Annual land use land cover (LULC) change information at medium spatial resolution (i.e. at 30 m) is required in numerous subjects, such as biophysical modelling, land management and global change ...studies. Annual LULC information, however, is usually not available at continental or national scale due to reasons such as insufficient remote sensing data coverage or lack of computational capabilities. Here we integrate high temporal resolution and coarse spatial resolution satellite images (i.e., Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Inventory Modelling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI)) with high spatial resolution datasets (China’s Land-Use/cover Datasets (CLUDs) derived from 30-meter Landsat TM/ETM+/OLI) to generate reliable annual nominal 30 m LULC maps for the whole of China between 1980 and 2015. We also test the performance of a statistical based change detection algorithm (Breaks for Additive Seasonal and Trend), originally designed for tracking forest change, in classifying all-type LULC change. As a result, a nominal 30 m annual land use/land cover datasets (CLUD-A) from 1980 to 2015 was developed for the whole China. The mapping results were assessed with a change sample dataset, a regional annual validation sample set and a three-year China sample set. Of the detected change years, 75.61% matched the exact time of conversion within ±1 year. Annual mapping results provided a detail process of urbanization, deforestation, afforestation, water and cropland dynamics over the past 36 years. The consistent characterization of land change dynamics for China can be further used in scientific research and to support land management for policy-makers.
Accurate and up-to-date land use land cover (LULC) mapping has long been a challenge in Africa. Recently, three LULC maps with moderate spatial resolution (20 m to 100 m) have been developed using ...multiple Earth observation datasets for 2015-2016 for the whole continent, which provide unprecedented spatial detail of the land surface for Africa. This study aimed to compare these three recent African LULC maps (i.e. the Copernicus Global Land Service Land Cover (CGLS-LC100, 100 m), European Space Agency Sentinel-2A Land Cover (ESA-S2-LC20, 20 m) and Finer Resolution Observation and Monitoring of Global Land Cover for Africa version 2 (FROM-GLC-Africa30, 30 m)) using a validation sample set and statistics from the FAO. The results indicated that the accuracy of the three datasets was unevenly distributed in spatial extent and area estimation. All the three datasets achieve an accuracy of above 60% and the fraction layer of CGLS-LC100 showed the best consistency with FAO statistics in the area. However, great disagreement in spatial details was found among three products, with 43.12% of the total area in Africa was in low agreement. The LULC mapping regions with the highest uncertainty were southeast Africa, the Sahel region and the Eastern Africa Plateau. Uncertainty was most closely related to elevation and precipitation changes along latitude/longitude.
Plantation is an important land use type that differs from natural forests and affects the economy and the environment. Tree age is one of the key factors used to quantify the impact of plantations. ...However, there is a lack of datasets explicitly documenting the planting years of global plantations. Here we used time-series Landsat archive from 1982 to 2020 and the LandTrendr algorithm to generate global maps of planting years based on the global plantation extent products in Google Earth Engine (GEE) platform. The datasets developed in this study are in a GeoTIFF format with 30-meter spatial resolution by recording gridded specie types and planting years of global plantations. The derived dataset could be used for yield prediction of tree crops and social and ecological cost-benefit analysis of plantations.
Intervertebral disc degenerative disease (IDD), which usually causes lower back and neck pain, is one of the most widespread musculoskeletal disorders and often causes a low quality of life. However, ...the surgical and conservative treatments commonly used in clinical practice are not effective. Previous studies have identified curcumin (Cur) as a potential therapeutic agent. However, its development in this regard has been limited due to its low dissolution, instability in water, and rapid metabolism. In this study, we developed a novel anti-inflammatory composite hydrogel scaffold with curcumin encapsulated in solid lipid nanoparticles and mixed it with gelatin methacrylate (GelMA) hydrogel to treat IDD. The hydrogel scaffold, denoted Cur-solid lipid nanoparticles (SLNs)/GelMA, promoted the restoration of Collagen type II (Col II) and aggrecan expression levels in vivo, indicating that the regeneration of the intervertebral discs was effective. Combined in vitro studies showed that Cur-SLNs inhibited the expression of the inflammatory factors TNF-α and IL-6. Additionally, immunofluorescence and western blotting experiments verified that Cur-SLNs regulated the recovery of Col II and aggrecan in an inflammatory environment and promoted the metabolic homeostasis of the extramedullary cell matrix. In conclusion, this study provides a new strategy to promote IDD regeneration, which brings new application prospects.
