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Accurate high-resolution maps of oil palm plantations underpin effective management of environmental and socio-economic impacts at global, regional, and national levels. However, young industrial and highly irregular small-holder plantations are mostly unmapped and not included in official FAO statistics. This issue is addressed here by discriminating global oil palm plantation in 2020 into four subclasses: Industrial Mature Oil Palm (IMOP); Industrial Young Oil Palm (IYOP); Smallholder Mature Oil Palm (SMOP); and Smallholder Young Oil Palm (SYOP). Data, resolved to 4.77 m, from Planet & NICFI, Sentinel-1/2, were combined with other layers using the image-oriented classification and regression tree (CART) algorithm which performed best in classification tests. Results show that SMOP dominates distributional extent, but it was also the most accurately mapped subclass typically found at 500–1000 m altitude. IMOP had the most extensive altitude range of 500–1300 m, while IYOP and SYOP were found at similar altitudes of 500–800 m and 500–900 m respectively. Recent developments in South East Asia show oil palm plantations expanding into new areas with a slope of 24 degrees. Results provide data to support Sustainable Development Goal by assisting future oil palm-related development planning and monitoring in the world's major oil palm-growing countries.

Accurate determination of the spatial distribution of coastal wetlands is crucial for the management and conservation of ecosystems. Feature selection methods based on the Jeffries-Matusita (J-M) method include J-M distance with simple average ranking (JMave), J-M distance based on weights and correlations (JMimproved), and heuristic J-M distance (JMmc). However, as the impacts of these methods on wetland classification are different, their applicability has rarely been investigated. Based on the Google Earth Engine (GEE) and random forest (RF) classifier, this is a comparative analysis of the applicability of the JMave, JMimproved, and JMmc methods. The results show that the three methods compress feature dimensions and retain all feature types as much as possible. JMmc exhibits the most significant compression from a value of 35 to 15 (57.14%), which is 37.14% and 40% more compressed than JMave and JMimproved, respectively. Moreover, they produce comparable classification results, with an overall classification accuracy of 90.20 ± 0.19% and a Kappa coefficient of 88.80 ± 0.22%. However, different methods had their own advantages for the classification of different land classes. Specifically, JMave has a better classification only in cropland, while JMmc is advantageous for recognizing water bodies, tidal flats, and aquaculture. While JMimproved failed to retain vegetation and mangrove features, it enables a better depiction of the mangroves, salt pans, and vegetation classes. Both JMave and JMimproved rearrange features based on J-M distance, while JMmc places more emphasis on feature selection. As a result, there can be significant differences in feature subsets among these three methods. Therefore, the comparative analysis of these three methods further elucidates the importance of J-M distance in feature selection, demonstrating the significant potential of J-M distance-based feature selection methods in wetland classification.

To study global and regional environment protection and sustainable development and also to optimize mapping methods, it is of great significance to compare three existing 10 m resolution global land cover products in terms of accuracy: FROM-GLC10, the ESRI 2020 land cover product (ESRI2020), and the European Space Agency world cover 2020 product (ESA2020). However, most previous validations lack field collection points in large regions, especially in Southeast Asia, which has a cloudy and rainy climate, creating many difficulties in land cover mapping. In 2018 and 2019, we conducted a 56-day field investigation in Southeast Asia and collected 3326 points from different places. By combining these points and 14,808 other manual densification points in a stratified random sampling, we assessed the accuracy of the three land cover products in Southeast Asia. We also compared the impacts of the different classification standards, the different sample methods, and the different spatial distributions of the sample points. The results show that in Southeast Asia, (1) the mean overall accuracies of the FROM-GLC10, ESRI2020, and ESA2020 products are 75.43%, 79.99%, and 81.11%, respectively; (2) all three products perform well in croplands, forests, and built-up areas; ESRI2020 and ESA2020 perform well in water, but only ESA2020 performs well in grasslands; and (3) all three products perform badly in shrublands, wetlands, or bare land, as both the PA and the UA are lower than 50%. We recommend ESA2020 as the first choice for Southeast Asia’s land cover because of its high overall accuracy. FROM-GLC10 also has an advantage over the other two in some classes, such as croplands and water in the UA aspect and the built-up area in the PA aspect. Extracting the individual classes from the three products according to the research goals would be the best practice.

