Thematic maps, such as forest change and land cover change maps, are generally inaccurate. The assessment of accuracy, defined as the correctness of a map, is critical for understanding the quality and utility of thematic maps. This review shows the fundamental principles in area estimation and accuracy assessment for forest change maps based on three components: sampling design, response design, and analysis, and then reveals the criterion and recommended practices. Several special cases of accuracy assessment are also discussed. A probability sampling design is implemented in the statistically rigorous accuracy assessment to estimate accuracy based on the comparison of a map and reference data. Population error matrix is crucial in assessing the accuracy and estimating area. Further, the use of unbiased or consistent estimators that correspond to the sampling design is critical for deriving accuracy metrics and area estimates with associated uncertainty. Although the fundamental principles of accuracy assessment are well established, methods for addressing issues have also recently been developed. Practitioners are required to choose the optimal protocols to achieve their objectives of the accuracy assessment because no single protocol or approach can completely address all situations.

This study aimed to estimate the tree height and volume based on image data, which are obtained by conversion of the sound generated from hammering a stem using deep learning. We hammered 20 trees 100 times, recorded the hammering sound, and generated the spectrogram, which presented the sound pressure at each frequency for 0.6 s. Data comprising 10,000 images were loaded into a deep learning model. We used the Neural Network Console (NNC) as the deep learning system and the LeNet, which forms a programmed regression layer to an output layer, as the deep learning algorithm. We divided 10,000 images into 5 equal sets, and performed three learning patterns (LP-I, LP-II, and LP-III). LP-I used the four sets as training data and the remainder as test data, LP-II used three trees and LP-III used six trees, which select for each tree from three divisions (large, medium, small), as test data. A performance evaluation of the proposed model was performed using three indicators: mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R²). Each learning pattern provided very good estimates (R² values for each learning pattern for the test data ranged from 0.9192 to 0.9996), except for the height estimate by LP-III (R²＝0.3672). LP-III generated very poor height estimates with a bias and tended to underestimate by > 30 m and overestimate by < 30 m. However, each learning pattern provided a good estimate of the tree volume, generally without any bias. Thus, we found this method to be more effective for estimation of tree volume than tree height.

As the world's forest area continues to decline, China's forest area has consistently increased since the 1980s. In this research, we clarified the driving force behind this by focusing on the socio-economic factors of China. There is much previous research on the relationship between socio-economic factors and forest resources in various countries. However, the methods used for research in this field are mainly panel data analysis, and the results produced using such tests, for example unit roots and cointegrations of time series data, are limited. In this research, we used the time series data for the last 40 years on forest area and socio-economic factors in China, and we used an autoregressive distribution lag (ARDL) model considering the stationarity of variables and the cointegration relationship. Unit root tests show that all variables are either stationary or becoming stationary after the first differencing. The estimation results show that the change rate of GDP per capita has a positive impact on the change rate of forest area in the short-run but a negative impact in the long-run, and that the change rate of rural populations has a negative impact in both the short- and long-run. It was also shown that the change rate of urban population and foreign direct investments in China have a positive impact on forest coverage rates in the short-run.

We investigated the height and suppression status of planted trees in young sugi (Cryptomeria japonica) plantations that were weeded every year in Yame, Fukuoka, Japan, to develop a criterion to determine the need for weeding. Plantations with varying competing vegetation types were studied, and the height of competing vegetation that had recovered one year after weeding differed with variation in the dominate species. Miscanthus (Miscanthus sinensis) exhibited high recovery values, while the recovery heights of three major component species of the competing vegetation (M. sinensis, Rhus chinensis and Mallotus japonicus) were almost the same within each species irrespective of the number of times of weeding (i.e., years after planting). The planted sugi trees tended to be released from suppression according to their height. However, the degree of the release was different for the different vegetation types; generalized linear models demonstrated that sugi needed to be taller in the miscanthus-dominated type than other vegetation types to achieve the same degree of release from suppression. These results suggest that there is no depression of the recovery height for major weeds or trees caused by repeat weeding, and that the weeding criterion should vary with vegetation types, according to their respective post weeding recovery potential. With the assumption that weeding is needed when 90％ of planted sugi tree are taller than surrounding competing vegetation, the critical height of sugi trees is 2.2 m for the miscanthus-dominated type and 1.4 m for the other vegetation types.