Article highlights
The combination of Cur encapsulated with SLNs and GelMA hydrogel in the treatment of IDD can reestablish the metabolic homeostasis of the pathologic process and reduce the inflammatory response.
This novel anti-inflammatory composite hydrogel scaffold can facilitate the repair of IDD and offers new perspectives for the treatment of IDD.
Intervertebral disc (IVD) degeneration (IDD) has become a global health issue; however, effective treatment remains undeveloped. Although the potential curative effect of resveratrol (Res) on IDD has ...been reported, the explosive release and rapid disappearance of Res in lesions seriously limit its use. In this study, Res was loaded into solid lipid nanoparticles (SLNs) by emulsification and cryogenic coagulation, and Res–SLNs/gelatin methacryloyl (GelMA) composite hydrogel scaffolds were designed by GelMA hydrogel encapsulation to improve the stability of therapeutic disc degeneration. In vitro studies demonstrated that Res–SLNs can inhibit nucleus pulposus (NP; major IVD cell) apoptosis by upregulating the expression of anabolic proteins. In vivo studies showed that the Res–SLNs/GelMA hydrogel scaffold improved the pinning-induced IDD model in rats and restored the stability of the IVD extracellular matrix (ECM). Our experiments consistently show that implantation of this scaffold can improve the inflammatory microenvironment, reduce the degeneration of NP cells, and reinforce the disc function repair effect. Therefore, the Res–SLNs/GelMA hydrogel scaffold has great application prospects for treating IDD.
We report the world＇s first all-season training and validation sample sets for global land cover classification with Landsat-8 data. Prior to this, such samples were only available at a single date ...primarily from the growing season. It is unknown how much limitation such a single-date sample has to mapping global land cover in other seasons of the year. To answer this question, we selected available Landsat-8 images from four seasons and collected training and validation samples from them. We compared the performances of training samples in different seasons using Random Forest algorithm. We found that the use of training samples from any individual season would result in the best overall classification accuracy when validated by samples in the same season. The global overall accuracy from combined best seasonal results was 67.2% when classifying the 11 Level-1 classes in the Finer Resolution Observation and Monitoring of Global Land Cover （FROM-GLC） classification system. The use of training samples from all seasons （named all-season training sample set hereafter） produced an overall accuracy of 67.0%. We also tested classification within 10° latitude 60° longitude zones using all-season training subsample within each zone and obtained an overall accuracy of 70.2%. This indicates that properly grouped subsamples in space can help improve classification accuracies. All the results in this study seem to suggest that it is possible to use an all-season training sample set to reach global optimality with universal applicability in classifying images acquired at any time of a year for global land cover mapping.
Laser powder bed fusion (L-PBF) enables the fast fabrication of pure nickel parts with complex structures. Tribological behaviors of the printed pure nickel are crucial to its application, and highly ...dependent on the volumetric energy density (VED), due to the variation of wear mechanisms. In this study, to investigate the wear mechanisms and evaluate the tribological behaviors of L-PBF pure nickel, samples fabricated with different VEDs were tested by a ball-on-disc tribometer under different representative loads. Unlike tribofilms formed on casting samples during sliding, L-PBF samples suffer abrasive wear, and their wear resistance decreases initially and increases afterwards with increasing VED. It suggests that the preferred VED provide good densification behaviors, high microhardness, and sufficient thin columnar subgrains, which decrease the abrasive wear. As a result, the wear resistance of the preferred L-PBF pure nickel can outperform cast counterparts under a load close to the yield strength of pure nickel.
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•Change patterns of global mining spots were presented.•North America have more surface mining spots categorized as shrink type (rehabilitated).•South America and Asia had the highest ...proportions of expand type of mining spots.•The potential of remote sensing to monitoring changes of surface mining region were investigated.
