The standard kappa index of agreement is usually not appro priate for map comparison. Open source r for applying machine learning to rpas remote. Correct formulation of the kappa coefficient of agreement. Volume 14 issue 3 journal of applied remote sensing. Assessing the classification accuracy of multisource remote. According to pontius 2011, kappa has not provided the useful information that it is supposed to bring. It is generally thought to be a more robust measure than simple percent agreement calculation, as. As an operational tool, geocbi visually assesses the magnitude of change by. Explaining the unsuitability of the kappa coefficient in. Chance agreement is, however, irrelevant in an accuracy assessment and is anyway inappropriately modelled in the calculation of a kappa coefficient for typical remote sensing applications. History of remote sensing the knowledge about the history of remote sensing is necessary for better understanding of the subject and its scope, and also for future development, particularly for the welfare of human society. Remote sensing and geographical information system gis. In remote sensing literature, there are two main classification approaches, pixelbased and objectbased. Imagery collected from remote sensing platforms is commonly classified using conventional remote sensing techniques supplied by available software in the market.
What is your impression of the value of the kappa statistic. The potential user of the kappa coefficient of agreement is cautioned that a number of remote sensing articles contain er rors in the formula for the kappa statistic or its variance. The pixelbased methods can be divided into unsupervised and. In order to improve the change detection accuracy of multitemporal high spatial resolution remotesensing hsrrs images, a change detection method of multitemporal remotesensing images based on saliency detection and spatial intuitionistic fuzzy cmeans sifcm clustering is proposed. Pete, the use of the kappa metric is currently discouraged in the remote sensing community. How are kappa and overall accuracy related with respect to. For these reasons, we believe that tau is a better measurc of cassification accuracv for use with remote sensing data than either kappa or percentage agreement. A kappa of 1 indicates perfect agreement, whereas a kappa of 0 indicates agreement equivalent to chance. Remote sensing technologies and gis tools for the diagnosis and preservation of cultural heritage uav data for the mapping of erosion and landslide processes papers incorporating novel and interesting techniques in studying these aspects, as well as some interesting applications, will be considered. This article concludes that these kappa indices are useless, misleading andor flawed for the practical applications in remote sensing that we. Accuracy assessment of land useland cover classification.
Congalton for suggesting the correction for locational uncertainty. Decision tree classification of remotely sensed satellite. Of these, the normalized difference vegetation index ndvi is the most widely used. Aerial and landsat satellite images are also frequently used to evaluate land cover distribu. Some studies have applied the landsat threshold method to estimate cropping intensity using seasonspeci. The journal of applied remote sensing jars is an online journal that optimizes the communication of concepts, information, and progress within the remote sensing community to improve the societal benefit for monitoring and management of natural disasters, weather forecasting, agricultural and urban landuse planning, environmental quality monitoring, ecological restoration, and numerous. Tau coefficients for accuracy assessment of classification of. What is kappa coefficient, and how it can be calculated. A limitation of kappa is that it is affected by the prevalence of the finding under observation. Principles of remote sensing shefali aggarwal photogrammetry and remote sensing division indian institute of remote sensing, dehra dun abstract. Kappa values range from 0 to 1, though they can be negative and range from 1 to 1. Remote sensing of environment university of oklahoma. Correct formulation of the kappa coefficient of agreement asprs. Explaining the unsuitability of the kappa coefficient in the.
In order to apply observation data to these fields, it is necessary to conduct a wide range of research activities ranging from fundamental research on the observation of target objects to applied research corresponding to application needs, including combination with other information sources. This tool provides a means to estimate the sample size required to achieve a confidence level and precision for statistical analysis. Remote sensing is the process of acquiring datainformation about. This investigation provides a methodology for mapping mangrove forests through remote sensing images in a semidetail scale 1. Remote sensing of the earth from orbital altitudes was recognized in the mid1960s as a potential technique for obtaining information important for the effective use and conservation of natural resources. The development of remote sensing over time can be broadly divided into following six phases. Pdf accuracy assessment of land use land cover in umabdalla. Remote sensing quantifies widespread abundance of permafrost. Data interpretation of remote sensing data is a major task for earth observation, and the accurate identification and localization of buildings is essential for building analysis, planning, and urban growth monitoring. The development of veryhighresolution vhr optical images has paved a path to study building properties on a large scale. Center for remote sensing and department of forestry, michigan state university, east lansing. Cohens kappa can be easily calculated using a formula and the number of true positive, false positive, false negative and true positive cases from the confusion matrix.
To obtain a highaccuracy vegetation classification of highresolution uav images, in this paper, a multiangle hyperspectral remote sensing system was built using a sixrotor uav and a cubert s185 frame hyperspectral sensor. The field of remote sensing applications is extremely broad. Metrics such as kappa coefficient have been demonstrated to provide. Identifying each random points value from aerial imagery. Kappa statistic as well as several other metrics including overall. Remote sensing is one of the tool which is very important for the production. Indices and band ratios are the most common form of spectral enhancement. Process of remote sensing pdf because of the extreme importance of remote sensing as a data input to gis, it has.
