Kappa Coefficient accuracy estimation for assessing the major components of Mudbank formation through the temporal scale (24 years) of the Subarnarekha river estuary zone, Odisha, India.

Abstract Remote sensing is an important tool and technique for the best assessment of LULC map preparation and satellite image classification. This study emphasizes the classification of LULC of the adjacent lower zone area of the Subarnarekha River. To complete this study, ten such parameters have been considered, like Water-Body, Vegetation, Settlement, agricultural land area, Point Bar, Sand Bar, Sand Bank, Sand Dunes, Fishery Zone, and Mud-Bank Area at estuarine part of this river. Availability of Landsat images six specific years are sampled like 1998, 2004,2009,2014,2018 and 2022 respectively. The several factors have been consider including availability of quality Landsat imagery data through precise classification steps and users experience and expertise of the procedures. The objective of this study has been completed using the geospatial techniques like RS and GIS applications, which have compiled distinct two sections. First phase is containing Land-use and Land-cover (LULC) classification and, second phase is containing accuracy assessment of considered parameters. The Nonparametric Kappa coefficient Khat Statistic rule has applied for esteemed supervised classification with Kappa coefficient scale. The study had an overall classification accuracy of 86.75% and Kappa coefficient (K) of 0.911, 0.908, 0.719, 0.803, 0.858, and 0.886 following the considered study year. Overall accuracy through Khat Statistic reveals that vegetation, settlement, agricultural land, fishery and mud-bank are dominant parameters for the considered random samples. The revealed results are considerable and helpful for sustainable plan in future for this area.遥感是开展土地利用与土地覆被（Land-use and Land-cover, LULC）图编制与卫星影像分类的重要工具与技术手段。本研究聚焦苏巴纳雷卡河下游毗邻区域的土地利用与土地覆被分类工作。本次研究共选取10类地表参数，分别为水体、植被、建成区、农用地、点沙坝、沙坝、沙岸、沙丘、渔业区以及该河河口区域的泥滩。研究选取了1998、2004、2009、2014、2018及2022年共6个年份的Landsat影像作为数据源。研究过程综合考量了高质量Landsat影像的可获取性、精准的分类流程以及操作人员的专业经验与技术熟练度。本研究依托遥感（Remote Sensing, RS）与地理信息系统（Geographic Information System, GIS）等地理空间技术，将研究内容划分为两个阶段：第一阶段为土地利用与土地覆被分类，第二阶段为所选参数的精度评估。针对监督分类结果，采用非参数Kappa系数（Khat）统计量法则，并依据Kappa系数分级标准开展精度评定。各研究年份的总体分类精度达86.75%，对应的Kappa系数依次为0.911、0.908、0.719、0.803、0.858及0.886。基于Khat统计量得到的总体精度结果显示，植被、建成区、农用地、渔业区及泥滩为本次研究抽样区域的优势地表覆被类型。本研究所得结果具有较高参考价值，可为该区域未来的可持续规划提供有力支撑。