Using Relative Projection Density for Classification of Terrestrial Laser Scanning Data with Unknown Angular Resolution

Point cloud classification is a key step for three-dimensional (3D) scene analysis in terrestrial laser scanning but is commonly affected by density variation. Many density-adaptive methods are used to weaken the impact of density variation and angular resolution, which denotes the angle between two horizontally or vertically adjacent laser beams and are commonly used as known parameters in those methods. However, it is difficult to avoid the case of unknown angular resolution, which limits the generality of such methods. Focusing on these problems, we propose a density-adaptive feature extraction method, considering the case when the angular resolution is unknown. Firstly, we present a method for angular resolution estimation called neighborhood analysis of randomly picked points (NARP). In NARP, n points are randomly picked from the original data and the k nearest points of each point are searched to form the neighborhood. The angles between the beams of each picked point and its corresponding neighboring points are used to construct a histogram, and the angular resolution is calculated by finding the adjacent beams of each picked point under this histogram. Then, a grid feature called relative projection density is proposed to weaken the effect of density variation based on the estimated angular resolution. Finally, a 12-dimensional feature vector is constructed by combining relative projection density and other commonly used geometric features, and the semantic label is generated utilizing a Random Forest classifier. Five datasets with a known angular resolution are used to validate the NARP method and an urban scene with a scanning distance of up to 1 km is used to compare the relative projection density with traditional projection density. The results demonstrate that our method achieves an estimation error of less than 0.001° in most cases and is stable with respect to different types of targets and parameter settings. Compared with traditional projection density, the proposed relative projection density can improve the performance of classification, particularly for small-size objects, such as cars, poles, and scanning artifacts

Elevation dependency of future degradation of permafrost over the Qinghai-Tibet Plateau

Global warming has caused widespread permafrost degradation, but the geographic regularity of permafrost degradation is unknown. Here, we investigated the three-dimensional features of future permafrost degradation on the Qinghai-Tibetan Plateau. Our findings show that permafrost degradation under shared socioeconomic pathways (SSPs) has obvious three-dimensional characteristics. In comparison to latitude and aridity, permafrost degradation is closely related to elevation, i.e. it slows with elevation, a phenomenon known as elevation-dependent degradation. The pattern of elevation-dependent degradation is consistent across four subzones and is strongly linked to thermal conditions that vary with elevation. Under SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, remarkable elevation-dependent warming (EDW) is observed at 3600–4900 m, but changes in mean annual ground temperature of permafrost and EDW as altitude rises are anti-phase. Under any SSP, the magnitude of mean annual air temperature along altitude belts determines the degree of permafrost degradation ( R ^2 > 0.90). This research provides new insight on the evolution of permafrost

Effects of permafrost collapse on soil bacterial communities in a wet meadow on the northern Qinghai-Tibetan Plateau

Abstract Background Permafrost degradation may develop thermokarst landforms, which substantially change physico–chemical characteristics in the soil as well as the soil carbon stock. However, little is known about changes of bacterial community among the microfeatures within thermokarst area. Results We investigated bacterial communities using the Illumina sequencing method and examined their relationships with soil parameters in a thermokarst feature on the northern Qinghai-Tibetan Plateau. We categorized the ground surface into three different micro-relief patches based on the type and extent of permafrost collapse (control, collapsing and subsided areas). Permafrost collapse significantly decreased the soil carbon density and moisture content in the upper 10 cm samples in the collapsing areas. The highest loading factors for the first principal component (PC) extracted from the soil parameters were soil carbon and nitrogen contents, while soil moisture content and C:N ratios were the highest loading factors for the second PC. The relative abundance of Acidobacteria decreased with depth. Bacterial diversity in subsided areas was higher than that in control areas. Conclusions Bacterial community structure was significantly affected by pH and depth. The relative abundance of Gemmatimonadetes and Firmicutes were significantly correlated with the first and second PCs extracted from multiple soil parameters, suggesting these phyla could be used as indicators for the soil parameters in the thermokarst terrain