A band selection model was described for efficient and accurate remotely-sensed vegetation mapping in cloudy mixed-vegetation areas, demonstrated with an application on mapping mangroves in Southeast Asia (SE Asia). We show how to use multi-source satellite imagery and Cloud Computing Platforms to improve mapping and computational efficiency in complex environments. A key element of the method relies upon field surveys to establish a detailed sample database that includes easily-confused land cover. The Maximal Separability and Information (MSI) model was developed to select key bands for target land cover classification from multiple satellite imagery based on two principles: 1. maximize separability of the target cover from other land cover; and 2. maximize and prioritize information from band combinations. Application of the MSI model to map mangroves in SE Asia using three optical and SAR data systems (Landsat OLI, Sentinel-2 and Sentinel-1) showed: 1. Sentinel-2 is better at classifying mangrove than Landsat and Sentinel-1; and 2. SWIR, NIR and Red bands (with SWIR in particular) are effective in separating mangrove from other vegetation. The MSI-mapped mangroves showed lower computation cost compared to using all bands from individual satellites, and higher accuracy (above 90%) when applied to SE Asia. It was robust in tolerating smaller sample sizes, thereby demonstrating computational feasibility and substantial improvements with the MSI model for large-scale land cover mapping in complex environments.

Pearl River Delta (PRD), as one of the most densely populated regions in the world, is facing both natural changes (e.g., sea level rise) and human-induced changes (e.g., dredging for navigation and land reclamation). Bathymetric information is thus important for the protection and management of the estuarine environment, but little effort has been made to comprehensively evaluate the performance of different methods and datasets. In this study, two linear regression models—the linear band model and the log-transformed band ratio model, and two non-linear regression models—the support vector regression model and the random forest regression model—were applied to Landsat 8 (L8) and Sentinel-2 (S2) imagery for bathymetry mapping in 2019 and 2020. Results suggested that a priori area clustering based on spectral features using the K-means algorithm improved estimation accuracy. The random forest regression model performed best, and the three-band combinations outperformed two-band combinations in all models. When the non-linear models were applied with three-band combination (red, green, blue) to L8 and S2 imagery, the Root Mean Square Error (Mean Absolute Error) decreased by 23.10% (35.53%), and the coefficient of determination (Kling-Gupta efficiency) increased by 0.08 (0.09) on average, compared to those using the linear regression models. Despite the differences in spatial resolution and band wavelength, L8 and S2 performed similarly in bathymetry estimation. This study quantified the relative performance of different models and may shed light on the potential combination of multiple data sources for more timely and accurate bathymetry mapping.

Background/Aims: Human dental pulp-derived mesenchymal stromal cells (hDPSCs) are promising seed cells for tissue engineering due to their easy accessibility and multi-lineage differentiation. Pannexin3 (Panx3) plays crucial roles during bone development and differentiation. The aim of the present study was to investigate the effect of Panx3 on osteogenesis of hDPSCs and the underlying mechanism. Methods: Utilizing qRT-PCR, Western blot, and immunohistochemistry, we explored the change of Panx3 during osteogenic differentiation of hDPSCs. Next, hDPSCs with loss (Panx3 knockdown) and gain (Panx3 overexpression) of Panx3 function were developed to investigate the effects of Panx3 on osteogenic differentiation of hDPSC and the underlying mechanism. Finally, a commercial β-TCP scaffold carrying Panx3-modified hDPSCs was utilized to evaluate bone defect repair. Results: Panx3 was upregulated during osteogenic differentiation in a time-dependent manner. Panx3 overexpression promoted osteogenic differentiation of hDPSCs, whereas depletion of Panx3 resulted in a decline of differentiation, evidenced by upregulated expression of mineralization-related markers, increased alkaline phosphatase (ALP) activity, and enhanced ALP and Alizarin red staining. Panx3 was found to interact with the Wnt/β-catenin signaling pathway, forming a negative feedback loop. However, Wnt/β-catenin did not contribute to enhancement of osteogenic differentiation as observed in Panx3 overexpression. Moreover, Panx3 promoted osteogenic differentiation of hDPSCs via increasing ERK signaling pathway. Micro-CT and histological staining results showed that Panx3-modified hDPSCs significantly improved ossification of critical-sized bone defects. Conclusion: These findings suggest that Panx3 is a crucial modulator of hDPSCs differentiation.

