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Currently, tapping is the only method of obtaining natural rubber, an essential industrial raw material. The stability of the labor force supply for tapping is crucial for the sustainable development of the natural rubber industry. As China's second-largest rubber planting area, Hainan Island had a planting area of 585,800 hectares in 2020, accounting for 45% of the country's total, with over 2 million workers. Labor force shortages and the aging phenomenon in tapping have become increasingly severe and have attracted significant attention. However, the related research lacks detailed spatial information for conversion estimation, which hinders macroeconomic regulation. This study is based on the 2020 Hainan Island natural rubber distribution map, Dynamic World land use classification products, and the WorldPop population distribution dataset. It employs multiple technical operations such as temporal and spatial filtering, removal of small patches, buffer parameter settings, and masking to obtain the 2020 potential tapping labor density map for Hainan's rubber planting areas. Spatial statistical methods are used to analyze the spatial distribution and age structure characteristics of potential tapping labor force in Hainan's rubber planting areas as well as the spatial distribution of labor force shortage degrees after conversion to actual tapping labor force, including the labor force shortage levels and areas for each city and county, revealing the spatial pattern. The results reveal the following: 1) The total number of potential tapping laborers in Hainan's rubber planting areas is 1.567 4 million, with a spatial distribution pattern of high in the north and low in the south. The proportion of the population aged 45~59 years is 34%, indicating a significant aging trend. 2) There are significant spatial differences in the degree of labor shortage. The primary shortage areas are mainly concentrated in the junction areas of the central-western and central-eastern counties, with a belt-like distribution. In contrast, the secondary and tertiary shortage areas are widely distributed in the central-western, central, and some central-eastern county junctions, with shortage in the central area being particularly severe, showing a contiguous distribution. 3) The primary shortage areas in Danzhou City, Baisha County, and Qiongzhong County rank in the top three, with a cumulative area of 50,500 hectares. Baisha, Qiongzhong, Chengmai, and Ledong Counties are also prominent in the secondary and tertiary shortage areas, covering a total area of 23,300 hectares. In total, the area of labor shortage regions in Hainan Island is 141,700 hectares, necessitating a future focus. To address the labor force shortage issue in tapping, it is recommended to strengthen infrastructure construction in the rubber plantations within the shortage areas and actively promote new tapping methods (e.g., low-frequency tapping) or tools (e.g., electric tapping knives) to reduce the labor intensity of tapping and increase the number of trees tapped per capita. This will provide favorable support for the sustainable and healthy development of Hainan's natural rubber industry. By revealing the spatial distribution characteristics and degree of tapping labor shortage, this study aims to provide robust decision-making support for the Hainan provincial government in addressing employment issues of tapping labor, optimizing human resource management strategies, and promoting the sustainable development of the natural rubber industry.

Population urbanization is the core of new urbanization in China and is an important task in the long term. Based on mediation effect models, spatial panel data econometric models, and coupling coordination models, this study attempts to identify the direct and indirect effects of the proportion of rubber-planted areas and the indications of population urbanization using panel data of counties on Hainan Island, explaining the mechanism. The results show that rubber plantations dominated by state-owned farms weakly impacted farmers' income in 1992-2002 (first phrase), and the effects became positive in the period of smallholding expansions from 2003 to 2013 (second phrase) at a statistically significant level of 10%. However, the effects became negative between 2013 and 2020 (third phrase) due to the continuously low prices of natural rubber compared to the second phrase; the regions with higher share of rubber plantations had less local fiscal revenues per capita—especially in the third phrase—mainly because of the short industrial chain of rubber in producing regions. Shares of rubber-planted areas in the city or county (defined as rubber-planted intensity, RPI) were negatively related to proportions of staff in the non-private sector (SNS) and nonrural employees (NRE) compared to people with jobs and census registered population urbanization rates (PUR). The reversed connections between RPI and NRE became weaker over time; a 10-percentage point increase in PRI might have led to a 10.3-percentage point decrease in NRE in the first phrase, but it reduced to 3.3 percentage points in NRE with no statistical significance in the third phrase. Conversely, the negative correlation of RPI with SNS and PUR became stronger from the first to third phrase; the farmers' income and local fiscal revenue could ease the negative relationships between RPI and SNS or NRE, but they turned to enhance the relation of RPI with PUR in the second and third phrase. The evidence from Moran's I and Geary's c indices proved the existence of obvious spatial correlations in the distribution of natural rubber and farmers' income and indicated that GDP per capita and other variables are also spatially related. The estimated spatial panel data models with adjacent, geographical and economical distance matrices indicated that the direct influences of RPI on SNS, NRE, and PUR were almost negative in all phrases, but the spillover and total effects can be positive in some situations, such as in the second phrase with adjacent and geographical distance matrices of PUR; this means that an increase in rubber-planted areas may promote nonfarm job market of adjacent regions mainly due to rural labor mobility. The coupling coordination degrees between the intensity of rubber planted and population urbanization of Haikou and Sanya reach 0.96 and 0.87, respectively, in the second and third phrases—much higher than those of other regions. Compared to the first phrase, the margin of increases in the degrees of coupling coordination in the second and third phrases are reducing from the east to west in Hainan Island. The key routes to mitigate the negative links are to improve labor productivity and extend the industrial chain of natural rubber along with the nonfarm job creation, and Hainan's population urbanization should consider many other possible angles across the island.

