Influence of Cutter Errors on Forming Accurate Variable Hyperbolic Circular Arc Tooth Trace Cylindrical Gears

The cutter error is an important factor in the accurate forming of the variable hyperbolic circular arc tooth trace (VH-CATT) cylindrical gears. Also, the study of the relationship between the cutter error and the forming of accurate teeth is beneficial for the gear modification design and the improvement in contact performance. Firstly, based on the principle of forming VH-CATT cylindrical gears, the sources of error in the tooth forming related to accuracy were analysed, including the errors in the cutter position and the cutter geometry, such as the error of rotation around the x-axis g, the error of rotation around the y-axis b, the error of translation along the x-axis Δx, the error of translation along the y-axis Δy, the error of translation along the z-axis Δz, the pressure angle error Δa and the tooth line radius error ΔRT. Next, based on the meshing theory and processing, an ideal tooth surface equation and a tooth surface equation with cutter errors were derived, and the tooth surface reconstruction was done. Then, the gear tooth thickness error was defined to characterize the accuracy of gear forming. Finally, the influences of the cutter error on the tooth thickness error and gear contact were investigated. The study shows that all cutter errors have certain influence on the tooth thickness error, contact area and load distribution; Δx has basically no effect on the gear tooth thickness error; Δx and g make the actual meshing point deviate from the middle section. The study content and the applied methods are helpful in the tooth surface error traceability, the counter-adjustment of the tooth surface processing and the modification design. This study provides also a basis for gear design and load-bearing contact analysis

Milling Machine Error Modelling and Analysis in the Machining of Circular-Arc-Tooth-Trace Cylindrical Gears

This paper is to study errors of a milling machine used for the machining of Circular-Arc-Tooth-Trace (CATT) cylindrical gears. A topological structure model of the milling machine is established using the lower numbered body array approach. Coordinate transformation matrices between adjacent parts of the milling machine are set up by the D-H homogeneous transformation matrix. Based on the topological structure model and transformation matrices, a comprehensive error model of a milling machine used for the machining of CATT gears is established. According to the error model, the angle errors of the milling point are linear combinations of the angle errors of moving parts, while the displacement errors of the milling point are related to displacement errors, the angle errors of moving parts, the adjustment parameters of machine tools, and the geometric parameters of the gear. Finally, the influence of the errors of the cutter head on the milling point errors in the machining of CATT gears are discussed. The research can provide a theoretical basis for the error analysis, precision design, and the geometric error compensation of a milling machine used for CATT gear machining

Exploring Bamboo Forest Aboveground Biomass Estimation Using Sentinel-2 Data

Bamboo forests, due to rapid growth and short harvest rotation, play an important role in carbon cycling and local economic development. Accurate estimation of bamboo forest aboveground biomass (AGB) has garnered increasing attention during the past two decades. However, remote sensing-based AGB estimation for bamboo forests is challenging due to poor understanding of the mechanisms between bamboo forest growth characteristics and remote sensing data. The objective of this research is to examine the remote sensing characteristics of on-year and off-year bamboo forests at different dates and their AGB estimation performance. This research used multiple Sentinel-2 data to explore AGB estimation of bamboo forests in Zhejiang Province, China, by taking into account the unique characteristics of on-year and off-year bamboo forest growth features. Combining field survey data and Sentinel-2 spectral responses (spectral bands and vegetation indices) and textural images, random forest was used to identify key variables for AGB estimation. The results show that (1) the on-year and off-year bamboo forests have considerably different spectral signatures, especially in the wavelengths between red edge 2 and near-infrared wavelength (NIR2) (740⁻865 nm), making it possible to separate on-year and off-year bamboo forests; (2) on-year bamboo forests have similar spectral signatures although AGB increases from as small as 40 Mgha−1 to as high as 90 Mgha−1, implying that optical sensor data cannot effectively model on-year bamboo AGB; (3) off-year bamboo AGB has significant relationships with red and shortwave infrared (SWIR) spectral bands in the April image and with red edge 2 in the July image, but the AGB saturation problem yields poor estimation accuracy; (4) stratification considerably improved off-year bamboo AGB estimation but not on-year, non-stratification using the April image is recommended; and (5) Sentinel-2 data cannot solve the bamboo AGB data saturation problem when AGB is greater than 70 Mgha−1, similar to other optical sensor data such as Landsat. More research should be conducted in the future to integrate multiple sources—remotely sensed data (e.g., lidar, optical sensor data) and ancillary data (e.g., soil, topography)—into AGB modeling to improve the estimation. The use of very high spatial resolution images that can effectively extract tree density information may improve bamboo AGB estimation and yield new insights

