Object Oriented Classification for Mapping Mixed and Pure Forest Stands Using Very-High Resolution Imagery

Abstract

The importance of mixed forests is increasingly recognized on a scientific level, due to their greater productivity and efficiency in resource use, compared to pure stands. However, a reliable quantification of the actual spatial extent of mixed stands on a fine spatial scale is still lacking. Indeed, classification and mapping of mixed populations, especially with semi-automatic procedures, has been a challenging issue up to date. The main objective of this study is to evaluate the potential of Object-Based Image Analysis (OBIA) and Very-High-Resolution imagery (VHR) to detect and map mixed forests of broadleaves and coniferous trees with a Minimum Mapping Unit (MMU) of 500 m2. This study evaluates segmentation-based classification paired with non-parametric method K- nearest-neighbors (K-NN), trained with a dataset independent from the validation one. The forest area mapped as mixed forest canopies in the study area amounts to 11%, with an overall accuracy being equal to 85% and K of 0.78. Better levels of user and producer accuracies (85–93%) are reached in conifer and broadleaved dominated stands. The study findings demonstrate that the very high resolution images (0.20 m of spatial resolutions) can be reliably used to detect the fine-grained pattern of rare mixed forests, thus supporting the monitoring and management of forest resources also on fine spatial scales.

Mapping Highland Barley on the Qinghai–Tibet Combing Landsat OLI Data and Object-Oriented Classification Method

“…The object-oriented sample information-based extraction method takes the object generated by segmenting the entire image as the research target, and the size of the object is determined by the image segmentation scale and spatial structure. When extracting information, not only the spectral feature difference of the image is considered, but also spatial features such as the texture and shape of the features in the image and the relationship structure information among various objects are considered [34]. The object-oriented sample information-based extraction method encompasses two main steps: (1) the "segmentation", which is the delineation of homogeneous objects from the input imagery, following the principle of clustering neighboring image pixels into "objects", so as to maximize the intra-object spectral homogeneity and inter-object spectral heterogeneity; (2) the "classification", which labels and assigns each polygon to the target cover class [35].…”

Monitoring Rock Desert Formation Caused by Ice–Snow Melting in the Qinghai-Tibet Plateau Using an Optimized Remote Sensing Technique: A Case Study of Yushu Prefecture

“…Therefore, the model based on multi-index principal component analysis can be used to estimate rock desert area coverage. Object-oriented classification is a processing method that integrates the structure, spectral features and geometric shape information of images [40]. This method takes the image objects generated after segmentation as the research objects and analyzes them by using the essential features of the image objects and topological relations between neighboring objects.…”

Extension of an Open GEOBIA Framework for Spatially Explicit Forest Stratification with Sentinel-2

“…Tuning the parameters of the region growing procedure often relies on testing different parameters through a trial-and-error approach. Results of different threshold combinations are then screened for their ability to delineate the target objects by a visual assessment [23,57]. Although most object-based approaches still rely on this visual assessment of the segmentation, and it was the only available method at the beginning of GEOBIA approaches [58], several automated methods to derive an indication of the best segmentation parameters do now exist.…”

“…The presented GEOBIA framework consists of an automatic procedure to delineate forest boundaries from color infrared (CIR) aerial imagery through segmentation that satisfies the geometric requirements of operational forest management within central European forests, which consist of a meaningful proportion of small-structured and fragmented units. An MMU of 0.05 ha has been set based on the lower boundary of the UNFCCC forest definition, [21] and is a common MMU for monitoring on a local forest scale [22,23]. While preserving this VHR geometry to reflect forest characteristics, Sentinel-2 provides the spectral and temporal information to provide timely updates on forest status.…”