Abstract
Research Highlights: In this study, we classified natural forest into four forest types using time-series multi-source remotely sensed data through a proposed semi-supervised model developed and validated for mapping forest types and assessing forest transition in Vietnam. Background and Objectives: Data on current forest state and changes detection are always essential for forest management and planning. There is, therefore, a need for improved tools to classify and evaluate forest dynamics more accurately and effectively. Our objective is to develop such tools using a semi-supervised model and landscape metrics to classify and map changes in natural forest types by using multi-source remotely sensed data. Materials and Methods: A combination of Landsat data with PALSAR and PALSAR-2 was used for forest classification through the proposed semi-supervised model. This model turned a kernel least square into a self-learning algorithm, trained by a small number of samples with given labels, and then used this classifier to assign labels to the unlabeled data. The overall accuracy, kappa, user’s accuracy, and producer’s accuracy were used to evaluate the classification accuracy by comparing the classified image with the results of ground truth interpretation. Based on the classified images, forest transition was evaluated using certain landscape metrics at the class and landscape levels. Results: The multi-source data approach achieved improved discrimination of forest types compared to only using single data (optical or radar data). Good classification accuracies were obtained, with kappas of 0.81, 0.76, and 0.74 for the years 2007, 2010, and 2016, respectively. The analysis of landscape metrics indicated that there were different behaviors in the four forest types, as well as provided much information about the trends in spatial pattern changes. Conclusions: This study highlights the utilization of a semi-supervised model in forest classification, and the analysis of forest transition using landscape metrics. However, future research should include a comparison of different models to estimate the improvement of the proposed model. Another important study that should be conducted is to test the proposed method on larger areas.
Highlights
Since the early 1990s, the tropical forest in several countries has been undergoing a transition period from degradation to reforestation [1,2,3]
The classification accuracy associated with using remote sensing is affected by many factors, such as the classification techniques, training samples, and the signal reflected from objects
We classified natural forests based on the timber reserve of standing trees into four main types: rich, medium, poor, and restoration forest. These four types differ in species composition and timber reserves, we found that with only a single source of data it is often difficult to discriminate between different kinds of natural forest types because of the very similar information on canopy and forest structure captured by remotely sensed data [7]
Summary
Since the early 1990s, the tropical forest in several countries has been undergoing a transition period from degradation to reforestation [1,2,3]. Forest transition is considered from the perspective of forest area changes and the conversion from other land use/land cover types to forest. Forests 2019, 10, 673 rapid development of remote sensing technology and the wide application of landscape ecology, they supply effective tools to analyze spatial-temporal changes and related ecological processes. Improved understanding of forest transition provides many benefits, such as global carbon balance or land use and forest policy implementation [4,5]. There is a need to further develop new methods for forest type classification and forest transition assessment. Remote sensing combined with the conventional method to supply validation data has been extensively used in forest inventory. The advantages of the remote sensing technique are costand labor-saving as well as swift observation of large scale forest changes over the long term. The classification accuracy associated with using remote sensing is affected by many factors, such as the classification techniques, training samples, and the signal reflected from objects
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