Terrestrial gross primary productivity (GPP) is an important flux that drives the global carbon cycle. However, quantifying the trend and the control factor of GPP from the pixel level to the regional level is still a challenge. We generated monthly GPP dataset using the vegetation photosynthesis model and calculated the interannual linear trend for China during 2000-2016. The Breaks For Additive Seasonal and Trend method was applied to detect the timing of breakpoint and trends shift of monthly GPP, while boosted regression tree analysis was used to identify the most important factor and its relative influence on GPP based on gridded leaf area index (LAI), aerosol optical thickness, and NCEP-DOE Reanalysis II meteorological data. The results show that annual mean GPP was significantly (P < 0.001, R 2 = 0.78) increased, especially in the Loess Plateau and South China, from 2000 to 2016. The change rate of annual mean GPP declined from 18.82 g C m-2 yr-1 in 2000-2008 to 3.48 g C m-2 yr-1 in 2008-2016. About 55.4% of the breakpoints occur between 2009 and 2011 and was mainly distributed in Qinghai-Tibet Plateau, Central China, Southwestern China, and South China, and negative oriented GPP trends variation type still accounts for about 28.76%. LAI and temperature related factors generally had the highest relative influence on GPP in the north part and south part of China, respectively. Our study indicates that the ecological restoration projects and rapid urbanization have respectively induced the most obvious increase and decrease trends of GPP in China. Land cover change and climate change are the main reasons for GPP dynamics in the north and south part of China, respectively.

Leaf area index (LAI) is an important index to describe the growth status and canopy structure of vegetation, is of great theoretical and practical significance to quickly obtain LAI of large area vegetation and crops for ecosystem science research and agricultural & forestry production guidance. In this study, the typical tropical crop rubber tree in Hainan Island was selected as the research area, the LAI estimation model of rubber plantation based on satellite remote sensing vegetation indices was constructed, and its spatiotemporal variation was analyzed. The results showed that, compared with correlations between LAI and the indices of normalized difference vegetation index (NDVI), green NDVI (GNDVI), ratio vegetation index (RVI) and wide dynamic range vegetation index (WDRVI), correlations were higher between LAI and the indices of enhanced vegetation index (EVI), soil adjusted vegetation index (SAVI), difference vegetation index (DVI) and modified soil adjusted vegetation index (MSAVI). Among the LAI estimation models based on different vegetation indices (linear, exponential and logarithmic models), the linear estimation model based on EVI index was the best, and its coefficient of determination (R2) was 0.69. The accuracy of LAI estimation model was high. The linear fitting R2 of observed and simulated LAI was 0.67, the root mean square error (RMSE) was 0.16, and the average relative error (RE) was -0.25%. However, there was underestimation in the middle value and overestimation in the high and low value area of LAI. The high LAI values (4.40－6.23) were mainly distributed in Danzhou and Baisha in the west of Hainan Island, the middle LAI values (3.80－4.40) were mainly distributed in Chengmai, Tunchang and Qiongzhong in the middle of Hainan Island, and the low LAI values (2.69－3.80) were mainly distributed in Ding'an, Qionghai, Wanning, Ledong and Sanya in the east and south of Hainan Island. In summary, the linear estimation model for rubber plantation LAI based on EVI index obtained high accuracy, and has good values of popularization and appliance.