With the advancement of global warming, the Arctic sea routes (ASRs) may open for the entire year. The ASRs will be far more competitive than they are now, and they will be the major international sea routes in the future. To date, most studies have researched the economic feasibility in the short term from a company’s perspective. To help to plan the shipping market in the future, we developed a three-stage model to simulate the trade demand of the ASRs for the long term. This model firstly considers the seasonal sea ice dynamics in the future and plans new paths for vessels shipping through the Arctic. Additionally, an improved trade prediction model was developed to adapt to the long-term forecasts. After verification, the accuracy of the model was found to be very high (R2 = 0.937). In comparison with another transportation cost model and a trade prediction model, our model was more reasonable. This study simulated the trade volumes of China, Europe (EU), and North America (NA) in 2100 with the ASRs open. The results show that the percentage of port trade can be up to 26% in representative concentration pathway (RCP)2.6, and the percentage of port trade can be up to 52% in RCP8.5.

Timely and accurate regional rice paddy monitoring plays a significant role in maintaining the sustainable rice production, food security, and agricultural development. This study proposes an operational automatic approach to mapping rice paddies using time-series SAR data. The proposed method integrates time-series Sentinel-1 data, auxiliary data of global surface water, and rice phenological characteristics with Google Earth Engine cloud computing platform. A total of 402 Sentinel-1 scenes from 2017 were used for mapping rice paddies extent in the Mun River basin. First, the calculated minimum and maximum values of the backscattering coefficient of permanent water (a classification type within global surface water data) in a year was used as the threshold range for extracting the potential extent. Then, three rice phenological characteristics were extracted based on the time-series curve of each pixel, namely the date of the beginning of the season (DBS), date of maximum backscatter during the peak growing season (DMP), and length of the vegetative stage (LVS). After setting a threshold for each phenological parameter, the final rice paddy extent was identified. Rice paddy map produced in this study was highly accurate and agreed well with field plot data and rice map products from the International Rice Research Institute (IRRI). The results had a total accuracy of 89.52% and an F1 score of 0.91, showing that the spatiotemporal pattern of extracted rice cover was consistent with ground truth samples in the Mun River basin. This approach could be expanded to other rice-growing regions at the national scale, or even the entire Indochina Peninsula and Southeast Asia.

Capturing spatial and temporal dynamics is a key issue for many remote-sensing based applications. Consequently, several image-blending algorithms that can simulate the surface reflectance with high spatial-temporal resolution have been developed recently. However, the performance of the algorithm against the effect of temporal interval length between the base and simulation dates has not been reported. In this study, our aim was to evaluate the effect of different temporal interval lengths on the accuracy using the widely used blending algorithm, Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), based on Landsat, Moderate-resolution Imaging Spectroradiometer (MODIS) images and National Land Cover Database (NLCD). Taking the southwestern continental United States as the study area, a series of experiments was conducted using two schemes, which were the assessment of STARFM with (i) a fixed base date and varied simulation date and (ii) varied base date and specific simulation date, respectively. The result showed that the coefficient of determination (R2), Root Mean Squared Error (RMSE) varied, and overall trend of R2 decreased along with the increasing temporal interval between the base and simulation dates for six land cover types. The mean R2 value of cropland was lowest, whereas shrub had the highest value for two schemes. The result may facilitate selection of an appropriate temporal interval when using STARFM.

