The role of topographic correction in mapping recently burned Mediterranean forest areas from LANDSAT TM images

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 of burned and non‐burned areas. Frequently, this occurs as a result of slope illumination and shadowing effects caused by the complex topography encountered in many forested areas. Cloud shadows can also be a problem. The aim of this work was to investigate how image classification results could be improved by removing the illumination effects of topography from satellite images. This was achieved by applying supervised classification to both uncorrected and topographically corrected LANDSAT TM data for a site on the Greek island of Thasos. The classification methodology included atmospheric and geometric correction, field‐based training, seperability/contingency analysis and maximum likelihood processing. The classification scheme was determined on the basis of consultation with the Greek Forest Service. Overlay of the resulting class maps enabled comparison of the total burned area and its spatial extent using the two different approaches to processing. The results of each approach were compared with the forest perimeter map generated by the Forest Service using traditional survey methods. Accuracy assessment and error analysis clearly indicated that the removal of the topographic effect from the satellite image before its classification resulted in more accurate mapping of the burned area. It is concluded that operational use of satellite remote sensing for forest fire management depends on accurate, robust, widely available and proven techniques. Topographic correction should now be regarded as an essential element of any classification methodology which will be used for operational, post‐fire management of forests in complex Mediterranean landscapes.     

Object-based image classification for burned area mapping of Creus Cape, Spain, using NOAA-AVHRR imagery

Due to the ability of the NOAA-AVHRR sensor to cover a wide area and its high temporal frequency, it is possible to quickly obtain a general overview of the prevailing situation over a large area of terrain and, more specifically, quickly assess the damage caused by a recent large forest fire by mapping the extent of the burned area. The aim of this work was to map a large forest fire that recently took place on the Spanish Mediterranean coast using innovative image classification techniques and low spatial resolution imagery. The methodology involved developing an object-based classification model using spectral as well as contextual object information. The burned area map resulting from the image classification was compared with the fire perimeter provided by the Catalan Environmental Department in terms of spatial overlap and size in order to determine to what extent they were compatible. Results of the comparison indicated a high degree (≈90%) of spatial agreement. The total burned area of the classified image was found to be 6900 ha, compared to a fire perimeter of 6000 ha produced by the Catalan Environmental Department. It was concluded that, although the object-oriented classification approach was capable of affording very promising results when mapping a recent burn on the Spanish Mediterranean coast, the method in question required further assessment to ascertain its ability to map other burned areas in the Mediterranean.     

The development of an operational procedure for burned-area mapping using object-based classification and ASTER imagery

Although burned-area mapping at a regional level is traditionally based on the use of Landsat data, the potential gap in the sensor's data collection emphasizes the need to find alternative data sources to be used in the operational mapping of burned areas. This work aims to investigate whether it is possible to develop a transferable object-based classification model for burned-area mapping using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. The initial step in the investigation involved the development of an object-based classification model for accurately mapping burned areas in central Portugal using an ASTER image, and subsequently an examination of its performance when mapping a burned area located on the island of Rhodes, Greece, using a different ASTER image. Results indicate that the combined use of object-based image analysis and ASTER imagery can provide an alternative operational tool that could be used to identify and map burned areas and thus fill a potential gap in Landsat data collection.     

Mapping burned areas in Mediterranean countries using spectral mixture analysis from a uni‐temporal perspective

The main aim of this study was to evaluate the usefulness of spectral mixture analysis (SMA) for mapping forest areas burned by fires in the Mediterranean area using low and medium spatial resolution satellite sensor data. A methodology requiring only one single post‐fire image was used to carry out the study (uni‐temporal techniques). This methodology is based on the contextual classification of the fraction images obtained after applying SMA to the original post‐fire image. The results showed that the proposed method, using only one image acquired post‐fire, could accurately identify the burned surface area (Kappa coefficient>0.8). The spatial resolution of the satellite images had practically no influence on the accuracy of the burned area estimate but did affect the possibility of detecting areas inside the perimeter of the burned area which were only slightly damaged.     

