Background: National forest resource assessments and monitoring, commonly known as National Forest Inventories (NFI's), constitute an important national information infrastructure in many countries. Methods: This study presents details about developments of the NFI in China, including sampling and plot design, and the uses of alternative data sources, and specifically • reviews the evolution of the national forest inventory in China through the 20th and 21st centuries, with some reference to Europe and the US;• highlights the emergence of some common international themes: consistency of measurement; more efficient sampling designs; implementation of improved technology; expansion of the variables monitored; scientific transparency;• presents an example of how China's expanding NFI exemplifies these global trends.

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Medium spatial resolution biomass is a crucial link from the plot to regional and global scales. Although remote-sensing data-based methods have become a primary approach in estimating forest above ground biomass (AGB), many difficulties remain in data resources and prediction approaches. Each kind of sensor type and prediction method has its own merits and limitations. To select the proper estimation algorithm and remote-sensing data source, several forest AGB models were developed using different remote-sensing data sources (Geoscience Laser Altimeter System (GLAS) data and Thematic Mapper (TM) data) and 108 field measurements. Three modeling methods (stepwise regression (SR), support vector regression (SVR) and random forest (RF)) were used to estimate forest AGB over the Daxing'anling Mountains in northeastern China. The results of models using different datasets and three approaches were compared. The random forest AGB model using Landsat5/TM as input data was shown the acceptable modeling accuracy (R 2 = 0.95 RMSE = 17.73 Mg/ha) and it was also shown to estimate AGB reliably by cross validation (R 2 = 0.71 RMSE = 39.60 Mg/ha). The results also indicated that adding GLAS data significantly improved AGB predictions for the SVR and SR AGB models. In the case of the RF AGB models, including GLAS data no longer led to significant improvement. Finally, a forest biomass map with spatial resolution of 30 m over the Daxing'anling Mountains was generated using the obtained optimal model.

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Herein, an amphiphilic perylene derivative (denoted as PTC-DEDA) was explored as DNA intercalators endowed with an enhanced affinity and intense electrochemiluminescence (ECL) to construct a target-induced DNA hydrogel biosensing platform for the sensitive detection of microRNA let-7a (miRNA let-7a). Specifically, the DNA hydrogel with numerous dendritic DNA structures was in situ generated via a target-induced nonlinear hybrid chain reaction in the presence of miRNA let-7a, which possessed a large loading capacity to entrap massive DNA intercalators. Then, the PTC-DEDA with positive charges could easily intercalate into the DNA grooves due to the inherent amphipathic structure, achieving a strong ECL signal. Using the proposed PTC-DEDA as both DNA intercalators and ECL emitters, the DNA hydrogel biosensing platform exhibited a high stability and an excellent sensitivity for miRNA let-7a, with a desirable linear range (10 fM to 10 nM) and a low detection limit (1.49 fM). Significantly, the work provides a potential alternative to develop simple and high-efficiency ECL platforms for biochemical analysis applications.

The electrochemiluminescence (ECL) micro-reactors with enhanced intensity and extreme stability were firstly established, unravelling the mechanism of ECL micro-reactors using COF-LZU1 assembled Ru(bpy)32+ as a case study.