Spatial scale of remote sensing instrument does not match classification scheme. Although we will focus on ndvi in the section, there are indices and band ratios to support a broad range of applications, from minerals to soil to vegetation. We detected 643,304 lakes with a size larger than 1 ha, with a total area of 118,182 km 2 in 2014 or 5. Remote sensing measurements represented as a series of digital numbers the larger this number, the higher the radiometric resolution, and the sharper the imagery spectral bands and resolution for various sensors cimss. However, since there should be a positive correlation between the remotely sensed classification and the reference data, positive kappa values are expected. More recently, olofsson, foody, herold, stehman, woodcock and wulder 2014, remote sensing of environment also advocated against kappa. Research and development remote sensing technology center. The kappa statistic or kappa coefficient is the most commonly used statistic for this purpose. Lees classification accuracy has traditionally been expressed by the overall accuracy percentage computed from the sum of the diagonal elements of the error, confusion, or misclassification matrix resulting from the application of a classifier. The application of uavbased multiangle remote sensing in fine vegetation classification. The experiments demonstrate that the proposed dhff method achieves significant improvement for change detection in heterogeneous optical and sar remote sensing images, in terms of both accuracy rate and kappa index. Status of land cover classification accuracy assessment ucl.
Accuracy assessment goals portland state university. Mangrove forest mapping through remote sensing imagery. This is in part because the promoted standard methods such as the kappa coefficient are not always. The kappa coefficient was introduced to the remote sensing community in the early. Accuracy assessment of land useland cover classification using remote sensing and gis. Data interpretation of remotesensing data is a major task for earth observation, and the accurate identification and localization of buildings is essential for building analysis, planning, and urban growth monitoring. Available bands of sensors are linked with required wavelenghts of indices, so that one can get all sensors usable for calculating an index and vice versa one can find all indices that can be calculated by data from a specific sensor. Quantification error versus location error in comparison of. A perfect classification would produce a kappa value of one. The studies began when the tiros satellites 1960 provided mans first synoptic view of the earths weather systems.
Root system estimation based on satellite remote sensing. Al though the erratum plotogranllnetric engineering and remote sensillg, vol. Integration of poverty and remote sensing dataremote. The moderate resolution imaging spectroradiometer modis instrument aboard the terra and aqua satellite with its neardaily global coverage allows dense timeseries that are well suited and extremely useful for crop mapping at resolutions of 250 m and above wardlow et al. Real remote sensing images acquired by sar and optical satellites are utilized to evaluate the performance of the proposed method. Cohens kappa when two binary variables are attempts by two individuals to measure the same thing, you can use cohens kappa often simply called kappa as a measure of agreement between the two individuals.
Download limit exceeded you have exceeded your daily download allowance. I do not agree on the fact that kappa is largely considered to be more robust than oa. Once remote sensing data have been collected, the user must interpret the data to derive the information needed to. The kappa coefficient is rated as substantial and hence the classified image found to be fit for further research.
Summary of kappa k statistics of land use land cover classes in unrf. Article good practices for assessing accuracy and estimating area of. Change detection in heterogeneous optical and sar remote. Application g the final element of the remote sensing process is. Apr 08, 2020 real remote sensing images acquired by sar and optical satellites are utilized to evaluate the performance of the proposed method.
Although we will focus on ndvi in the section, there are indices and band ratios to support a broad range of. Urban remote sensing lidar data hyperspectral imagery discriminant analysis watershed segmentation in this study we fused highspatial resolution 3. Integration of poverty and remote sensing dataremote sensing data. Remote sensing can be defined as any process whereby information is. Volume 14 issue 1 journal of applied remote sensing. As a result, a land use map was generated from the best classifier, according to the kappa index, providing scientifically relevant information such as the area of each land use class. Chance agreement is, however, irrelevant in an accuracy assessment and is anyway inappropriately modelled in the calculation of. Firstly, the clusterbased saliency cue method is used to obtain the saliency maps of two temporal remote. Remote sensing is a technique to observe the earth surface or the atmosphere from out of space using satellites space borne or from the air using aircrafts airborne. The kappa coefficient is not an index of accuracy, indeed it is not an index of overall agreement but one of agreement beyond chance. This tool allows you to compare the kappa statistics between different analyses, perhaps comparing different observers, predictive algorithms or dates of remote sensing imagery. Remote sensing is the main source for several kinds of thematic data critical to gis analyses, including data on landuse and landcover characteristics. This guideline explores some of the basic analysis options for agricultural applications of remote sensing data. Change detection in multitemporal high spatial resolution.