Oil palm is the main cash crop grown in Malaysia, and palm oil plays an important role in the world oil market. A number of studies have used multisource remote sensing data to conduct research on oil palms in Malaysia, but there are a lack of long-term oil palm mapping studies, especially when the percentage of oil palm tree cover was higher than other plantations in Malaysia during the period of 2000–2012. To overcome this limitation, we used the Google Earth Engine platform to perform oil palm classification based on Landsat reflectance data. The spatial distribution of oil palms in Malaysia in five periods from 2000 to 2018 was obtained. Then, the planting center of gravity transfer method was applied to analyze the expansion of oil palms in Malaysia from 2000 to 2018 using Landsat data, elevation data, oil palm planting area, crude palm oil price, and other statistical data. Meanwhile, the driving factors affecting the change in oil palm planting area were also analyzed. The results showed that: (1) During 2000–2018, the oil palm planted area in Malaysia increased by 5.06 Mha (million ha), with a growth rate of 83.50%. Specifically, the increased area and growth rate for West Malaysia were 2.05 Mha and 62.05% and for East Malaysia were 3.01 Mha and 109.45%, respectively. (2) Three expansion patterns of oil palms were observed: (i) from a fragmented pattern to a connected area, (ii) expansion along a river, and (iii) from a plain to a gently sloped area. (3) The maximum shift of the center of gravity of the oil palms in West Malaysia was 10 km, while in East Malaysia, it reached 100 km. The East Malaysia oil palm planting potential was greater than that of West Malaysia and showed a trend of shifting from coastal areas to inland areas. (4) Malaysia’s oil palms are mainly planted in areas below 100 m above sea level; although a trend of expansion into high altitudes is visible, oil palm plantings extend to areas below 300 m above sea level. (5) Topography, crude palm oil prices, and deforestation are closely related to changes in oil palm planted area.

Long time-series monitoring of mangroves to marine erosion in the Bay of Bangkok, using Landsat data from 1987 to 2017, shows responses including landward retreat and seaward extension. Quantitative assessment of these responses with respect to spatial distribution and vegetation growth shows differing relationships depending on mangrove growth stage. Using transects perpendicular to the shoreline, we calculated the cross-shore mangrove extent (width) to represent spatial distribution, and the normalized difference vegetation index (NDVI) was used to represent vegetation growth. Correlations were then compared between mangrove seaside changes and the two parameters—mangrove width and NDVI—at yearly and 10-year scales. Both spatial distribution and vegetation growth display positive impacts on mangrove ecosystem stability: At early growth stages, mangrove stability is positively related to spatial distribution, whereas at mature growth the impact of vegetation growth is greater. Thus, we conclude that at early growth stages, planting width and area are more critical for stability, whereas for mature mangroves, management activities should focus on sustaining vegetation health and density. This study provides new rapid insights into monitoring and managing mangroves, based on analyses of parameters from historical satellite-derived information, which succinctly capture the net effect of complex environmental and human disturbances.

Urban heat islands (UHIs) created through urbanization can have negative impacts on the lives of people living in cities. They may also vary spatially and temporally over a city. There is, thus, a need for greater understanding of these patterns and their causes. While previous UHI studies focused on only a few cities and/or several explanatory variables, this research provides a comprehensive and comparative characterization of the diurnal and seasonal variation in surface UHI intensities (SUHIIs) across 67 major Chinese cities. The factors associated with the SUHII were assessed by considering a variety of related social, economic and natural factors using a regression tree model. Obvious seasonal variation was observed for the daytime SUHII, and the diurnal variation in SUHII varied seasonally across China. Interestingly, the SUHII varied significantly in character between northern and southern China. Southern China experienced more intense daytime SUHIIs, while the opposite was true for nighttime SUHIIs. Vegetation had the greatest effect in the day time in northern China. In southern China, annual electricity consumption and the number of public buses were found to be important. These results have important theoretical significance and may be of use to mitigate UHI effects.