Agreement assessment of NOAA/AVHRR NDVI with Landsat TM NDVI for mapping burned forested areas

Meteorological satellites are appropriate for operational applications related to early warning, monitoring and damage assessment of forest fires. Environmental or resources satellites, with better spatial resolution than meteorological satellites, enable the delineation of the affected areas with a higher degree of accuracy. In this study, the agreement of two datasets, coming from National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) and Landsat TM, for the assessment of the burned area, was investigated. The study area comprises a forested area, burned during the forest fire of 21-24 July 1995 in Penteli, Attiki, Greece. Based on a colour composite image of Landsat TM a reference map of the burned area was produced. The scatterplot of the multitemporal Normalized Difference Vegetation Index (NDVI) images, from both Landsat TM and NOAA/AVHRR sensors, was used to detect the spectral changes due to the removal of vegetation. The extracted burned area was compared to the digitized reference map. The synthesis of the maps was carried out using overlay techniques in a Geographic Information System (GIS). It is illustrated that the NOAA/AVHRR NDVI accuracy is comparable to that from Landsat TM data. As a result NOAA/AVHRR data can, operationally, be used for mapping the extent of the burned areas.     

Comparing ten classification methods for burned area mapping in a Mediterranean environment using Landsat TM satellite data

Various methods have been developed during the past three decades to improve the classification accuracy in burned area mapping using satellite data captured by different sensors. In this article, we compare ten such classification approaches using Landsat Thematic Mapper (TM) imagery on three Mediterranean test sites by evaluating the classification accuracy using (i) a traditional pixel-based approach, (ii) the concept of the Pareto boundary of efficient solution and (iii) linear regression analysis. Additionally, we make a discrimination of errors depending on their distribution and causal factor. The classification approaches compared resulted in not statistically significant differences in the accuracy of the burned area maps. Differences between the methods were also observed when considering the accuracy along the edges of the burned patches; however, again these were not statistically significant. The findings of our study in a Mediterranean environment clearly demonstrate that, for the selection of the most suitable classification approach, other factors could be given more weight, such as computational resources, imagery characteristics, availability of ancillary data, available software and the analyst's experience. Maybe the most important finding of our work is that the variance imposed by the methods is less than the variance imposed by factors differentiated locally in the three study sites since the between-group variance of the overall accuracy is higher than that of the within groups.     

Burned area mapping using logistic regression modeling of a single post-fire Landsat-5 Thematic Mapper image

Logistic regression modeling was applied, as an alternative classification procedure, to a single post-fire Landsat-5 Thematic Mapper image for burned land mapping. The nature of the classification problem in this case allowed the structure and application of logistic regression models, since the dependent variable could be expressed in a dichotomous way. The two logistic regression models consisted of the TM 4, TM 7, TM 1 and TM 4, TM 7, TM 2 presented an overall accuracy of 97.37% and 97.30%, respectively and proved to be the most well performing three-channel color composites. The discriminator ability in respect to burned area mapping of each one of the six spectral channels of Thematic Mapper, which was achieved by applying six logistic regression models, agreed with the results taken from the separability indices Jeffries-Matusita and Transformed Divergence.     

Logistic regression modelling of multitemporal Thematic Mapper data for burned area mapping

This study focused on the development of a logistic regression model for burned area mapping using two Landsat-5 Thematic Mapper (TM) images. Logistic regression models were structured using the spectral channels of the two images as explanatory variables. The overall accuracy of the results and other statistical indications denote that logisticregression modelling can be usedsuccessfully for burned area mapping. The model that consisted of the spectral channels TM4, TM7 and TM1 and had an overall accuracy of 97.62%, proved to be the most suitable. Moreover, the study concluded that the spectral channel TM4 was the most sensitive to alterations of the spectral response of the burned category pixels, followed by TM7.     

