Abstract
High-precision information regarding the location, time, and type of land use change is integral to understanding global changes. Time series (TS) analysis of remote sensing images is a powerful method for land use change detection. To address the complexity of sample selection and the salt-and-pepper noise of pixels, we propose a bidirectional segmented detection (BSD) method based on object-level, multivariate TS, that detects the type and time of land use change from Landsat images. In the proposed method, based on the multiresolution segmentation of objects, three dimensions of object-level TS are constructed using the median of the following indices: the normalized difference vegetation index (NDVI), the normalized difference built index (NDBI), and the modified normalized difference water index (MNDWI). Then, BSD with forward and backward detection is performed on the segmented objects to identify the types and times of land use change. Experimental results indicate that the proposed BSD method effectively detects the type and time of land use change with an overall accuracy of 90.49% and a Kappa coefficient of 0.86. It was also observed that the median value of a segmented object is more representative than the commonly used mean value. In addition, compared with traditional methods such as LandTrendr, the proposed method is competitive in terms of time efficiency and accuracy. Thus, the BSD method can promote efficient and accurate land use change detection.
Highlights
Land use patterns have changed significantly in recent decades owing to global changes [1,2]
Object-level time series (TS) of remote sensing images—another type of analysis method—treats each homogeneous object generated via image segmentation as a whole; this better addresses the aforementioned limitations of pixel-level TS methods [28,29]
We proposed a method based on the analysis of object-level multivariate TS for remote sensing detection in the context of land use change
Summary
Land use patterns have changed significantly in recent decades owing to global changes [1,2]. Many of the aforementioned methods use a single index to measure the similarity of TS, which highly depends on the accuracy of the selected sample points, and cannot detect the change time. Detecting land use changes based on pixel-level TS has stringent requirements for the registration accuracy and radiation correction of the remote sensing images. Object-level TS of remote sensing images—another type of analysis method—treats each homogeneous object generated via image segmentation as a whole; this better addresses the aforementioned limitations of pixel-level TS methods [28,29]. Detection methods based on incomplete samples cannot accurately identify the type and time of complex land use change [38,39]. Wuseemataitnhleyssaomlveettihmeepwroibthleomutoufsdinetgecthtiengchtahnegcehdansgaemtpimlees.aTnhdecmhaaningecotynptreibofultaionndsuosfetahtisthsetusdamy aerteim(1e) wusitehofuthuesimngedthiaenchinasntgeaed soafmthpelems.eTahneams athine croenptrreisbeunttiaotnivseofetahtiusrsetuodf yobarjeec(t1s)aunsde o(2f)thcoemmbeidnianng ifnosrtweaadrdoaf nthdebmacekawn arsdthdeetrecptrieosnenptraotcivesesfeesatourdeeotefcotbdjeifcftesraentdt(y2p)ecsoamnbdintimngesfoorfwlanrdd aunsde cbhaacnkwgea.rd detection processes to detect different types and times of land use change
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