*4.3. Extraction of Ecotones*

Data sizes and resolutions can be applied effectively when studying small-scale landscape patterns. It is also noted that transitions between abandoned farmland and forest ecosystems have created height variations among plant communities, and a CHM built by DSM and DTM data could highlight such differences. Hence, this model is used for the extraction of the forest-agriculture ecotones. Hou and Waltz [48,61] have similarly used height variations between different plant communities to segregate and extract small-scale landscape units such as forest–grassland ecotones and tree lines from a 3D perspective. They used airborne LiDAR elevation data and high-resolution satellite image data to classify 3D landscape maps before conducting a comparative analysis between the results and those obtained using the traditional 2D classification method. The results confirmed that 3D height information could reflect gradient changes occurring at forest boundaries and thus be used to extract small-scale landscape units such as ecotones. However, LiDAR scanners are still not considered as standard tools because of the high cost of airborne LiDAR instruments and peripherals (reference targets, tripods, software suites, graphic workstations), as well as data collection [65].

Moreover, some previous study has shown that the point clouds derived from LiDAR and RGB sensors successfully capture the 3D structure [66]. Photogrammetric processing relies on identifiable features to be matched across sequences of images. It should be emphasized that the structural information obtained based on the photogrammetric point cloud is naturally compatible with the orthophoto image, so in this study, the ecotones extraction method is different from that of Hou. As described in the method part, we did not do growing as Hou did. We directly shrink due to the perfect match between the structure information from the photogrammetric point cloud and image data. Image data acquired through UAS photography have higher resolution, which means that the proposed method may be superior to the traditional satellite remote sensing method for regional- and smallscale studies on ecotones. Although the resolution of some satellite images (e.g., Sentinel-2 data [67]) has improved in recent years, and the heights of ground objects can be accurately obtained from airborne or satellite LiDAR (e.g., ICESat-2 data [68]) data, both methods incur substantial data acquisition costs, and the data cycle cannot be prescribed. By contrast, UAS has the advantages of flexibility and controllability. Image data obtained through this method have various characteristics, including being regionalized, customized, and personalized. In addition to satisfying various research requirements, the cost of using this method is also generally acceptable to most research organizations and teams.

### *4.4. Quantification of Ecotones*

Traditionally, studies on ecotones have often relied on transect lines laid along environmental gradients to collect data on plant communities and soil properties and then

determined the range of ecotones using moving split-window techniques. Being constrained by human or terrain factors, this method only allows research to be conducted in one direction along the transect lines. By contrast, the remote sensing method based on UAS photographic technology allows comprehensive regional data acquisition. It permits analysis and research from the perspective of landscape patterns, thereby eliminating the necessity for multiple sessions of field investigations and thus making this method obviously advantageous in terms of reduced working hours and intensities. This fully demonstrates the advantages of continuous spatial pattern characterization in landscape ecology research, such as quantified area or edge length (Table 2). The results of this study on the quantification of the landscape containing the ecotones that the ecotones occupy a considerable weight in the whole landscape can be seen from the results of this study. Both TA and PLAND reflect the important spatial landscape status of the ecotones, which is often neglected in previous landscape ecology studies. The neglect of the ecotones may lead to underestimating landscape heterogeneity and the overestimation of landscape contrast. It can be seen from the PLAND index that the dominant patches in the landscape are forest, abandoned land, and ecotones. It also reflects the high landscape heterogeneity. In the TE index, the values of ecotones and abandoned land are close to each other.