Extensive forest changes have occurred in monsoon Asia, substantially affecting climate, carbon cycle and biodiversity. Accurate forest cover maps at fine spatial resolutions are required to qualify and quantify these effects. In this study, an algorithm was developed to map forests in 2010, with the use of structure and biomass information from the Advanced Land Observation System (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) mosaic dataset and the phenological information from MODerate Resolution Imaging Spectroradiometer (MOD13Q1 and MOD09A1) products. Our forest map (PALSARMOD50 m F/NF) was assessed through randomly selected ground truth samples from high spatial resolution images and had an overall accuracy of 95%. Total area of forests in monsoon Asia in 2010 was estimated to be ~6.3 × 10(6 )km(2). The distribution of evergreen and deciduous forests agreed reasonably well with the median Normalized Difference Vegetation Index (NDVI) in winter. PALSARMOD50 m F/NF map showed good spatial and areal agreements with selected forest maps generated by the Japan Aerospace Exploration Agency (JAXA F/NF), European Space Agency (ESA F/NF), Boston University (MCD12Q1 F/NF), Food and Agricultural Organization (FAO FRA), and University of Maryland (Landsat forests), but relatively large differences and uncertainties in tropical forests and evergreen and deciduous forests.

The precise acquisition of phenotypic parameters for individual trees in plantation forests is important for forest management and resource exploration. The use of Light-Detection and Ranging (LiDAR) technology mounted on Unmanned Aerial Vehicles (UAVs) has become a critical method for forest resource monitoring. Achieving the accurate segmentation of individual tree crowns (ITCs) from UAV LiDAR data remains a significant technical challenge, especially in broad-leaved plantations such as rubber plantations. In this study, we designed an individual tree segmentation framework applicable to dense rubber plantations with complex canopy structures. First, the feature extraction module of PointNet++ was enhanced to precisely extract understory branches. Then, a graph-based segmentation algorithm focusing on the extracted branch and trunk points was designed to segment the point cloud of the rubber plantation. During the segmentation process, a directed acyclic graph is constructed using components generated through grey image clustering in the forest. The edge weights in this graph are determined according to scores calculated using the topologies and heights of the components. Subsequently, ITC segmentation is performed by trimming the edges of the graph to obtain multiple subgraphs representing individual trees. Four different plots were selected to validate the effectiveness of our method, and the widths obtained from our segmented ITCs were compared with the field measurement. As results, the improved PointNet++ achieved an average recall of 94.6% for tree trunk detection, along with an average precision of 96.2%. The accuracy of tree-crown segmentation in the four plots achieved maximal and minimal R2 values of 98.2% and 92.5%, respectively. Further comparative analysis revealed that our method outperforms traditional methods in terms of segmentation accuracy, even in rubber plantations characterized by dense canopies with indistinct boundaries. Thus, our algorithm exhibits great potential for the accurate segmentation of rubber trees, facilitating the acquisition of structural information critical to rubber plantation management. [ABSTRACT FROM AUTHOR]

Short-rotation eucalyptus plantations play a key positive role in the forestry economy due to their fast-growing and high-yielding properties. However, some studies have suggested that eucalyptus plantations introductions may have negative impacts on biodiversity and ecosystems' stability. In order to precisely and promptly determine the influence of eucalyptus plantations on soil characteristics and hydrological processes, based on the rotation change rules of eucalyptus plantations, this study combined the continuous change detection and classification and spectral mixture analysis (CCDC-SMA) algorithm and the random forest (RF) algorithm to map the distribution and stand age of short-rotation eucalyptus plantations in Hainan Island. First, the forest distribution map was used to mask out the rubber plantations, and forest disturbances were extracted through the CCDC-SMA algorithm to determine the potential short-rotation eucalyptus plantations distribution. Second, using CCDC-SMA algorithm fitting coefficients, field surveys, original spectral bands, vegetation indices, and digital elevation models (DEM) as inputs to the RF algorithm, short-rotation eucalyptus plantations distribution maps were created and evaluated based on Google Earth images. Finally, the stand age of the newly mapped short-rotation eucalyptus plantations was estimated based on the breakpoints of the CCDC-SMA algorithm. The results showed that the producer, user, and overall accuracies of the 2022 short-rotation eucalyptus plantations map were estimated at 0.95, 0.95, and 0.94, respectively, and the validation R2 of the estimated stand ages was at 0.97. The eucalyptus plantations in Hainan Island had a total area of roughly 9.93 × 104 ha in 2022. Danzhou City had the highest planting area of eucalyptus plantations, followed by Changjiang County, Chengmai County, and Lingao County. It was worth noting that the eucalyptus plantations were mostly located in places with low altitudes (<200 m) and flat slopes (<10°). Approximately 43.91% of eucalyptus plantations were located in the three major watersheds. In addition, the 1-year-old eucalyptus plantations accounted for the highest areal percentage of 30.58%. These datasets are valuable tools to aid sustainable production, ecological assessment, and conservation of eucalyptus plantations. [ABSTRACT FROM AUTHOR]

