Interaction dynamics of multiple ecosystem services and abrupt changes of landscape patterns linked with watershed ecosystem regime shifts

Functional and structural regime shifts have been observed among many ecosystems. Understanding regime shifts in watershed ecosystems is crucial for landscape management and sustainable development. We propose the perspective that the relationship dynamics of ecosystem services (ESs) can reflect watershed ecosystem regime shifts. An assessment of the critical transitions in watershed landscape patterns is integrated to support the regime shift analysis. The downstream basin of the Nu-Salween River (NSR) was selected as the study area to demonstrate regime shifts occurring from 1999 to 2019. To detect the functional critical transition, changes in the relationships among various ESs, including in the habitat quality for biodiversity (HQ), carbon storage (CS), water yield (WY), soil conservation (SC) and grain production (GP), were revealed using time series correlation analysis. To identify the critical structural transitions of watershed, the Pettitt test and principal component analysis (PCA) were used to display changes in landscape patterns. The results showed that (1) WY and GP were key ESs that could define watershed stable states; (2) three states, the “coordinated state” from 1999 to 2008, the “transient state” from 2009 to 2013 and the “trade-off state” from 2014 to 2019, were identified in the downstream basin of the NSR; and (3) the watershed functional critical transition had a 1-year time lag with the structural critical transition. This research revealed the nonlinear dynamics of a complex system in watersheds, and the detection of regime shifts that integrate the interaction dynamics of ESs can better serve watershed management to cope with abrupt changes

Impacts of Human Activity Intensity on Ecosystem Services for Conservation in the Lhasa River Basin

Quantifying the impacts of human activities on ecosystems and ecosystem services is crucial for the sustainable development of ecosystems at the local scale. We used the InVEST model to quantify ecosystem services and the human footprint index to calculate the human activity intensity (HAI). We evaluated the spatial correlations and fitting relationships between HAI and the key ecosystem services for the Lhasa River basin. The results showed that the spatial patterns of the 4 ecosystem services exhibited obvious heterogeneity. Excluding soil retention, the other ecosystem services exhibited overall downward trends from 2000 to 2018. The overall trend in HAI was ascending, with an average slope of 0.11. The spatial correlations between HAI and the 4 ecosystem services exhibited statistically significant differences (P < 0.01). The curve fitting results showed that water conservation and soil retention consistently decreased as HAI increased. Carbon sequestration and habitat quality increased and then decreased as HAI increased. Our findings help to understand the spatial interactions between HAI and multiple ecosystem services, thereby contributing to the development of a general scientific framework for ecological protection and integrated management

Building Plane Segmentation Based on Point Clouds

Planes are essential features to describe the shapes of buildings. The segmentation of a plane is significant when reconstructing a building in three dimensions. However, there is a concern about the accuracy in segmenting plane from point cloud data. The objective of this paper was to develop an effective segmentation algorithm for building planes that combines the region growing algorithm with the distance algorithm based on boundary points. The method was tested on point cloud data from a cottage and pantry as scanned using a Faro Focus 3D laser range scanner and Matterport Camera, respectively. A coarse extraction of the building plane was obtained from the region growing algorithm. The coplanar points where two planes intersect were obtained from the distance algorithm. The building plane’s optimal segmentation was then obtained by combining the coarse extraction plane points and the corresponding coplanar points. The results show that the proposed method successfully segmented the plane points of the cottage and pantry. The optimal distance thresholds using the proposed method from the uncoarse extraction plane points to each plane boundary point of cottage and pantry were 0.025 m and 0.030 m, respectively. The highest correct rate and the highest error rate of the cottage’s (pantry’s) plane segmentations using the proposed method under the optimal distance threshold were 99.93% and 2.30% (98.55% and 2.44%), respectively. The F1 score value of the cottage’s and pantry’s plane segmentations using the proposed method under the optimal distance threshold reached 97.56% and 95.75%, respectively. This method can segment different objects on the same plane, while the random sample consensus (RANSAC) algorithm causes the plane to become over-segmented. The proposed method can also extract the coplanar points at the intersection of two planes, which cannot be separated using the region growing algorithm. Although the RANSAC-RG method combining the RANSAC algorithm and the region growing algorithm can optimize the segmentation results of the RANSAC (region growing) algorithm and has little difference in segmentation effect (especially for cottage data) with the proposed method, the method still loses coplanar points at some intersection of the two planes

