A phenological knowledge transfer-based fine grained land cover change sample collection method: a case study of coastal wetlands

Abstract

Methods for fine-grained sample collection are essential for detecting land cover changes at large scales. The complexity of wetland types increases the difficulty of obtaining training samples for high-precision wetland changes, while existing methods mainly focus on coarse-grained classification of urban areas, ignoring the physical growth cycle of vegetation. To solve the above problems, we propose a method for phenological knowledge transfer-based fine grained land cover change sample collection (PKT). Taking the Yellow River Delta as an example, the experimental results are shown as follows. (1) The overall accuracy of the results of the PKT method is 77.03%, and k is 0.42, which is better than the results of the other methods. (2) The PKT method is able to obtain the area of wetland change more accurately and can identify the wetland type changes in the area of change. (3) Making full use of multisource data and fine-grained category information can effectively improve the accuracy of change training samples. (4) Changes in coastal wetlands are the result of the interaction between natural factors and human activities. (5) Further restoration and management of wetlands can be carried out in terms of appropriate protective measures and restrictions on construction behavior.