Rubber (Hevea brasiliensis Muell.) plantations are among the most critical agricultural ecosystems in tropical regions, playing a vital role in regional carbon balance. Accurate large-scale biomass estimation for these plantations remains a challenging task due to the severe signal saturation problem. Recent advances in remote sensing big data, cloud platforms, and machine learning have facilitated the precise acquisition of key physiological variables, such as stand age (A) and canopy height (H), which are critical parameters for biomass estimation but have been underutilized in prior studies. Using Hainan Island—the second-largest rubber planting base in China—as a case study, we integrated extensive ground surveys, maps of stand age and canopy height, remote sensing indicators (RSIs), and geographical and climate indicators (ECIs) to ascertain the optimal method for estimating rubber plantation biomass. We compared different inputs and estimation approaches (direct and indirect) using the random forest algorithm and analyzed the spatiotemporal characteristics of rubber plantation biomass on Hainan Island. The results indicated that the traditional model (RSIs + ECIs) had low accuracy and significant estimation bias (R2 = 0.24, RMSE = 38.36 mg/ha). The addition of either stand age or canopy height considerably enhance model accuracy (R2 = 0.77, RMSE ≈ 21.12 mg/ha). Moreover, incorporating the DBH obtained through indirect inversion yielded even greater predictive accuracy (R2 = 0.97, RMSE = 7.73 mg/ha), outperforming estimates derived from an allometric equation model input with the DBH (R2 = 0.67, RMSE = 25.43 mg/ha). However, augmenting the model with stand age, canopy height, or their combination based on RSIs, ECIs, and DBH only marginally improved the accuracy. Consequently, it is not recommended in scenarios with limited data and computing resources. Employing the optimal model, we generated biomass maps of rubber plantations on Hainan Island for 2016 and 2020, revealing that the spatiotemporal distribution pattern of the biomass is closely associated with the establishment year of the rubber plantations. While average biomass in a few areas has undergone slight decreases, total biomass has exhibited significant growth, reaching 5.46 × 107 mg by the end of 2020, underscoring its considerable value as a carbon sink. [ABSTRACT FROM AUTHOR]

Forest aboveground biomass (AGB) is an important research topic in the field of forestry, with implications for carbon cycles and carbon sinks. Malania oleifera Chun et S. K. Lee (M. oleifera) is a valuable plant species that is listed on the National Second-Class Protected Plant checklist and has received global attention for its conservation and resource utilization. To obtain accurate AGB of individual M. oleifera trees in a fast, low-finance-cost and low-labor-cost way, this study first attempted to estimate individual M. oleifera tree AGB by combining the centimeter-level resolution RGB imagery derived from unmanned aerial vehicles (UAVs) and the deep learning model of Mask R-CNN. Firstly, canopy area (CA) was obtained from the 3.5 cm high-resolution UAV-RGB imagery using the Mask R-CNN; secondly, to establish an allometric growth model between the diameter at breast height (DBH) and CA, the correlation analysis of both was conducted; thirdly, the AGB estimation method of individual M. oleifera trees was presented based on an empirical equation. The study showed that: (1) The deep learning model of Mask R-CNN achieved an average segmentation accuracy of 90% in the mixed forests to the extraction of the canopy of M. oleifera trees from UAV-RGB imagery. (2) The correlation between the extracted CA and field-measured DBH reached an R2 of 0.755 (n = 96). (3) The t-test method was used to verify the predicted and observed values of the CA-DBH model presented in this study, and the difference in deviation was not significant (p > 0.05). (4) AGB of individual M. oleifera was estimated for the first time. This study provides a reference method for the estimation of individual tree AGB of M. oleifera based on centimeter-level resolution UAV-RGB images and the Mask R-CNN deep learning. [ABSTRACT FROM AUTHOR]