### *4.5. Transect-Based Analysis*

Ecotones were mainly determined based on the variations in vegetation height when transitioning between different communities in this study. By contrast, transect surveys use plant diversity levels within the community or soil indicators as the bases. The proliferation and migration of plant species can be determined by surveying sample communities (Figure 6). The herbaceous plants growing within forests can also proliferate over wide extents. However, in this study, we could only determine the height changes at the canopy of the communities but could not assess the specific changes of plants growing in the understory. It might explain the differences in measurement results for the Baijixun sample plot between this study and earlier transect surveys conducted at the traditional community scale. The difference may also be caused by the inaccurate expression of the width in the traditional one-dimensional transect analysis method. Thus, in this study, interpolation lines are used to measure the binarization map with ecotones, and more accurate width measurement results are obtained. It is also worth noting that due to restrictions imposed by the terrains of the sample plots, previous transect surveys could only lay transect lines along one direction: from the base of the mountains up toward the upper edge of the abandoned agricultural land [42,56,60]. Consequently, the data obtained using these transects could only determine the range and dynamic changes of ecotones between the abandoned agricultural land and forests in that particular direction. The survey results revealed that after the abandonment of farmlands and over time, the ecotones gradually became wider in that direction and could eventually be restored as pine forests [42,60]. Additionally, it should be noted that the detection based on MSW technology requires additional consideration of the size of the window, which also brings uncertainty to the ecotones' detection. For example, it can be seen from the results that the initial value of OM in transect 1 and IV in transect 3 is slightly greater than 0, which means that the ecotones that may occur earlier along the transect direction is not detected, which also partially affects the verification of width. However, the precision of the location is irrelevant, as all peaks fall within the range of UAS results. This also reflects the shortcomings of the traditional one-dimensional MSW method.

### **5. Conclusions**

In general, we present a scientifically solid and practical method for extracting 3D ecotones utilizing UAS photographic technology, which can be utilized to enhance the quantification of landscape pattern. The results confirm the advantages of spatial geostatistical techniques in processing photogrammetric derived data, which benefit from high-precision reconstruction of vegetation structure information. In addition, due to the natural matching of structural information and spectral information derived from UAS, the ability of detecting ecotones based on edge 2D moving window technology is further strengthened, which is enough to produce high-resolution land cover mapping with ecotones. In the end, compared to the standard one-dimensional approach of ecotones measurement utilizing transect surveys and the moving split-window methodology, the method described here provides a thorough understanding of the landscape pattern and spatial characteristics of ecotones. This approach has the potential to quantify regional vegetation and forest transition processes. It should be utilized to evaluate and optimize the management zones of existing nature reserves, as well as to establish additional protected areas and corridors for species migration, genetic exchange, and population size. This should coincide with the exhaustive and regular gathering of species diversity census data. Last, the ecotones' extraction method, landscape pattern quantification, and change detection should be linked to the function of ecotones in a landscape and included into biodiversity and ecosystems protection, as well as sustainable planning and management of regional areas. Through constant inquiry and monitoring, it will be possible to get a full understanding of the entire biological process of the restoration of abandoned agricultural land using this ecotones extraction approach.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/d14050406/s1, Table S1. A summary of field investigation data; Table S2. Summary of cross-validation results; Table S3. Four indices used to quantify the characteristics of the ecotones in the landscape; Figure S1. The pattern of the three transects set; Figure S2. A set of training samples for training classifiers; Figure S3. Based on a control-variable experiment, the optimal shape and the compactness are selected; Figure S4. Based on the optimal shape and compactness selected, an adaptive scale of segmentation is determined through a series of scale changes; Figure S5. Topographic prediction results based on five different geostatistical models.

**Author Contributions:** B.W., W.W. and Z.Z. designed and conceived this study; field survey and UAS data collection were completed by B.W., H.S., Q.L., Q.X. and Y.D.; B.W., H.S., Y.D. and Q.L. processed all image data and some work about object-based classification; B.W., Y.M., Z.Z. and Q.X. participated in the data extraction, analysis, and validation; Z.Z., A.P.C. and L.L. guided the study and paper revision. The prime draft was completed by B.W. and Z.Z.; W.W., L.L. and A.P.C. finished the revision of the first draft. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by grants from the National Natural Science Foundation of China (41761040 and 41361046), the National Key R&D Program of China (No. 2017YFC0505200), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDPB0203), and the foundation of Innovation in Culture Adaptation: Fostering Sustainable Community-Based Natural Resource Management in the South-Western Ethnic Minority Region, China (15XSH02). This work was supported by Graduate Research Innovation Fund project of Yunnan University (2020Z58).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** All data used in the manuscript are already publicly accessible, and we provided the download address in the manuscript.

**Acknowledgments:** We are grateful for the kindness and generosity of the people in Forestry Bureau of Weixi County, Yunnan Province who helped us conduct our work. We also thank reviewers and Shiliang Liu for comments that helped us refine our thinking. In addition, we are very grateful to Cameron Proctor from the University of Windsor for his suggestions and amendments to our text.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **Abbreviations**


### **References**