Development of an IKONOS image classification rule-set for multi-scale mapping of Mediterranean rural landscapes

In this research, a rule-set of object-based classification of IKONOS imagery for fine-scale mapping of Mediterranean rural landscapes was developed. This study was conducted on the Mediterranean island of Crete (Greece). A three-level classification hierarchy was designed in a bottom-up approach containing a total number of 22 classes. The first level was associated with vegetation physiognomy (6 classes), the second level with linear features (6 classes) and the third level with land uses existing in the area (10 classes). Image objects were created with multiresolution segmentation, an algorithm supplied by eCognition software. The segmentation parameters were selected through a trial-and-error approach after visual evaluation of the resulting image objects. The rule-set comprised 100 classification rules described with the ‘Membership Function’ classifier. The classification stability was found to lie between 0.59 and 0.77, inversely proportional to the complexity of each level's classification. For an accuracy assessment, the error matrix method was used in a set of 250 randomly selected points. The overall classification accuracy achieved at the first level was 74%, at the second level 50% and at the third level 64%. The geometric accuracy of the classification was beyond the scope of this research; and moreover, consistent reference data sets were not available. The conclusion is that the use of rules in an object-based image analysis (OBIA) process has the potential to produce accurate landscape maps even in the case of complex environments, in which ancillary data are not available. Future work should focus on testing the transferability of the rule-set in different Mediterranean study sites, in order to draw a conclusion in relation to its potential operational use.     

Utilizing image texture to detect land-cover change in Mediterranean coastal wetlands

Land-use/cover change dynamics were investigated in a Mediterranean coastal wetland. Change Vector Analysis (CVA) without and with image texture derived from the co-occurrence matrix and variogram were evaluated for detecting land-use/cover change. Three Landsat Thematic Mapper (TM) scenes recorded on July 1985, 1993 and 2005 were used, minimizing change detection error caused by seasonal differences. Images were geometrically, atmospherically and radiometrically corrected. CVA without and with texture measures were implemented and assessed using reference images generated by object-based supervised classification. These outputs were used for cross-classification to determine the ‘from–to’ change used to compare between techniques. The Landsat TM image bands together with the variogram yielded the most accurate change detection results, with Kappa statistics of 0.7619 and 0.7637 for the 1985–1993 and 1993–2005 image pairs, respectively.     

Study on Mangrove Species Classification based on ZY-3 Image

A hybrid mangrove forest extraction and species classification model for large coastal region was proposed using a ZY-3 (ZiYuan-3) image in the eastern part of Beibu Gulf (located at the junction of Guangdong and Guangxi).Firstly,the coastline was extracted according to the spectral features of ZY-3 image.Secondly,the buffer zone along with the coastline was generated as the suitable area of mangrove distribution.Mangrove forests and non-mangrove areas were then further classified using threshold method based on object-based image classification in these areas.Finally,Mangrove forests were classified at specie level using three pixel-based supervised classification methods,k-Nearest Neighbor,Bayes,and Random Forest.The classification results and accuracies were also compared and discussed.The results indicated that object-based threshold method can extract the coastline accurately and map the mangrove forests effectively.The pixel-based random forest classifier can obtain satisfactory results (the overall accuracy of 82.24%) of mangrove species classification than the other classifiers.In summary,the hybrid mode proposed in this paper is suitable for mangrove forests mapping and species classification in a large region.It is also validated the feasibility application of ZY-3 image in coastal mangrove research.     

Semi-automated object-based landform classification modelling in a part of the Deccan Plateau of central India

Landform mapping holds significance in governing boundary conditions for the underlying processes operative in the fields of natural resource management, yet the automation in recognizing landform occurrence remains difficult. Geospatial object-based image analysis (GEOBIA) technique has evolved as a promising tool for addressing the issue. Majority of the GEOBIA-based landform classification studies document generic approach. The present study undertaken in Katol Tehsil of Nagpur District, a part of Deccan Plateau of central India aims at knowledge-based modelling through a multi-scale mapping workflow comprising multi-resolution segmentation (input raster dataset of IRS-P6 LISS-IV image and Cartosat-1 digital terrain model), knowledge-based classification, and accuracy assessment against a reference landform map. Contour- and drainage-based relative topographic position zone is derived in a novel attempt. Finally, knowledge-based rules are framed using the primary terrain parameters of elevation, slope, profile curvature, and drainage for deriving final output. The results of landform classification indicate the dominance of erosive landform over depositional one; maximum area of 6244 ha being under pediment. An accuracy assessment exercise is carried out in a watershed occurring in the study area, which shows very good statistical agreements between modelled and reference landforms including partial detection. The key constraint of this knowledge-based modelling is its limited adaptability to only localized conditions. However, such kind of object-based and knowledge-based analyses have immense potential with the increasing availability of finer resolution remote-sensing data products that demand the alternative paths of deriving objects that are made up of several pixels.     

