Mapping the vertical forest structure in a large subtropical region using airborne LiDAR data

Vertical forest structure (VFS) refers to the vertical stratification or layering of forest communities in space, which is a fundamental characteristic of a plant community. It plays a vital role in forest vitality and facilitates various ecological activities and processes. The mapping of VFS is of significant value for both ecological and forestry purposes. In this paper, we presented a novel approach for the automated mapping of VFS in a large subtropical region based on discrete airborne LiDAR data. Firstly, the LiDAR point clouds of a stand (plot or grid cell) were segmented into 100 height bins from the top to the ground, and a height-frequency histogram was obtained by calculating the proportion of the number of returns in the bins to the total number of returns, which objectively represented the vertical distribution of canopy material. Secondly, a univariate ten-order polynomial was used to fit the height-frequency histogram, enabling the generation of a continuous vertical canopy profile (pseudo-waveform) of the stand. Thirdly, a comprehensive set of vertical structure parameters was defined and extracted based on the pseudo-waveforms, which effectively characterized the vertical profile layer and the canopy layer. Fourthly, to construct a comprehensive framework, 43 model profiles were summarized from the field plots, taking into account the number of effective peaks in the pseudo-waveforms and other vertical structure parameters. Finally, 43 classification rules were developed and 18 judgment criteria were established using the vertical structure parameters of the mode profiles. They classified vertical forest structures into 24 classes with explicit spatial definitions. The classification of 1147 field plots resulted in an overall accuracy of 94.7% and a kappa coefficient of 0.937. The VFS mapping over a large area demonstrated an effective execution rate of 99.8% for both rules and criteria. The proposed approach exhibits high accuracy and excellent generalization ability across different forest types, species, and study sites, highlighting its ecological and forestry significance. It represents a significant advance in the automated classification and mapping of VFS in large subtropical regions using airborne LiDAR data. However, the proposed approach needs to be validated in other vegetation zones to assess its generalizability and extend its applicability

Using the Error-in-Variable Simultaneous Equations Approach to Construct Compatible Estimation Models of Forest Inventory Attributes Based on Airborne LiDAR

Airborne LiDAR has been extensively used for estimating and mapping forest attributes at various scales. However, most models have been developed separately and independently without considering the intrinsic mathematical relationships and correlations among the estimates, which results in the mathematical and biophysical incompatibility of the estimates. In this paper, using the measurement error model approach, the error-in-variable simultaneous equation (SEq) for airborne LiDAR-assisted estimations of four forest attributes (stand volume, V; basal area, G; mean stand height, H; and diameter at breast height, D) for four forest types (Chinese fir, pine, eucalyptus, and broad-leaved forest) is developed and compared to the independence models (IMs). The results indicated that both the SEqs and IMs performed well, and the rRMSEs of the SEqs were slightly larger than those of the IMs, while the increases in rRMSE were less than 2% for the SEqs. There were statistically significant differences (α = 0.05) in the means of the estimates between SEqs and IMs, even though their average differences were less than ±1.0% for most attributes. There were no statistically significant differences in the mean estimates between SEqs, except for the estimates of the D and G of the eucalyptus forest. The SEqs with H and G as the endogenous variables (EVs) to estimate V performed slightly better than other SEqs in the fir, pine, and broad-leaved forests. The SEq that used D, H, and V as the EVs for estimating G was best in the eucalyptus forests. The SEq ensures the definite mathematical relationship among the estimates of forest attributes is maintained, which is consistent with forest measurement principles and therefore facilitates forest resource management applications, which is an issue that needs to be addressed for airborne LIDAR forest parameter estimation

Generalized models for subtropical forest inventory attribute estimations using a rule-based exhaustive combination approach with airborne LiDAR-derived metrics

Airborne LiDAR has been widely used to map forest inventory attributes at various scales. However, most of the developed models on airborne LiDAR-based forest attribute estimations are specific to a study site and forest type (or species), so it is essential to develop predictive models with excellent generalization capabilities across study sites and forest types for the consistent estimation of forest attributes. In this study, 13 LiDAR-derived metrics, which depicted the three-dimensional structural aspects of stand canopy and had clear forest mensuration and ecology significance, were categorized into three groups (height, density, and vertical structure). A rule-based exhaustive combination was then used to construct 86 multiplicative power formulations consisting of 2–5 predictors for estimating the stand volume and basal area. By calibrating and validating these formulations using data from four forest types in the three study regions, we obtained the 24 best local models. Based on these models we proposed a set of accuracy criteria to determine generalized formulations and models. By applying two selection methods (the mean and mixed data methods), we finally archived the eight best region-generalized models, which could be used for estimating the stand volume and basal area of four forest types across study sites on a province scale. This study highlights the accuracy criteria and procedures for developing generalized formulations and models for consistent estimations of forest inventory attributes using airborne LiDAR data

Ecosystem and Driving Force Evaluation of Northeast Forest Belt

The ecosystem in the Northeast Forest Belt (NFB) can provide various ecosystem services, such as soil conservation, habitat provision, water conservation, and so on. It is essential for maintaining the ecological environment in Northeast China and the entire country. In the face of increasingly severe environmental problems, the comprehensive and accurate evaluation of ecosystem conditions and their changes is significant for scientific and reasonable recovery and protection measures. In this study, the NFB was taken as the research area. The spatio-temporal changes in ecological quality from 2005 to 2015 and the main driving factors behind them were analyzed by constructing the comprehensive ecosystem evaluation index. The results showed that: The landscape types of the NFB were mainly forest, cropland, and grassland. And the better ecological environment of the NFB was mainly distributed in the south of Changbai Mountains (CBM), the middle of Lesser Khingan Mountains (LKM), and the northwest of Greater Khingan Mountains (GKM). In contrast, the northeast of CBM, the southwest of LKM, and the edge of southern GKM were relatively poor. During 2005–2015, the ecosystem in the NFB was in a relatively good state as a whole, showing a steady-to-good development trend. However, more attention needed to be paid to some areas where degradation still existed. Land use/cover, climate (annual average rainfall, etc.), and human disturbance were potential factors affecting ecosystem evolution in the NFB. This study aims to provide an effective scientific basis and policy reference for the environmental protection and construction of the NFB

