Spatiotemporal Variations of Aboveground Biomass under Different Terrain Conditions

Effects of Driving Factors on Forest Aboveground Biomass (AGB) in China’s Loess Plateau by Using Spatial Regression Models

Forests are the main body of carbon sequestration in terrestrial ecosystems and forest aboveground biomass (AGB) is an important manifestation of forest carbon sequestration. Reasonable and accurate quantification of the relationship between AGB and its driving factors is of great importance for increasing the biomass and function of forests. Remote sensing observations and field measurements can be used to estimate AGB in large areas. To explore the applicability of the panel data models in AGB and its driving factors, we compared the results of panel data models (spatial error model and spatial lag model) with those of geographically weighted regression (GWR) and ordinary least squares (OLS) to quantify the relationship between AGB and its driving factors. Furthermore, we estimated the tree height, diameter at breast height, canopy cover (CC) and species diversity index (Shannon–Wiener index) of Robinia pseudoacacia plantations in Changwu on the Loess Plateau using field data and remote sensing images by a random forest model and estimated soil organic carbon (SOC) contents using laboratory data by ordinary kriging (OK) interpolation. We estimated AGB using the already estimated tree height and diameter at breast height combined with the allometric growth equation. In this study, we estimated SOC contents by OK interpolation, and the accuracy R2 values for each soil layer were greater than 0.81. We estimated diameter at breast height (DBH), CC, SW and tree height (TH) using the random forest, and the accuracy R2 values were 0.85, 0.82, 0.76 and 0.68, respectively. We estimated AGB with random forest and the allometric growth equation and found that the average AGB was 55.80 t/ha. The OLS results showed that the residuals of the OLS regression exhibited obvious spatial correlations and rejected OLS applications. GWR, SEM and SLM were used for spatial regression analysis, and SEM was the best model for explaining the relationship between AGB and its driving factors. We also found that AGB was significantly positively correlated with CC, SW, and 0–60 cm SOC content (p < 0.05) and significantly negatively correlated with slope aspect (p < 0.01). This study provides a new idea for studying the relationship between AGB and its driving factors and provides a basis for practical forest management, increasing biomass, and giving full play to the role of carbon sequestration.

Modelling Impact of Site and Terrain Morphological Characteristics on Biomass of Tree Species in Putorana Region

(1) Background: Boreal forests influence global carbon balance and fulfil multiple ecosystem services. Their vegetation growth and biomass are significantly affected by environmental conditions. In the present study we focused on one of the least accessible and least studied parts of the boreal region situated in the western part of Putorana plateau, Central Siberia (Lama and Keta lakes, Krasnoyarsk region), northern Russia. (2) Methods: We derived local height-diameter and crown radius-height models for six tree species. We used univariate correlation and multiple regression analyses to examine the relationships between tree biomass and environmental conditions. (3) Results: Total tree biomass stock (aboveground tree biomass + aboveground and buried deadwood) varied between 6.47 t/ha and 149 t/ha, while total deadwood biomass fluctuated from 0.06 to 21.45 t/ha. At Lama, biomass production decreased with elevation. At Keta, the relationship of biomass to elevation followed a U shape. Stand biomass changed with micro-terrain morphology and soil nutrient content, while the patterns were location-specific. (4) Conclusions: The majority of the derived models were significant and explained most of the variability in the relationships between tree diameter or crown radius and tree height. Micro-site environmental conditions had a substantial effect on tree biomass in the studied locations.

Remote Sensing Technology Applications in Forestry and REDD+

Advances in close-range and remote sensing technologies drive innovations in forest resource assessments and monitoring at varying scales. Data acquired with airborne and spaceborne platforms provide us with higher spatial resolution, more frequent coverage and increased spectral information. Recent developments in ground-based sensors have advanced three dimensional (3D) measurements, low-cost permanent systems and community-based monitoring of forests. The REDD+ mechanism has moved the remote sensing community in advancing and developing forest geospatial products which can be used by countries for the international reporting and national forest monitoring. However, there still is an urgent need to better understand the options and limitations of remote and close-range sensing techniques in the field of degradation and forest change assessment. This Special Issue contains 12 studies that provided insight into new advances in the field of remote sensing for forest management and REDD+. This includes developments into algorithm development using satellite data; synthetic aperture radar (SAR); airborne and terrestrial LiDAR; as well as forest reference emissions level (FREL) frameworks.