Object-based classification using SPOT-5 imagery for Moso bamboo forest mapping

This study proposed a multi-scale, object-based classification analysis of SPOT-5 imagery to map Moso bamboo forest. A three-level hierarchical network of image objects was developed through multi-scale segmentation. By combining spectral and textural properties, both the classification tree and nearest neighbour classifiers were used to classify the image objects at Level 2 in the three-level object hierarchy. The feature selection results showed that most of the object features were related to the spectral properties for both the classification tree and nearest neighbour classifiers. Contextual information characterized by the composition of classified image objects using the class-related features assisted the detection of shadow areas at Levels 1 and 3. Better classification results were achieved using the nearest neighbour algorithm, with both the producer’s and user’s accuracy higher than 90% for Moso bamboo and an overall accuracy of over 85%. The object-based approach toward incorporating textural and contextual information in classification sequence at various scales shows promise in the analysis of forest ecosystems of a complex nature.     

Application of spectral mixture analysis to Amazonian land-use and land-cover classification

Abundant vegetation species and associated complex forest stand structures in moist tropical regions often create difficulties in accurately classifying land-use and land-cover (LULC) features. This paper examines the value of spectral mixture analysis (SMA) using Landsat Thematic Mapper (TM) data for improving LULC classification accuracy in a moist tropical area in Rondônia, Brazil. Different routines, such as constrained and unconstrained least-squares solutions, different numbers of endmembers, and minimum noise fraction transformation, were examined while implementing the SMA approach. A maximum likelihood classifier was also used to classify fraction images into seven LULC classes: mature forest, intermediate secondary succession, initial secondary succession, pasture, agricultural land, water, and bare land. The results of this study indicate that reducing correlation between image bands and using four endmembers improve classification accuracy. The overall classification accuracy was 86.6% for the seven LULC classes using the best SMA processing routine, which represents very good results for such a complex environment. The overall classification accuracy using a maximum likelihood approach was 81.4%. Another finding is that use of constrained or unconstrained solutions for unmixing the atmospherically corrected or raw Landsat TM images does not have significant influence on LULC classification performances when image endmembers are used in a SMA approach.     

Detection of annual burned forest area using change metrics constructed from MODIS data in Manitoba,Canada

The monitoring of annual burned forest area is commonly used to evaluate forest fire carbon release and forest recovery and can provide information on the evolution of carbon sources and sinks. In this work, a new method for mapping annual burned area using four types of change metrics constructed from Moderate Resolution Imaging Spectroradiometer (MODIS) data for Manitoba, Canada, was developed for the 2003–2007 period. The proposed method included the following steps: (1) four types of change metrics constructed from MODIS composite data; (2) Stochastic Gradient Boosting algorithm; and (3) two thresholds to ascertain the final burned area map. Fire-event records from the Canadian National Fire Database (CNFDB) for Manitoba were used to train and validate the proposed algorithm. The predicted burned area was within 91.8% of the CNFDB results for all of the study years. The results indicate that the presented metrics could retain spectral information necessary to discriminate between burned and unburned forests while reducing the effects of clouds and other noise typically present in single-date imagery. A visual comparison to Thematic Mapper (TM) images further revealed that in some areas the mapping provided improvement to the CNFDB data set.     

Geo-referencing of continental-scale JERS-1 SAR mosaics based on matching homologous features with a digital elevation model: theory and practice

An effective method for a posteriori ortho-rectification of continental-scale synthetic aperture radar (SAR) mosaics using a digital elevation model (DEM) has been developed. The method is based on homologous feature matching between the DEM and a simulated SAR image. The simulated image is derived from the radar-viewing geometry, topographic information and contextual information provided by the Shuttle Radar Topography Mission (SRTM), shorelines and water bodies database (SWBD) and GeoCover Landsat mosaics. Two large L-band SAR mosaics (the global boreal forest mapping (GBFM) Siberia mosaic and the global rain forest mapping (GRFM) Africa mosaic), assembled from the Japanese Earth Resources Satellite-1 (JERS-1) data, were accurately geo-referenced and ortho-rectified. The GRFM Africa mosaic was also radiometrically corrected for topographic effects. The accurate co-registration with the DEM allows for improved classification methods based on the combination of SAR backscatter with terrain features. Comparison of the revised GBFM and GRFM mosaics with a forthcoming set of continental-scale mosaics assembled from the Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) data will offer a unique possibility for change detection studies over the Tropical and Boreal forest zones with a temporal spacing of some 10 years.     