Spatial, Phenological, and Inter-Annual Variations of Gross Primary Productivity in the Arctic from 2001 to 2019

Quantifying the spatial, seasonal (phenological), and inter-annual variations of gross primary productivity (GPP) in the Arctic is critical for comprehending the terrestrial carbon cycle and its feedback to climate warming in this region. Here, we evaluated the accuracy of the MOD17A2H GPP product using the FLUXNET 2015 dataset in the Arctic, then explored the spatial patterns, seasonal variations, and interannual trends of GPP, and investigated the dependence of the spatiotemporal variations in GPP on land cover types, latitude, and elevation from 2001 to 2019. The results showed that MOD17A2H was consistent with in situ measurements (R = 0.8, RMSE = 1.26 g C m−2 d−1). The functional phenology was also captured by the MOD17A2H product (R = 0.62, RMSE = 9 days) in the Arctic. The spatial variation of the seasonal magnitude of GPP and its interannual trends is partly related to land cover types, peaking in forests and lowest in grasslands. The interannual trend of GPP decreased as the latitude and elevation increased, except for the latitude between 62°~66° N and elevation below 700 m. Our study not only revealed the variation of GPP in the Arctic but also helped to understand the carbon cycle over this region

Impacts of permafrost on above- and belowground biomass on the northern Qinghai-Tibetan Plateau

Because permafrost is extremely sensitive to climate change, it is of great importance to understand the relationship between permafrost and vegetation biomass. This study aims to reveal the impacts of permafrost on above- and belowground vegetation biomass on the northern Qinghai-Tibetan Plateau. Soil temperature, moisture, active-layer thickness, vegetation coverage, aboveground biomass (AGB), belowground biomass (BGB), and soil organic carbon were investigated in the growing seasons during 2014–2016. The average AGB and BGB in the growing seasons were 0.036 and 0.83 g cm−2, respectively. The AGB was significantly positively correlated with BGB, soil moisture, and soil organic carbon content, but was significantly negatively correlated with mean annual ground temperature and active-layer thickness, suggesting that permafrost degradation can potentially decrease vegetation growth. The BGB was positively correlated with active-layer thickness and was negatively correlated with soil moisture. This study suggests that permafrost degradation can decrease the soil moisture on the northern Qinghai-Tibetan Plateau and thus decrease AGB. The decreased soil moisture can also lead to lower BGB, while the vegetation in drier soils tends to have higher BGB to access more water resources for plant growth

Spatio-Temporal Characteristics and Differences in Snow Density between the Tibet Plateau and the Arctic

The Tibet Plateau (TP) and the Arctic are typically cold regions with abundant snow cover, which plays a key role in land surface processes. Knowledge of variations in snow density is essential for understanding hydrology, ecology, and snow cover feedback. Here, we utilized extensive measurements recorded by 697 ground-based snow sites during 1950–2019 to identify the spatio-temporal characteristics of snow density in these two regions. We examined the spatial heterogeneity of snow density for different snow classes, which are from a global seasonal snow cover classification system, with each class determined from air temperature, precipitation, and wind speed climatologies. We also investigated possible mechanisms driving observed snow density differences. The long-term mean snow density in the Arctic was 1.6 times that of the TP. Slight differences were noted in the monthly TP snow densities, with values ranging from 122 ± 29 to 158 ± 52 kg/m3. In the Arctic, however, a clear increasing trend was shown from October to June, particularly with a rate of 30.3 kg/m3 per month from March to June. For the same snow class, the average snow density in the Arctic was higher than that in the TP. The Arctic was characterized mainly by a longer snowfall duration and deeper snow cover, with some areas showing perennial snow cover. In contrast, the TP was dominated by seasonal snow cover that was shallower and warmer, with less (more) snowfall in winter (spring). The results will be helpful for future simulations of snow cover changes and land interactions at high latitudes and altitudes