Given the advances in remotely sensed imagery and associated technologies, several global land cover maps have been produced in recent times including IGBP DISCover, UMD Land Cover, Global Land Cover 2000 and GlobCover 2009. However, the utility of these maps for specific applications has often been hampered due to considerable amounts of uncertainties and inconsistencies. A thorough review of these global land cover projects including evaluating the sources of error and uncertainty is prudent and enlightening. Therefore, this paper describes our work in which we compared, summarized and conducted an uncertainty analysis of the four global land cover mapping projects using an error budget approach. The results showed that the classification scheme and the validation methodology had the highest error contribution and implementation priority. A comparison of the classification schemes showed that there are many inconsistencies between the definitions of the map classes. This is especially true for the mixed type classes for which thresholds vary for the attributes/discriminators used in the classification process. Examination of these four global mapping projects provided quite a few important lessons for the future global mapping projects including the need for clear and uniform definitions of the classification scheme and an efficient, practical, and valid design of the accuracy assessment.

The development of unmanned aerial systems (UAS) equipped with various sensors (e.g., Lidar, multispectral sensors, and/or cameras) has provided the capability to “see” the individual trees in a forest. Individual tree crowns (ITCs) are the building blocks of precision forestry, because this knowledge allows users to analyze, model and manage the forest at the individual tree level by combing multiple data sources (e.g., remote sensing data and field surveys). Trees in the forest compete with other vegetation, especially neighboring trees, for limited resources to grow into the available horizontal and vertical space. Based on this assumption, this research developed a new region growing method that began with treetops as the initial seeds, and then segmented the ITCs, considering its growth space between the tree and its neighbors. The growth space was allocated by Euclidian distance and adjusted based on the crown size. Results showed that the over-segmentation accuracy (Oa), under-segmentation (Ua), and quality rate (QR) reached 0.784, 0.766, and 0.382, respectively, if the treetops were detected from a variable window filter based on an allometric equation for crown width. The Oa, Ua, and QR increased to 0.811, 0.853, and 0.296, respectively, when the treetops were manually adjusted. Treetop detection accuracy has a great impact on ITCs delineation accuracy. The uncertainties and limitations within this research including the interpretation error and accuracy measures were also analyzed and discussed, and a unified framework assessing the segmentation accuracy was highly suggested.

Improvements in computer vision combined with current structure-from-motion photogrammetric methods (SfM) have provided users with the ability to generate very high resolution structural (3D) and spectral data of the forest from imagery collected by unmanned aerial systems (UAS). The products derived by this process are capable of assessing and measuring forest structure at the individual tree level for a significantly lower cost compared to traditional sources such as LiDAR, satellite, or aerial imagery. Locating and delineating individual tree crowns is a common use of remotely sensed data and can be accomplished using either UAS-based structural or spectral data. However, no study has extensively compared these products for this purpose, nor have they been compared under varying spatial resolution, tree crown sizes, or general forest stand type. This research compared the accuracy of individual tree crown segmentation using two UAS-based products, canopy height models (CHM) and spectral lightness information obtained from natural color orthomosaics, using maker-controlled watershed segmentation. The results show that single tree crowns segmented using the spectral lightness were more accurate compared to a CHM approach. The optimal spatial resolution for using lightness information and CHM were found to be 30 and 75 cm, respectively. In addition, the size of tree crowns being segmented also had an impact on the optimal resolution. The density of the forest type, whether predominately deciduous or coniferous, was not found to have an impact on the accuracy of the segmentation.

Unmanned aerial systems (UAS) have become a flexible and low-cost platform to supplement aerial or satellite remote sensing for precision forestry. The data derived from UAS are widely used to measure variables at a single tree level where individual tree crowns become fundamental. Most research has adapted some region growing method for individual tree crown delineation (ITCD). However, pixels are often used as growing units without considering the spatial and contextual information, which can be adversely affected by noise (e.g., background or branches) in the imagery. Instead, over-segments can compensate for these pixels’ shortcomings while also partially detecting the edge of a tree crown. These over-segments then become the growing units used in this study. In addition, this research incorporated competition among the over-segments to alleviate the deficits of sequential ordering. The algorithm was evaluated in three study sites with distinctive forest patterns utilizing natural color imagery. Results demonstrated that using over-segments as growing units improved the ITCD accuracy by 1.8%–2.3%, whereas incorporating the competitive mechanism further increased the accuracy by 4.3%–9.3%. The spatial arrangement of trees also affected the segmentation accuracy. The sources of uncertainties, such as the manually interpreted treetops and feature selection for region growing, were also analyzed. The algorithm developed in this research can be easily extended to other data sources to achieve promising accuracy.

The primary goal of thematic accuracy assessment is to measure the quality of land cover products and it has become an essential component in global or regional land cover mapping. However, there are many uncertainties introduced in the validation process which could propagate into the derived accuracy measures and therefore impact the decisions made with these maps. Choosing the appropriate reference data sample unit is one of the most important decisions in this process. The majority of researchers have used a single pixel as the assessment unit for thematic accuracy assessment, while others have claimed that a single pixel is not appropriate. The research reported here shows the results of a simulation analysis from the perspective of positional errors. Factors including landscape characteristics, the classification scheme, the spatial scale, and the labeling threshold were also examined. The thematic errors caused by positional errors were analyzed using the current level of geo-registration accuracy achieved by several global land cover mapping projects. The primary results demonstrate that using a single-pixel as an assessment unit introduces a significant amount of thematic error. In addition, the coarser the spatial scale, the greater the impact on positional errors as most pixels in the image become mixed. A classification scheme with more classes and a more heterogeneous landscape increased the positional effect. Using a higher labeling threshold decreased the positional impact but greatly increased the number of abandoned units in the sample. This research showed that remote sensing applications should not employ a single-pixel as an assessment unit in the thematic accuracy assessment.