Integration of Landsat time-series vegetation indices improves consistency of change detection

Vegetation indices (VIs) were used to detect when and where vegetation changes occurred. However, different VIs have different or even diametrically opposite results, which obstructed the in-depth understanding of vegetation change. Therefore, this study examined the spatial and temporal consistency of five VIs (EVI; NBR; NDMI; NDVI; and NIRv) in detecting abrupt and gradual vegetation changes, and provided an ensemble algorithm which integrated the change detection results of the five indices to reduce the uncertainty of change detection using a single index. The spatial consistency of the five indices in abrupt change detection accounted for 50.6% of the study area, but the temporal consistency was low (21.6%). Wetness indices (NBR, NDMI) were more sensitive to negative abrupt changes, greenness indices (EVI, NDVI, NIRv) were more sensitive to positive abrupt changes; and both types of indices were similar in detecting gradual and total changes. The overall accuracy of the ensemble method was 81.60% and higher than that of any single index in abrupt change detection. This study provides a comprehensive evaluation of the spatial and temporal inconsistencies of change detection in model-fitting errors and various types of vegetation changes. The proposed ensemble method can support robust change-detection

Examining Vegetation Change and Associated Spatial Patterns in Wuyishan National Park at Different Protection Levels

Examining the characteristics of vegetation change and associated spatial patterns under different protection levels can provide a scientific basis for national park protection and management. Based on the dense time-series Landsat enhanced vegetation index (EVI) data between 1986 and 2020, we utilized the Wild Binary Segmentation (WBS) approach to detect spatial and temporal characteristics of abrupt, gradual, and total changes in Wuyishan National Park. The differences in vegetation change in three protection-level areas (strictly protected [Prots], generally protected [Prot], and non-protected [NP]) were examined, and the contributions to their spatial patterns were evaluated through Geodetector. The results showed the following: (1) The highest percentage of area without abrupt change was in Prots (39.89%), and the lowest percentage was in NP (17.44%). The percentage of abrupt change frequency (larger than three times) increased from 4.40% to 9.10% and 12.49% with the decreases in protection. The significance test showed that the difference in changed frequencies was not significant among these regions, but the interannual variation of abrupt change in Prots was significantly different from other areas. (2) The vegetation coverage of the Wuyishan National Park generally improved. The total EVI change (TEVI) showed that the positive percentage of Prots and Prot was 90.43% and 91.71%, respectively, slightly higher than that of NP (88.44%). However, the mean greenness change of NP was higher than that of Prots and Prot. (3) The park’s EVI spatial pattern in 1986 was the strongest factor determining the EVI spatial pattern in 2020; the explanatory power reduced as the protection level decreased. The explanation power (q value) of abrupt vegetation change was lower and increased as the protection level decreased. The interaction detection showed that EVI1986 and TEVI had the strongest explanatory powers, but the explanatory ability gradually weakened from 0.713 to 0.672 to 0.581 in Prots, Prot, and NP, respectively. This study provided a systematic analysis of vegetation changes and their impacts on spatial patterns

Examining the Effects of Soil and Water Conservation Measures on Patterns and Magnitudes of Vegetation Cover Change in a Subtropical Region Using Time Series Landsat Imagery

