Desert vegetation is an important part of arid and semi-arid areas, which plays an important role in preventing wind and fixing sand, conserving water and soil, maintaining the balanced ecosystem. Therefore, mapping the vegetation accurately is necessary to conserve rare desert plants in the fragile ecosystems that are easily damaged and slow to recover. In mapping desert vegetation, there are some weaknesses by using traditional digital classification algorithms from high resolution data. The traditional approach is to use spectral features alone, without spatial information. With the rapid development of drones, cost-effective visible light data is easily available, and the data would be non-spectral but with spatial information. In this study, a method of mapping the desert rare vegetation was developed based on the pixel classifiers and use of Random Forest (RF) algorithm with the feature of VDVI and texture. The results indicated the accuracy of mapping the desert rare vegetation were different with different methods and the accuracy of the method proposed was higher than the traditional method. The most commonly used decision rule in the traditional method, named Maximum Likelihood classifier, produced overall accuracy (76.69%). The inclusion of texture and VDVI features with RGB (Red Green Blue) data could increase the separability, thus improved the precision. The overall accuracy could be up to 84.19%, and the Kappa index with 79.96%. From the perspective of features, VDVI is less important than texture features. The texture features appeared more important than spectral features in desert vegetation mapping. The RF method with the RGB+VDVI+TEXTURE would be better method for desert vegetation mapping compared with the common method. This study is the first attempt of classifying the desert vegetation based on the RGB data, which will help to inform management and conservation of Ulan Buh desert vegetation.

Forests play a crucial role in mitigating climate change through carbon storage and sequestration, though environmental change drivers and management scenarios are likely to influence these contributions across multiple spatial and temporal scales. In this study, we employed three tree growth models-the Richard, Hossfeld, and Korf models-that account for the biological characteristics of trees, alongside national forest inventory (NFI) datasets from 1994 to 2018, to evaluate the carbon sink potential of existing forests and afforested regions in China from 2020 to 2100, assuming multiple afforestation and forest management scenarios. Our results indicate that the Richard, Hossfeld, and Korf models provided a good fit for 26 types of vegetation biomass in both natural and planted Chinese forests. These models estimate that in 2020, carbon stocks in existing Chinese forests are 7.62 ± 0.05 Pg C, equivalent to an average of 44.32 ± 0.32 Mg C/ ha. Our predictions then indicate this total forest carbon stock is expected to increase to 15.51 ± 0.99 Pg C (or 72.26 ± 4.6 Mg C/ha) in 2060, and further to 19.59 ± 1.36 Pg C (or 91.31 ± 6.33 Mg C/ha) in 2100. We also show that plantation management measures, namely tree species replacement, would increase carbon sinks to 0.09 Pg C/ year (contributing 38.9 %) in 2030 and 0.06 Pg C/ year (contributing 32.4 %) in 2060. Afforestation using tree species with strong carbon sink capacity in existing plantations would further significantly increase carbon sinks from 0.02 Pg C/year (contributing 10.3 %) in 2030 to 0.06 Pg C/year (contributing 28.2 %) in 2060. Our results quantify the role plantation management plays in providing a strong increase in forest carbon sequestration at national scales, pointing to afforestation with native tree species with high carbon sequestration as key in achieving China's 2060 carbon neutrality target.

Accurate estimates of forest site productivity are essential for environmental planning and forest management. In this study, we developed a new productivity index, hereafter termed basal area potential productivity index (BAPP), to estimate site productivity for irregular and complex forests characterized by multi-aged, multi-species, and multi-layer stands. We presented the biological relevance of BAPP with its computational details. We also compared BAPP against basal area realized productivity (BARP) in order to verify the practicability and reliability of BAPP. Time-series data of the national forest inventory on 1912 permanent sample plots that were located in two main forest types and consisted of oak-dominated mixed forests and other broadleaf forests in northeast China were used to demonstrate the application of BAPP. The results showed that the value of BAPP for each sample plot was larger than or equal to the corresponding BARP value for each forest type. For appropriately managed stands with relatively better site conditions, the values of both BARP and BAPP were almost identical. The values of the difference between BAPP and BARP could therefore be used to effectively assess forest site productivity. Meanwhile, BAPP also provides much reliable and valuable information that can aid decision-making in forest management.

Forest degradation has been considered as one of the main causes of climate change in recent years. The knowledge of estimating degraded forest areas without the application of remote sensing tools can be useful in finding solutions to resolve degradation problems through appropriate restoration methods. Using the existing knowledge through literature review and field-based primary information, we generated new knowledge by combining the information obtained from multi-criteria decision analyses with an analytic hierarchy process, and this was then used to estimate degraded forest area. Estimation involves determining forest degradation index (FDI) and degradation threshold. Continuous inventory data of permanent sample plots collected from degraded forests, consisting of various forest types divided by dominant tree species in the Guangdong province and Tibet autonomous region of China, were used for the purposes. We identified four different forest degradation levels through the determination and comprehensive evaluation of FDI. The degraded forest area with broad-leaved species as dominant tree species in the Guangdong province was estimated to be 83.3% of a total forest area of 24,037 km². In the same province, the degraded forest area with eucalyptus as a dominant tree species was 59.5% of a total forest area of 18,665 km². In the Tibet autonomous region, the degraded forest area with spruce as a dominant tree species was 99.1% of a total forest area of 17,614 km², and with fir as a dominant tree species, the degraded area was 98.4% of a forest area of 12,103 km². A sampling accuracy of forest areas with national forest inventory was about 95% in both provinces. Our study concludes that the FDI method used has a certain scientific rationality in estimating degraded forest area. The forest provides a variety of tangible and intangible goods and services for humans. Therefore, forest management should focus on the improvement of its overall productivity, which is only possible with improving forest site quality. One of the important steps to improve the quality of a forest site is to resolve its degradation issues. The presented method in this article will be useful in finding the solutions to forest degradation problems. This method, which does not need any remote sensing tool, is simple and can be easily applied for estimating any degraded forest area and developing effective forest restoration plans.