Yuan-Zheng Yang

The leaf area index (LAI) is a crucial indicator for quantifying forest productivity and community ecological processes. Satellite remote sensing can achieve large-scale LAI monitoring, but it needs to be calibrated and validated according to the in situ measurements on the ground. In this study, we attempted to use different indirect methods to measure LAI in a tropical secondary forest. These methods included the LAI-2200 plant canopy analyzer (LAI-2200), Digital Hemispherical Photography (DHP), Tracing Radiation and Architecture of Canopies (TRAC), and Terrestrial Laser Scanning (TLS) (using single-station and multi-station measurements, respectively). Additionally, we tried to correct the measured LAI by obtaining indicators of woody components and clumping effects. The results showed that the LAI of this forest was large, with estimated values of 5.27 ± 1.16, 3.69 ± 0.74, 5.86 ± 1.09, 4.93 ± 1.33, and 3.87 ± 0.89 for LAI-2200, DHP, TRAC, TLS multi-station, and TLS single-station, respectively. There was a significant correlation between the different methods. LAI-2200 was significantly correlated with all other methods (p < 0.01), with the strongest correlation with DHP (r = 0.684). TRAC was significantly correlated with TLS single-station (p < 0.01, r = 0.283). TLS multi-station was significantly correlated with TLS single-station (p < 0.05, r = 0.266). With the multi-station measurement method, TLS could maximize the compensation for measurement bias due to the shadowing effects. In general, the clumping index of this forest was 0.94 ± 0.05, the woody-to-total area ratio was 3.23 ± 2.22%, and the total correction coefficient was 1.03 ± 0.07. After correction, the LAI estimates for all methods were slightly higher than before, but there was no significant difference among them. Based on the performance assessment of existing ground-based methods, we hope to enhance the inter-calibration between methods to improve their estimation accuracy under complex forest conditions and advance the validation of remote sensing inversion of the LAI. Moreover, this study also provided a practical reference to promote the application of LiDAR technology in tropical forests.

Boreal forests store a large proportion of the global terrestrial carbon (C), while wildfire plays a crucial role in determining their C storage and dynamics. The aboveground C (AC) pool is an important component of forest C stocks. To quantify the turning point (transforming from C source to C sink) and recovery time of postfire AC, and assess how stand density affects the AC, 175 plots from eight stand age classes were surveyed as a chronosequence in the Great Xing'an Mountains of Northeast China. Linear and nonlinear regression analyses were conducted to describe postfire AC recovery patterns. The results showed that (1) postfire AC exhibited a skewed U‐shaped pattern with the turning point at approximately year 30, when the change rate of AC shifted from negative to positive, (2) it took more than 120 years for this forest ecosystem to recover 80% of AC in unburned old‐growth (200 years) stands, and (3) there was an overall positive relationship between AC and stand density over the entire range of stand age classes; and such relationship was stronger during the early‐ and late‐successional stages, but weaker (p > 0.05) during the midsuccessional stage. Although boreal larch forests have been C sinks under historical fire free intervals, predicted increases in fire frequency could potentially shift it to a C source. Understanding postfire AC dynamics in boreal larch forests is central to predicting C cycling response to wildfire and provides a framework for assessing ecosystem resilience to disturbance in this region.

Air temperature (T_air) near the ground surface is a fundamental descriptor of terrestrial environment conditions and one of the most widely used climatic variables in global change studies. The main objective of this study was to explore the possibility of retrieving high-resolution T_air from the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) products, covering complex terrain in Northeast China. The All Subsets Regression (ASR) method was adopted to select the predictors and build optimal multiple linear regression models for estimating maximum (T_max), minimum (T_min), and mean (T_mean) air temperatures. The relative importance of predictors in these models was evaluated via the Standardized Regression Coefficients (SRCs) method. The results indicated that the optimal models could estimate the T_max, T_min, and T_mean with relatively high accuracies (Model Efficiency ≥ 0.90). Both LST and day length (DL) predictors were important in estimating T_max (SRCs: daytime LST = 0.53, DL = 0.35), T_min (SRCs: nighttime LST = 0.74, DL = 0.23), and T_mean (SRCs: nighttime LST = 0.72, DL = 0.28). Models predicting T_min and T_mean had better performance than the one predicting T_max. Nighttime LST was better at predicting T_min and T_mean than daytime LST data at predicting T_max. Land covers had noticeable influences on estimating T_air, and even seasonal vegetation greening could result in temporal variations of model performance. Air temperature could be accurately estimated using remote sensing, but the model performance was varied across different spatial and temporal scales. More predictors should be incorporated for the purpose of improving the estimation of near surface T_air from the MODIS LST production.

To timely and accurately acquire the spatial distribution pattern of wetlands is of significance for the dynamic monitoring, conservation, and sustainable utilization of wetlands. The small remote sensing satellite constellations A/B stars (HJ-1A/1B stars) for environmental hazards were launched by China for monitoring terrestrial resources, which could provide a new data source of remote sensing image acquisition for retrieving wetland types. Taking Liaohe Delta as a case, this paper compared the accuracy of wetland classification map and the area of each wetland type retrieved from CCD data (HJ CCD data) and TM5 data, and validated and explored the applicability and the applied potential of HJ CCD data in wetland resources dynamic monitoring. The results showed that HJ CCD data could completely replace Landsat TM5 data in feature extraction and remote sensing classification. In real-time monitoring, due to its 2 days of data acquisition cycle, HJ CCD data had the priority to Landsat TM5 data (16 days of data acquisition cycle).