Soil and water erosion has long been regarded as a serious environmental problem in the world. Thus, research on reducing soil erosion has received continuous attention. Different conservation measures such as restoring low-function forests, closing hillsides for afforestation, planting trees and grass, and constructing terraces on slope land have been implemented for controlling soil erosion problems and promoting vegetation cover change. One important task is to understand the effects of different conservation measures on reducing water and soil erosion problems. However, directly conducting the evaluation of soil erosion reduction is difficult. One solution is to evaluate the patterns and magnitudes of vegetation cover change due to implementing these measures. Therefore, this research selected Changting County, Fujian Province as a case study to examine the effects of implementing conservation measures on vegetation cover change based on time series Landsat images and field survey data. Landsat images between 1986 and 2021 were used to produce time series vegetation cover data using the Google Earth Engine. Sentinel-2 images acquired in 2021 and Landsat images in 2010 were separately used to develop land cover maps using the random forest method. The spatial distribution of different conservation measures was linked to annual vegetation cover and land cover change data to examine the effects on the change in vegetation cover. The results showed a significant reduction in bare lands and increase in pine forests. The vegetation coverage increased from 42% in 1986 to 79% in 2021 in the conservation region compared with an increase from 73% to 87% in the non-conservation region during the same period. Of the different conservation measures, the change magnitude was 0.44 for restoring low-function forests and closing hillsides for afforestation and 0.65 for multiple control measures. This research provides new insights in terms of understanding the effects of taking proper measures for reducing soil and water erosion problems and provides scientific results for decisionmaking for soil erosion controls. The strategy and method used in this research are valuable for other regions in understanding the roles of different conservation measures on vegetation cover change and soil erosion reduction through employing remote sensing technologies

Examining Vegetation Change and Associated Spatial Patterns in Wuyishan National Park at Different Protection Levels

Examining the characteristics of vegetation change and associated spatial patterns under different protection levels can provide a scientific basis for national park protection and management. Based on the dense time-series Landsat enhanced vegetation index (EVI) data between 1986 and 2020, we utilized the Wild Binary Segmentation (WBS) approach to detect spatial and temporal characteristics of abrupt, gradual, and total changes in Wuyishan National Park. The differences in vegetation change in three protection-level areas (strictly protected [Prots], generally protected [Prot], and non-protected [NP]) were examined, and the contributions to their spatial patterns were evaluated through Geodetector. The results showed the following: (1) The highest percentage of area without abrupt change was in Prots (39.89%), and the lowest percentage was in NP (17.44%). The percentage of abrupt change frequency (larger than three times) increased from 4.40% to 9.10% and 12.49% with the decreases in protection. The significance test showed that the difference in changed frequencies was not significant among these regions, but the interannual variation of abrupt change in Prots was significantly different from other areas. (2) The vegetation coverage of the Wuyishan National Park generally improved. The total EVI change (TEVI) showed that the positive percentage of Prots and Prot was 90.43% and 91.71%, respectively, slightly higher than that of NP (88.44%). However, the mean greenness change of NP was higher than that of Prots and Prot. (3) The park’s EVI spatial pattern in 1986 was the strongest factor determining the EVI spatial pattern in 2020; the explanatory power reduced as the protection level decreased. The explanation power (q value) of abrupt vegetation change was lower and increased as the protection level decreased. The interaction detection showed that EVI1986 and TEVI had the strongest explanatory powers, but the explanatory ability gradually weakened from 0.713 to 0.672 to 0.581 in Prots, Prot, and NP, respectively. This study provided a systematic analysis of vegetation changes and their impacts on spatial patterns

Retrieval of eucalyptus planting history and stand age using random localization segmentation and continuous land-cover classification based on Landsat time-series data

