set and other analysis stages. On average, the results also indicate high producer and user accuracies (i.e. above 80%) for most of the classes with a McNemar’s Z score of 9.00 at 95% confidence interval showing significant improvement compared with classification using reflective bands as standalone. Overall, the results of this study indicate that the integration of the Landsat 8’s OLI and TIR data presents an invaluable potential for accurate and robust land cover classification in a complex urban landscape, especially in areas where the availability of high resolution datasets remains a challenge.     

作物种植成数的遥感监测精度评价

以河南开封和山西太谷地区作为研究区域 ,选用LandsatTM作为农作物种植面积遥感监测的数据源。利用LandsatTM提取河南开封实验区 2 0 0 1年的夏季作物和山西太谷地区 2 0 0 3年秋季作物的作物种植成数。同时 ,利用IKONOS ,QuickBird高分辨率遥感影像 ,通过地面调查进行了地面作物填图和分类 ,同样得到实验区的农作物种植成数。最后通过两种结果对比 ,表明开封实验区夏季作物的监测精度达到 99%以上 ,太谷实验区秋季作物的监测精度达到 97%以上 ,由此推断 ,表明利用LandsatTM监测农作物种植成数的精度能够满足中国农情遥感监测的运行化要求     

Improving crop type determination using satellite imagery: A study for the Regione del Veneto,Italy

The Regione del Veneto (Italy) is cooperating with the University of California, Santa Barbara and other researchers in Italy and the U.S.A. to develop a system of econometric crop production modeling. Five crops are to be included in this project: small grains (wheat and barley), corn, sugar beets, soybeans, orchards and vineyards. A critical part of the crop yield modeling process is the identification of crops using multispectral satellite data. This paper explores two strategies to improve crop classification accuracies: (1) use of ancillary data stored in digital format and (2) use of multitemporal data. Ancillary information stored on digital files were used in this research to remove (mask) non‐agricultural areas from satellite image data. Comparison between the classification of masked and unmasked images showed that improvement ranged from 3% to 26% depending on crop type. The multidate classification was performed by compiling an image of transformed spectral bands and three TM‐5 bands. The transformed bands were TM band 4 over TM band 3. Based on the work conducted in this study it is clear that crop type determination from satellite imagery is possible for small field agricultural areas such as those found in Italy.     

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.     

Predictive GIS model for potential mapping of Cu, Pb, Zn mineralization

IntroductionChina is the third richest country in the world ofmineral resources with a total potential value of 11trillion USdollars. Yunnan Provincce is one of theimportant metallogenic belts for nonferrous and pre-cious metal deposits[1].Mineral source …