Quantifying the spatiotemporal change of land cover and understanding their ecological, environmental, and socioeconomic impacts are important for sustainable development. Surface mining by the minerals industry is one driver of the changes in land cover, leading to loss of natural vegetation and top soils, and interruption of ecosystem service flows. This study investigates the effectiveness of remote sensing datasets to identify and map land cover changes, with the specific goal of understanding the impact of surface mining activities on land cover globally from 1980s to 2013. Diverse remote sensing datasets with long term observations are analyzed, including high-resolution images in Google Earth, Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI), the Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Index (VI) product and Defense Meteorological Satellites Program (DMSP)/Operational Linescan System (OLS) stable night-time light. The results indicated that after entering 21st century, North America (e.g., the United States and Canada) was the only continent to have more surface mining spots categorized as Shrink type (rehabilitated) rather than Expand type. South America (e.g., Chile and Brazil) and Asia (e.g., India and China) had the highest proportions of Expand Type of surface mining spots. Detailed demonstrations on how those remote sensing datasets could help in mining spot monitoring are presented.
Abstract
The year 2022 saw record breaking temperatures in Europe during both summer and fall. Similar to the recent 2018 drought, close to 30% (3.0 million km
2
) of the European continent was under ...severe summer drought. In 2022, the drought was located in central and southeastern Europe, contrasting the Northern-centered 2018 drought. We show, using multiple sets of observations, a reduction of net biospheric carbon uptake in summer (56-62 TgC) over the drought area. Specific sites in France even showed a widespread summertime carbon release by forests, additional to wildfires. Partial compensation (32%) for the decreased carbon uptake due to drought was offered by a warm autumn with prolonged biospheric carbon uptake. The severity of this second drought event in 5 years suggests drought-induced reduced carbon uptake to no longer be exceptional, and important to factor into Europe’s developing plans for net-zero greenhouse gas emissions that rely on carbon uptake by forests.
Many land surface models (LSMs) assume a steady‐state assumption (SS) for forest growth, leading to an overestimation of biomass in young forests. Parameters inversion under SS will potentially ...result in biased carbon fluxes and stocks in a transient simulation. Incorporating age‐dependent biomass into LSMs can simulate real disequilibrium states, enabling the model to simulate forest growth from planting to its current age, and improving the biased post‐calibration parameters. In this study, we developed a stepwise optimization framework that first calibrates “fast” light‐controlled CO2 fluxes (gross primary productivity, GPP), then leaf area index (LAI), and finally “slow” growth‐controlled biomass using the Global LAnd Surface Satellite (GLASS) GPP and LAI products, and age‐dependent biomass curves for the 25 forests. To reduce the computation time, we used a machine learning‐based model to surrogate the complex integrated biosphere simulator LSM during calibration. Our calibrated model led to an error reduction in GPP, LAI, and biomass by 28.5%, 35.3% and 74.6%, respectively. When compared with net biome productivity (NBP) using no‐age‐calibrated parameters, our age‐calibrated parameters increased NBP by an average of 50 gC m−2 yr−1 across all forests, especially in the boreal needleleaf evergreen forests, the NBP increased by 118 gC m−2 yr−1 on average, increasing the estimate of the carbon sink in young forests. Our work highlights the importance of including forest age in LSMs, and provides a novel framework for better calibrating LSMs using constraints from multiple satellite products at a global scale.
Plain Language Summary
Physical and biological process‐based models always overestimate the biomass of young forests, with an assumption that they usually hold maximum carbon stocks like old‐growth stands. Such an assumption can lead to biased carbon fluxes and stocks in further simulation. Considering stand age in LSMs improves their ability to simulate real forest growth. In this study, we develop a stepwise method to account for stand age effects in model simulations by assimilating remotely sensed information on vegetation productivity, leaf area, biomass, and age. To reduce the computational cost of the complex original code, we use a substitute model constructed using a machine learning method for calculations. The improved model successfully reproduces the changes in ecosystem biomass and fluxes as forest age varies. Our research provides a novel approach to improving other land surface models for predicting age‐dependent ecosystem properties.
Key Points
We presented a stepwise calibration framework for better integrating age‐dependent biomass into the integrated biosphere simulator model
We utilized a machine learning‐based model as an alternative to the physical model, accelerating the calibration process
The calibration noticeably improved the simulation of gross primary productivity, leaf area index, and biomass
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