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Although GIS technologies have been utilized in many fields, including geoscience and geography, obtaining skills in GIS operation is still challenging. Effective GIS operation requires geographical and cartographic knowledge such as the principles of map projection, techniques to use computers and related devices, and expertise in graphic, spreadsheet, and database software. In addition, various recent concepts and technologies, such as AR, VR, UAVs, web mapping, and 3D printing, have been combined with today’s GIS. Therefore, comprehensive GIS education is crucial for fostering scientists with GIS skills.We have been developing methodologies and materials for GIS education to address this issue. They include 1) online open-source materials for learning how to use GIS software for various applications, 2) web-based online GIS to learn applications of GIS for understanding disaster risks, 3) web pages to learn the basics of Python programming relevant to GIS, and 4) materials for learning the utilization of UAVs, 3D printers, AR, and VR in connection with GIS. This presentation outlines our activities and discusses their implications to provide future perspectives.In 2022, a subject called General Geography with an emphasis on GIS became compulsory in Japanese senior high schools (10 to 12 grades) due to government educational reform. Because students usually select what to major in universities during high school, attracting high-school students' attention to GIS and related geoscientific and geographical issues is essential for the future development of geoscience and geography. Therefore, the targets of our activities are both university and high school students.

Land conversion from natural forests to plantations (e.g., oil palm) in Southeast Asia is one of the most intensive land-use changes occurring worldwide. To clarify the effects of oil palm plantations on water quality, we conducted multipoint river and stream water sampling in peninsular Malaysia at the end of the rainy season over a 3-year period (2013-2015). We measured the major dissolved ions and stable isotope ratios of water (δ

Effective geomorphological education for young students such as university undergraduates and high school students is crucial for fostering future geomorphologists and the long-term development of Geomorphology. As an outreach activity, geomorphological education for common people is also meaningful. Especially, education related to geomorphological hazards for citizens will lead to disaster risk reduction. We have been developing materials and curricula for geomorphological education using GIS, Internet technology, close-range remote sensing, and virtual reality, and have applied them to practical courses in high school classes and social events in Japan and China. The developed materials include: 1) online resources for learning GIS operations including geomorphometric analysis, 2) Web-based online GIS for a better understanding of flood hazard maps in relation to landforms, 3) explanatory materials of typical landforms in Japan based on photographs and topographic data obtained by Unmanned Aerial Vehicles (UAVs), and 4) visual contents for virtual tours of geomorphological sites such as a coastal cliff and an underground cave. This presentation introduces the main points of our educational activities and discusses their implications to provide future perspectives.

Numerous studies utilizing unmanned aerial systems (UASs) have been conducted using the structure from motion technique to study regions/objects of interest for various geoscience-related studies. ...

Accurate remote sensing-based forest aboveground biomass (AGB) estimation is important for accurate understanding of carbon accounting and climate change at a large scale. However, over- and underestimation are common in the process, resulting in inaccurate AGB estimations. Here, the AGB was estimated and mapped by combining Landsat 8 images and forest inventory data in western Hunan Province, China. We used forest canopy density (FCD) mapper to quantify the forest canopy structure. The linear model (LR) and piecewise model with FCD gradients (classified by k-means clustering; sparse, medium, and dense) were developed to estimate AGB for each forest type (coniferous, broadleaf, mixed, and total forests). The piecewise model considered the following scenarios: piecewise model using the variables of LR model (PM), and piecewise model using the variables selected for different FCD gradients (PMV). The PM (R2:0.45-0.56) and PMV (R2:0.63-0.75) models showed better agreement between observed and predicted AGB than the LR (R2:0.18-0.27) models, and the PMV model was the most accurate for each forest type. The PM and PMV models performed better than LR models at different FCD gradients. The PM and PMV models can better alleviate the over- and underestimations of the LR models. At different FCD gradients, the PMV models had different variables, indicating that the correlation between the AGB and spectral variables was different. Overall, FCD is an important forest parameter that influences AGB estimation, and the piecewise model has potential to improve remote sensing-based AGB estimation.

The development of new methods for estimating precise forest structure parameters is essential for the quantitative evaluation of forest resources. Conventional use of satellite image data, increasing use of terrestrial laser scanning (TLS), and emerging trends in the use of unmanned aerial systems (UASs) highlight the importance of modern technologies in the realm of forest observation. Each technology has different advantages, and this work seeks to incorporate multiple satellite, TLS- and UAS-based remote sensing data sets to improve the ability to estimate forest structure parameters. In this paper, two regression analysis approaches are considered for the estimation: random forest regression (RFR) and support vector regression (SVR). To collect the dependent variable, in situ measurements of individual tree parameters (tree height and diameter at breast height (DBH)) were taken in a Japanese cypress forest using the nondestructive TLS method, which scans the forest to obtain dense and accurate point clouds under the tree canopy. Based on the TLS data, the stem volume was then computed and treated as ground truth information. Topographic and UAS information was then used to calculate various remotely sensed explanatory variables, such as canopy size, canopy cover, and tree height. Canopy cover and canopy shapes were computed via the orthoimages derived from the UAS and watershed segmentation method, respectively. Tree height was computed by combining the digital surface model (DSM) from the UAS and the digital terrain model (DTM) from the TLS data. Topographic variables were computed from the DTM. The backscattering intensity in the satellite imagery was obtained based on L-band (Advanced Land Observing Satellite-2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2)) and C-band (Sentinel-1) synthetic aperture radar (SAR). All satellite (10–25 m resolution), TLS (3.4 mm resolution) and UAS (2.3–4.6 cm resolution) data were then combined, and RFR and SVR were trained; the resulting predictive powers were then compared. The RFR method yielded fitting R2 up to 0.665 and RMSE up to 66.87 m3/ha (rRMSE = 11.95%) depending on the input variables (best result with canopy height, canopy size, canopy cover, and Sentinel-1 data), and the SVR method showed fitting R2 up to 0.519 and RMSE up to 80.12 m3/ha (rRMSE = 12.67%). The RFR outperformed the SVR method, which could delineate the relationship between the variables for better model accuracy. This work has demonstrated that incorporating various remote sensing data to satellite data, especially adding finer resolution data, can provide good estimates of forest parameters at a plot level (10 by 10 m), potentially allowing advancements in precision forestry.

