Forest ecosystems are in serious trouble globally, largely due to the over-exploitation. To alleviate environmental problems caused by deforestation, China has undertaken a series of key forestry ecological development programs, including the Natural Forest Protection Program (NFPP), the Conversion of Cropland into Forests Program (CCFP), the Desertification Combating Program around Beijing and Tianjing (DCBT), the Key Shelterbelt Development Programs in the Three-North Region and in the Middle and Lower Reaches of the Yangtze River (KSDP) and the Nature Reserve Development Program in Forestry Sector (WCNR). This article aims to make a documentation of the specific contents (duration, major aims, geographic coverage and investment), and environmental impacts of these programs from peer-reviewed literature, official reports and journals. Environmental impact is measured with land area afforested (except the WCNR) and the consequent changes in ecosystem function. Overall, with the huge investment and long-term efforts, these programs have made tremendous progress in increasing vegetative coverage, enhancing carbon sequestration, controlling soil erosion, conservation of biodiversity, etc. For proper implementation and remarkable achievement, a more balanced approach with flexible planning, suitable measures and proper management should be adopted. Meanwhile, the scientific communities need to be more actively involved in execution and assessment of these programs. The environmental impact of the DCBT, the KSDP, and the WCNR deserve more research concern.

The continuous and extensive pinewood nematode disease has seriously threatened the sustainable development of forestry in China. At present, many studies have used high-resolution remote sensing images combined with a deep semantic segmentation algorithm to identify standing dead trees in the red attack period. However, due to the complex background, closely distributed detection scenes, and unbalanced training samples, it is difficult to detect standing dead trees (SDTs) in a variety of complex scenes by using conventional segmentation models. In order to further solve the above problems and improve the recognition accuracy, we proposed a new detection method called multi-scale spatial supervision convolutional network (MSSCN) to identify SDTs in a wide range of complex scenes based on airborne remote sensing imagery. In the method, a Gaussian kernel approach was used to generate a confidence map from SDTs marked as points for training samples, and a multi-scale spatial attention block was added into fully convolutional neural networks to reduce the loss of spatial information. Further, an augmentation strategy called copy–pasting was used to overcome the lack of efficient samples in this research area. Validation at four different forest areas belonging to two forest types and two diseased outbreak intensities showed that (1) the copy–pasting method helps to augment training samples and can improve the detecting accuracy with a suitable oversampling rate, and the best oversampling rate should be carefully determined by the input training samples and image data. (2) Based on the two-dimensional spatial Gaussian kernel distribution function and the multi-scale spatial attention structure, the MSSCN model can effectively find the dead tree extent in a confidence map, and by following this with maximum location searching we can easily locate the individual dead trees. The averaged precision, recall, and F1-score across different forest types and disease-outbreak-intensity areas can achieve 0.94, 0.84, and 0.89, respectively, which is the best performance among FCN8s and U-Net. (3) In terms of forest type and outbreak intensity, the MSSCN performs best in pure pine forest type and low-outbreak-intensity areas. Compared with FCN8s and U-Net, the MSSCN can achieve the best recall accuracy in all forest types and outbreak-intensity areas. Meanwhile, the precision metric is also maintained at a high level, which means that the proposed method provides a trade-off between the precision and recall in detection accuracy.

Precise growing stock volume (GSV) estimation is essential for monitoring forest carbon dynamics, determining forest productivity, assessing ecosystem forest services, and evaluating forest quality. We evaluated four machine learning methods: classification and regression trees (CART), support vector machines (SVM), artificial neural networks (ANN), and random forests (RF), for their reliability in the estimation of the GSV of Pinus massoniana plantations in China’s northern subtropical regions, using remote sensing data. For all four methods, models were generated using data derived from a SPOT6 image, namely the spectral vegetation indices (SVIs), texture parameters, or both. In addition, the effects of varying the size of the moving window on estimation precision were investigated. RF almost always yielded the greatest precision independently of the choice of input. ANN had the best performance when SVIs were used alone to estimate GSV. When using texture indices alone with window sizes of 3 × 5 × 5 or 9 × 9, RF achieved the best results. For CART, SVM, and RF, R² decreased as the moving window size increased: the highest R² values were achieved with 3 × 3 or 5 × 5 windows. When using textural parameters together with SVIs as the model input, RF achieved the highest precision, followed by SVM and CART. Models using both SVI and textural parameters as inputs had better estimating precision than those using spectral data alone but did not appreciably outperform those using textural parameters alone.