A general greening trend in the Arctic tundra biome has been indicated by satellite remote sensing data over recent decades. However, since 2011, there have been signs of browning trends in many parts of the region. Previous research on tundra greenness across the Arctic region has relied on the satellite-derived normalized difference vegetation index (NDVI). In this research, we initially used spatially downscaled solar-induced fluorescence (SIF) data to analyze the spatiotemporal variation of Arctic tundra greenness (2007−2013). The results derived from the SIF data were also compared with those from two NDVIs (the Global Inventory Modeling and Mapping Studies NDVI3g and MOD13Q1 NDVI), and the eddy-covariance (EC) observed gross primary production (GPP). It was found that most parts of the Arctic tundra below 75° N were browning (−0.0098 mW/m2/sr/nm/year, where sr is steradian and nm is nanometer) using SIF, whereas spatially and temporally heterogeneous trends (greening or browning) were obtained based on the two NDVI products. This research has further demonstrated that SIF data can provide an alternative direct proxy for Arctic tundra greenness.

High spatiotemporal resolution satellite imagery is useful for natural resource management and monitoring for land-use and land-cover change and ecosystem dynamics. However, acquisitions from a single satellite can be limited, due to trade-offs in either spatial or temporal resolution. The spatial and temporal adaptive reflectance fusion model (STARFM) and the enhanced STARFM (ESTARFM) were developed to produce new images with high spatial and high temporal resolution using images from multiple sources. Nonetheless, there were some shortcomings in these models, especially for the procedure of searching spectrally similar neighbor pixels in the models. In order to improve these models’ capacity and accuracy, we developed a modified version of ESTARFM (mESTARFM) and tested the performance of two approaches (ESTARFM and mESTARFM) in three study areas located in Canada and China at different time intervals. The results show that mESTARFM improved the accuracy of the simulated reflectance at fine resolution to some extent.

Understanding the spatial patterns of urban land use at both the macro and the micro levels is a central issue in global change studies. Due to the nonlinear features associated with land use spatial patterns, it is currently necessary to provide some distinct analysis methods to analyze them across a range of remote sensing imagery resolutions. The objective of our study is to quantify urban land use patterns from various perspectives using multidimensional fractal methods. Three commonly used fractal dimensions, i.e., the boundary dimension, the radius dimension, and the information entropy dimension, are introduced as the typical indices to examine the complexity, centrality and balance of land use spatial patterns, respectively. Moreover, a new lacunarity dimension for describing the degree of self-organization of urban land use at the macro level is presented. A cloud-free Landsat ETM+ image acquired on 17 September 2010 was used to extract land use information in Wuhan, China. The results show that there are significant linear relationships represented by good statistical fitness related to these four indices. The results indicate that rapid urbanization has substantially affected the urban landscape pattern, and different land use types show different spatial patterns in response. This analysis reveals that multiple fractal/nonfractal indices provides a more comprehensive understanding of the spatial heterogeneity of urban land use spatial patterns than any single fractal dimension index. These findings can help us to gain deeper insight into the complex spatial patterns of urban land use.

At high latitudes, lake and river ecosystems are predominant and these ecosystems are undergoing significant changes due to climate change. Although many scientists have studied lakes and rivers in the Arctic region, the inland water dynamics in this region at the continental scale remain unknown. In this study, the dynamics of the Arctic water were analyzed at the continental scale using Landsat ortho-rectified surface reflectance products of fine spatial and temporal resolutions for the period of 2000–2016, using the random forests method. The results of this study produced the following revelations: (i) the water area is decreasing year by year in the long term; (ii) the water loss and gain always show the same dynamic pattern spatially and temporally; (iii) the spatial distribution of the water budget is strongly linked to permafrost, which implies that permafrost determines the distribution pattern of the water dynamics more than climatic factors; and (iv) the dynamics of the water show a certain rule with surface temperature, but the pattern of the dynamics cannot be explained by temperature alone.