Obtaining robust change-detection results and reconstructing planting history are important bases for conducting forest resource monitoring and management. The existence of multiple change points in a very short period can lead to a global segmentation method incorrectly locate the change points, because they could impact each other during model initialization. This is especially true for monitoring plantations such as eucalyptus, which has a unique growth cycle with short rotation periods and frequent disturbances. In this study, we proposed a method to find critical change points in a normalized difference vegetation index (NDVI) time series by combining random localization segmentation and the Chow test. Features of the NDVI time series calculated on the divided segments and change points were used to train a Random Forest classifier for continuous land-cover classification. The proposed method was successfully applied to a eucalyptus plantation for identifying the management history, including harvest time, generation, rotation cycle, and stand age. The results show that our method is robust for different lengths of NDVI time series, and can detect short-interval (cut and stability) change points more accurately than the global segmentation method. The overall accuracy of identification was 80.5%, and successive generations in 2021 were mainly first- and second-generation, accounting for 69.0% and 27.9% of the total eucalyptus area, respectively. The rotation cycle of eucalyptus plantation is usually 5–8 years for 66.9% of the total area. The eucalyptus age was accurately estimated with an R2 value of 0.91 and RMSE of 13.3 months. One-year-old eucalyptus plantations accounted for the highest percentage of 14.5%, followed by seven-year-old plantations (12.9%). This study provides an important research basis for accurately monitoring the rotation processes of short-period plantations, assessing their timber yield and conducting carbon- and water-cycle research

Examining Forest Disturbance and Recovery in the Subtropical Forest Region of Zhejiang Province Using Landsat Time-Series Data

Detection of forest disturbance and recovery has received much attention during the last two decades due to its important influence on forest carbon budget estimation. This research used Landsat time-series data from 1984 to 2015 to examine forest disturbance and recovery in a subtropical region of eastern Zhejiang Province, China, through the LandTrendr algorithm. Field inventory data and high spatial resolution images were used to evaluate the disturbance and recovery results. This research indicates that high producer and user accuracies for both disturbance and recovery classes were obtained and three levels of disturbance and recovery each can be detected. Through incorporation of climate data and disturbance results, drought events also can be successfully detected. More research is needed to incorporate multisource data for detection of forest disturbance types in subtropical regions

Quantifying annual land-cover change and vegetation greenness variation in a coastal ecosystem using dense time-series Landsat data

Land-cover change may affect water and carbon cycles when transitioning from one land-cover category to another (land-cover conversion, LCC) or when the characteristics of the land-cover type are altered without changing its overall category (land-cover modification, LCM). Given the increasing availability of time-series remotely sensed data for earth monitoring, there has been increased recognition of the importance of accounting for both LCC and LCM to study annual land-cover changes. In this study, we integrated 1,513 time-series Landsat images and a change-updating method to identify annual LCC and LCM during 1986–2015 in the coastal area of Zhejiang Province, China. The purpose was to quantify their contributions to land-cover changes and impacts on the amount of vegetation. The results show that LCC and LCM can be successfully distinguished with an overall accuracy of 90.0%. LCM accounted for 22% and 40.5% of the detected land-cover changes in reclaimed and inland areas, respectively, during 1986–2015. In the reclaimed area, LCC occurred mostly in muddy tidal flats, construction land, aquaculture ponds, and freshwater herbaceous land, whereas LCM occurred mostly in freshwater herbaceous land, Spartina alterniflora, and muddy tidal flats. In the inland area, both LCC and LCM were concentrated in forest and dryland. Overall, LCC had a mean magnitude of normalized difference vegetation index (NDVI) change similar to that of LCM. However, LCC had a positive effect and LCM had a negative effect on NDVI change in the reclaimed area. Both LCC and LCM in the inland area had negative impacts on vegetation greenness, but LCC resulted in larger NDVI change magnitude. Impacts of LCC and LCM on vegetation greenness were quantified for each land-cover type. This study provided a methodological framework to take both LCC and LCM into account when analyzing land-cover changes and quantified their effects on coastal ecosystem vegetation