Measuring Gross Ecosystem Product (GEP) in Guangxi, China, from 2005 to 2020

The economic and social development evaluation system with the Gross Domestic Product (GDP) as the leading indicator is no longer applicable to the current social progress in China. It is essential to carry out an assessment of the Gross Ecosystem Product (GEP) to integrate ecological benefits into the economic and social evaluation system and promote sustainable socio-economic development. This study took Guangxi, an important province in South China, as the study area. We used four periods of land use and land cover data (LULC), meteorological data, soil data and yearbook statistics to construct a GEP assessment framework based on geographic information system (GIS) and remote sensing (RS) technologies. We accounted for the provisioning services, regulating services, and tourism services provided by Guangxi in 2005, 2010, 2015, and 2020 and analyzed the region’s and municipalities’ spatial–temporal pattern characteristics and trends of change in GEP. In addition, this study also discusses the relationship between GEP and GDP. The results showed that many important products and services provided by natural ecosystems in Guangxi had enormous economic benefits. GEP had increased from CNY 15,657.37 billion in 2005 to CNY 36,677.04 billion in 2020, and the distribution of GEP showed obvious spatial heterogeneity. The value of ecosystem regulation services was about 65–89% of GEP, which is the main component of GEP. From 2005 to 2020, natural ecosystem protection and socio-economic development have achieved coordinated development in Guangxi. GEP and GDP showed upward trends in general. Although Guangxi is relatively backward in terms of economic development, the scientific quantification of the unrealized value of the services provided by the ecosystem through GEP accounting makes it possible to transform ecological advantages into economic advantages. It could help the local government and people to re-recognize the value of ecological resources and realize the beautiful vision of lucid waters and lush mountains as invaluable assets

Increase in precipitation and fractional vegetation cover promote synergy of ecosystem services in China’s arid regions—Northern sand-stabilization belt

Research on synergies and trade-offs between ecosystem services (ES) contributes to a better understanding of the linkages between ecosystem functions. Relevant research mainly focuses on mountain areas, while research in arid areas is obviously insufficient. In this research, we use the northern sand-stabilization belt (NSB) as an example to explore how the synergies and trade-offs between different ES vary with the gradient of precipitation and fractional vegetation cover (FVC) over the period 2000-2020. Based on five simulated ecosystem services (habitat provision, sand-stabilization service, water conservation service, soil conservation service and carbon sequestration service), the Pearson correlation coefficient method was used to analyze the various characteristics of the trade-offs and synergies among the different ES pairs along the FVC and precipitation gradients. Results showed that: Synergies between most paired ES increased significantly with increasing precipitation and FVC. However, ES have different sensitivities to environmental change, FVC promotes bit more synergy of ES pairs than precipitation. The study also found that land use/land cover may be an important driving factor for trade-offs and synergies between paired ES. The findings demonstrate that increased precipitation and FVC promote synergy of ecosystem services in arid regions of China. In the future, it can be investigated whether anthropogenic increase in FVC in arid regions can significantly contribute to the synergy of ES. In the meantime, this study could improve our understanding of arid and semi-arid (or macro-regional) ecosystems and contribute to the development of ecosystem management and conservation measures in NSB

Response of ecosystem services to impervious surface changes and their scaling effects in Loess Plateau ecological Screen, China

The complexity and fragility of the Yellow River Basin ecosystem limits its economic growth and sustainable social development as a strategic planning area for development in western China. The Chinese government has established the Loess Plateau Ecological Screen (LPES) in the region to eliminate or mitigate the negative ecological impacts of human activities represented by the expansion of impervious surfaces (IS) through active ecological conservation and restoration. However, there are few studies that quantify the effects of impervious surfaces on ecosystem services (ES). To fill this gap, this study takes the LPES in China as an example and explores the response of ES to IS changes and its scale effect from 2000 to 2020. Based on remote sensing, meteorological, soil, hydrological, social, and economic data using GIS spatial analysis techniques. The results show that: From 2000 to 2020, the urbanization of the LPES developed rapidly, and the IS increased rapidly. The increase in IS affected the supply of ES, which decreased with the increase in IS growth rate, and this phenomenon had a scale effect. Overall, except for soil conservation service (SCS) - IS, carbon storage service (C) - IS at the administrative scale, the negative correlation increased with increasing scale, while the opposite was true at the grid scale. There were thresholds for the response of ES to IS, and the thresholds were also influenced by the scale of study. The smaller the scale was, the lower the threshold was. However, there were differences in the ranking of each ES reaching the threshold with increasing IS at the grid-scale and administrative division scale. The ecosystem services composite index (ESCI) was found to be the best indicator for exploring the relationship between IS and ES compared to other single ecosystem services indices, with the largest negative correlation with IS and the least influenced by scale effects. Given the obvious scale effect of IS on ES, this study suggests that the development of ecological management programs at the national level should be macroscopically regulated at the provincial level, with specific measures at smaller grid scales (5 Km × 5 Km) to constrain IS expansion.