As urban thermal environments are being caused by global climatic changes and urbanization is not uniform on diurnal, seasonal, or annual scales, the spatiotemporal patterns of surface urban heat islands (SUHI) similarly vary between cities across regions. This research assessed the spatiotemporal variations in SUHI intensities (SUHII), and then revealed their spatiotemporal patterns and relationships that existed within 32 major cities in China using spatialization technologies, such as the self-organizing map (SOM) method and statistical methods. Results showed that the spatial patterns of the SUHII patterns in China were significantly affected by the climatic types, whereas human heat discharge also disturbed the patterns to a certain extent. Specifically, the daytime SUHIIs in China had much higher seasonal variations in North China than in South China. The nighttime SUHIIs were much weaker and more stable than the daytime SUHIIs, and had far more obvious spatial patterns with much higher values in North China than in South China. As for the temporal regimes, the temporal variation in the SUHIIs in one city was more related to the development of the urbanization. To be specific, not all cities were experiencing increasingly worse urban thermal environments with urbanization as reported by previous studies. This research not only proposes a spatiotemporal framework to study the SUHIIs patterns and their relationships, but also provides an in-depth and comprehensive understanding of SUHIIs in China.

Climate change inevitably leads to changes in hydrothermal circulation. However, thermal-hydrologic exchanging caused by land cover change has also undergone ineligible changes. Therefore, studying the comprehensive effects of climate and land cover changes on land surface water and heat exchanges enables us to well understand the formation mechanism of regional climate and predict climate change with fewer uncertainties. This study investigated the land surface thermal-hydrologic exchange across southern China for the next 40 years using a land surface model (ecosystem-atmosphere simulation scheme (EASS)). Our findings are summarized as follows. (i) Spatiotemporal variation patterns of sensible heat flux (H) and evapotranspiration (ET) under the land cover scenarios (A2a or B2a) and climate change scenario (A1B) are unanimous. (ii) Both H and ET take on a single peak pattern, and the peak occurs in June or July. (iii) Based on the regional interannual variability analysis, H displays a downward trend (10%) and ET presents an increasing trend (15%). (iv) The annual average H and ET would, respectively, increase and decrease by about 10% when woodland converts to the cultivated land. Through this study, we recognize that land surface water and heat exchanges are affected greatly by the future climate change as well as land cover change.

Drought is a complex hazard, and it has an impact on agricultural, ecological, and socio-economic systems. The vegetation condition index (VCI), which is derived from remote-sensing data, has been widely used for drought monitoring. However, VCI based on the normalized difference vegetation index (NDVI) does not perform well in certain circumstances. In this study, we examined the utility of the vegetation index based on the universal pattern decomposition method (VIUPD) based VCI for drought monitoring in various climate divisions across the continental United States (CONUS). We compared the VIUPD-derived VCI with the NDVI-derived VCI in various climate divisions and during different sub-periods of the growing season. It was also compared with other remote-sensing-based drought indices, such as the temperature condition index (TCI), precipitation condition index (PCI) and the soil moisture condition index (SMCI). The VIUPD-derived VCI had stronger correlations with long-term in situ drought indices, such as the Palmer Drought Severity Index (PDSI) and the standardized precipitation index (SPI-3, SPI-6, SPI-9, and SPI-12) than did the NDVI-derived VCI, and other indices, such as TCI, PCI and SMCI. The VIUPD has considerable potential for drought monitoring. As VIUPD can make use of the information from all the observation bands, the VIUPD-derived VCI can be regarded as an enhanced VCI.