不同遥感指数提取林火迹地研究

森林火灾是一种重要的森林扰动,是导致森林碳储量减少的重要途径之一,及时、准确地获取林火迹地对于研究全球碳循环及气候变化具有重要意义。以江西省武宁县为研究区,选用1991年和1992年两期Landsat TM遥感影像,采用区分指数(M),分析了Landsat TM 7个波段、植被指数、火烧迹地指数及其改进指数对林火迹地的提取能力,并评价其分类精度。结果表明:TM4 、TM7和TM6波段区分度较高,能较好地反映林火迹地的特征;18个不同的遥感指数中,NBR、NSTV1、NSTV2和NSEV1区分度大于1.5,林火迹地提取精度达到85%以上,具有较高的林火迹地提取能力,EVI、BAI、MIRBI、SAVIT和NSEV2指数提取能力相对较弱;而以引入TM6波段的改进指数NSTV2提取能力最高,适当的方式引入热红外波段可改进遥感指数对林火迹地的提取能力。     

森林叶面积指数遥感反演模型构建及区域估算

基于eCognition面向对象分类算法及校正后的TM遥感影像,获取研究区2010年土地利用/覆被数据。同时在ArcGIS平台下,提取遥感影像6个波段反射率及RVI、NDVI、SLAVI、EVI、VII、MSR、NDVIc、BI、GVI和WI等10个植被指数,并辅助于DEM、ASPECT、SLOPE等地形信息,在与植物冠层分析仪(TRAC)实测各森林类型叶面积指数相关性分析的基础上,研究表明:相对多元线性回归方法,偏最小二乘法能够更好地把握各森林类型LAI动态变化,而后结合研究区森林覆被信息进行区域估算。     

Segmentation optimization and stratified object-based analysis for semi-automated geomorphological mapping

Semi-automated geomorphological mapping techniques are gradually replacing classical techniques due to increasing availability of high-quality digital topographic data. In order to efficiently analyze such large amounts of data, there is a need for optimizing the processing of automated mapping techniques. In this context, we present a novel approach to semi-automatically map alpine geomorphology using stratified object-based image analysis. We used a 1 m Digital Terrain Model (DTM) derived from laser altimetry data from a mountainous catchment from which we calculated various Land-Surface Parameters (LSPs). The LSPs ‘slope angle’ and ‘topographic openness’ have been combined into a single composite layer for selecting reference material and delineating training samples. We developed a novel method to semi-automatically assess segmentation results by comparing 2D frequency distribution matrices of training samples and image objects. The segmentation accuracy assessment allowed us to automate optimization of the scale parameter and LSPs used for segmentation. We concluded that different geomorphological feature types have different sets of optimal segmentation parameters. The feature-dependent parameters were used in a new approach of stratified feature extraction for classifying karst, glacial, fluvial and denudational landforms. In this way, we have used stratified object-based image analysis to semi-automatically extract contrasting geomorphological features from high-resolution digital terrain data. A further step would be to also automate the optimization of classification rules. We would then be able to create a library of feature characteristics that could be transferred and applied to other mountain regions and further automate geomorphological mapping strategies.     

森林过火面积的遥感测算方法

根据对近年来多次特大森林火灾和相应的气象卫星资料的分析,提出利用NOAA/AVHRR数据测算森林大火的过火面积的四种方法,即灰度修正像元法、植被修正像元法、坐标法和蔓延法。在GIS地面信息数据库支持下,利用这4种方法能准确、快速地计算出过火面积。经今春应急评估试运行验证,森林大火过火面积测算精度达90%。