Building Plane Segmentation Based on Point Clouds

Planes are essential features to describe the shapes of buildings. The segmentation of a plane is significant when reconstructing a building in three dimensions. However, there is a concern about the accuracy in segmenting plane from point cloud data. The objective of this paper was to develop an effective segmentation algorithm for building planes that combines the region growing algorithm with the distance algorithm based on boundary points. The method was tested on point cloud data from a cottage and pantry as scanned using a Faro Focus 3D laser range scanner and Matterport Camera, respectively. A coarse extraction of the building plane was obtained from the region growing algorithm. The coplanar points where two planes intersect were obtained from the distance algorithm. The building plane&rsquo;s optimal segmentation was then obtained by combining the coarse extraction plane points and the corresponding coplanar points. The results show that the proposed method successfully segmented the plane points of the cottage and pantry. The optimal distance thresholds using the proposed method from the uncoarse extraction plane points to each plane boundary point of cottage and pantry were 0.025 m and 0.030 m, respectively. The highest correct rate and the highest error rate of the cottage&rsquo;s (pantry&rsquo;s) plane segmentations using the proposed method under the optimal distance threshold were 99.93% and 2.30% (98.55% and 2.44%), respectively. The F1 score value of the cottage&rsquo;s and pantry&rsquo;s plane segmentations using the proposed method under the optimal distance threshold reached 97.56% and 95.75%, respectively. This method can segment different objects on the same plane, while the random sample consensus (RANSAC) algorithm causes the plane to become over-segmented. The proposed method can also extract the coplanar points at the intersection of two planes, which cannot be separated using the region growing algorithm. Although the RANSAC-RG method combining the RANSAC algorithm and the region growing algorithm can optimize the segmentation results of the RANSAC (region growing) algorithm and has little difference in segmentation effect (especially for cottage data) with the proposed method, the method still loses coplanar points at some intersection of the two planes

Energy Harvesting Performance of a Novel Nonlinear Quad-Stable Piezoelectric Energy Harvester with Only One External Magnet

Nonlinear multi-stable piezoelectric energy harvesters show broadband frequency spectra and excellent energy harvesting performance, owing to their high output power related to inter-well transitions. However, existing quad-stable piezoelectric energy harvesters contain too many structural parameters, which makes the systems clumsy, and increases the difficulties of dynamic analysis and structural optimization. Herein, a nonlinear quad-stable piezoelectric energy harvester, with only one external magnet, is proposed based on the magnetic force characteristics between a ring magnet and a rectangular magnet. Under selected structural parameters, as the magnet spacing increases, the stability characteristic of the harvester changes from quad-stability to bi-stability, and then to mono-stability. The transformation of the stability characteristic results from the changes in the variation rate of the vertical magnetic force. Subsequently, under the filtered Gaussian white noise within the frequency range of 0–120 Hz, the energy harvesting performance of the harvester is simulated by the classic fourth-order Runge-Kutta method. Simulation results show that the performance of the harvester under the quad-stable structural parameters is better than that under the bi-stable structural parameters, independent of whether the excitation acceleration is small or large. This result is related to the potential well characteristics under the quad-stable and bi-stable structural parameters. More specifically, the potential well depths under the quad-stable and bi-stable structural parameters are almost the same, but the distance between the two outer potential wells under the quad-stable structural parameters is larger than that under the bi-stable structural parameters. Finally, a fabricated prototype is used to measure the experimental performance of the harvester. The experimental data and the estimated data share the same trend. This study provides a new conception and technical method for the design, optimization, and application of quad-stable piezoelectric energy harvesters

The Role of Protected Areas in Mitigating Vegetation Disturbances on the Qinghai-Tibetan Plateau

Long-term vegetation dynamics with satellite observations can provide valuable insights into natural variation in ecosystems and quantify disturbances associated with external pressures. Monitoring vegetation dynamics within protected areas (PAs) is essential, given their crucial role in protecting biodiversity and maintaining ecosystem integrity. In this study, using the normalized difference vegetation index (NDVI) and Breaks For Additive Seasonal and Trend (BFAST)model, we detected vegetation dynamics especially abrupt changes inside nature reserves (NRs, the primary type of PAs) on the Qinghai-Tibetan Plateau from 2000 to 2020. We then applied the matching approach and postmatching regression to evaluate the effect of NRs on natural vegetation with average NDVI, NDVI slope, and the number of abrupt changes. Our results showed that 78.97% of the vegetation within NRs exhibited greening trends. In addition, 29.15% of the area inside of the NRs experienced 1 or more abrupt changes, with the major change type interrupted greening (15.96%), followed by greening to browning (6.27%) and browning to greening (4.00%). The NRs significantly reduced the frequency of disturbances, and older NRs also showed a higher value of average NDVI compared to those in matched unprotected areas. Postregression models indicated that vegetation in newer NRs tended to be more vulnerable to disturbances and stricter NR management could benefit vegetation enhancement. Our analysis offers a new approach to vegetation dynamic monitoring that considers short-term disturbances. The findings of this work can help better understand effectiveness of PAs on ecosystem protection and offer practical guidance to future PAs management

The relationship between soil physical properties and alpine plant diversity on Qinghai-Tibet Plateau

Through a large-scale research, we examined the heterogeneity of soil properties and plant diversity, as well as their relationships across alpine grassland types on Qinghai-Tibet Plateau. The soil pH and EC value increased with the constant deepening of the soil in all the three alpine grassland types which in order of absolute value in every soil layer were alpine desert steppe, alpine steppe and alpine meadow. Among the three grassland types, the alpine meadow possessed the highest SM but the lowest SBD. For plant diversity, alpine meadow was the highest, alpine desert steppe ranked the second and alpine steppe was the last. SM and SBD were the highest influential soil physical properties to species richness, but with opposite effects