Observing Forest attributes using small, weighted unmanned aerial systems (UASs) is increasing interest to forest ecologists and managers. Here, we provide a comprehensive model for estimating the relationship between diameter at breast height (DBH) and crown size for Japanese cypress (Chamaecyparis obtusa), intending to promote the use of UAS-acquired data. Aerial images were processed using Structure from Motion software to develop ortho-mosaicked imagery for the study sites. We also measured the DBH and height of 196 individual trees at these same sites. Crown size, estimated from the orthoimagery, was compared with DBH using multiple functional models. Model fit estimates has ranged from R2 = 0.6403–0.7584 and root mean square error (RMSE) = 6.34 cm–5.20 cm (relative RMSE (rRMSE) = 17.83–13.44%). Including tree height as a predictor improved model fit of both linear and Support Vector Regression (SVR) model, where the best result in Adjusted R2 = 0.834 and RMSE = 4.25 cm (rRMSE = 11.30%) for the SVR model. This study suggests the potential for accurately surveying forest attributes through UAS remote sensing, which has important implications for forest monitoring and management.

Methane ( $$\mathrm {CH}_{4}$$ ) is known to be emitted from lakes to the atmosphere via processes such as diffusion and ebullition (i.e., bubble emission). We developed a practical method for partitioning eddy-covariance $$\mathrm {CH}_{4}$$ fluxes from a shallow lake into diffusive and ebullitive fluxes using a wavelet analysis based on local scalar similarity between the $$\mathrm {CH}_{4}$$ concentration and other reference scalars, such as the air temperature or water vapour concentration, in the wavelet time-scale domain, with the hypothesis that similar and dissimilar fluctuation components are related to diffusive and ebullitive $$\mathrm {CH}_{4}$$ fluxes, respectively. Our method is applied to approximately two weeks of data obtained at a shallow mid-latitude lake. The partitioned diffusive flux has a physically sound relationship with wind speed, supporting the validity of the method. The ratio of the diffusive flux to the total flux is typically 0.11 with flow from an area of steady bubble emission, but otherwise 0.36. Further validation is required using a larger dataset and data from other lakes. The proposed method can be easily applied to historical data because it requires only 10-Hz data of $$\mathrm {CH}_{4}$$ concentration and other reference scalars, along with an empirical parameter.

Land cover change (LCC) can have a significant impact on human and environmental well-being. LCC maps derived from historical remote sensing (RS) images are often used to evaluate the impacts of past LC changes and to construct models to predict future LC changes. Free moderate spatial resolution (~ 30 m) optical and synthetic aperture radar (SAR) RS imagery is now becoming increasingly available for this LCC monitoring. However, the classification algorithms used to extract LC information from these images typically require “training data” for classification (i.e. points or polygons with LC class labels), and acquiring this labelled training data can be difficult and time-consuming. Alternatively, crowdsourced geographic data (CGD) has become widely available from online sources like OpenStreetMap (OSM), and it may provide a useful source of training data for LCC monitoring. A major challenge with utilizing CGD for LCC mapping, however, is the presence of class labelling errors, and these errors can vary spatially (e.g. due to differing levels of CGD contributor expertise) and temporally (e.g. due to time lag between CGD creation and RS imagery acquisition). In this study, we investigated a new LCC mapping method which utilizes free Landsat (optical) and PALSAR mosaic (SAR) satellite imagery in combination with labelled LC data extracted from CGD sources (the OSM “landuse” and “natural” polygon datasets). A semi-unsupervised classification approach was employed for the LCC mapping to reduce the effects of class label noise in the CGD. The main motivation and benefit of the proposed method is that it does not require training data to be manually collected, allowing for a faster and more automated assessment of LCC. As a case study, we applied the method to map LCC in the Laguna de Bay area of the Philippines over the 2007–2015 period. The LCC map produced using our proposed approach achieved an overall classification accuracy of 90.2%, providing evidence that CGD and multi-temporal/multi-sensor satellite imagery, when combined, have a great potential for LCC monitoring.

An improved methodology for the extraction and mapping of urban built-up areas at a global scale is presented in this study. The Moderate Resolution Imaging Spectroradiometer (MODIS)-based multispectral data were combined with the Visible Infrared Imager Radiometer Suite (VIIRS)-based nighttime light (NTL) data for robust extraction and mapping of urban built-up areas. The MODIS-based newly proposed Urban Built-up Index (UBI) was combined with NTL data, and the resulting Enhanced UBI (EUBI) was used as a single master image for global extraction of urban built-up areas. Due to higher variation of the EUBI with respect to geographical regions, a region-specific threshold approach was used to extract urban built-up areas. This research provided 500-m-resolution global urban built-up map of year 2014. The resulted map was compared with three existing moderate-resolution global maps and one high-resolution map in the United States. The comparative analysis demonstrated finer details of the urban built...

We explored the potential for rapid land use/land cover (LULC) mapping using time-series Landsat satellite imagery and training data (for supervised classification) automatically extracted from crowdsourced OpenStreetMap (OSM) “landuse” (OSM-LU) and “natural” (OSM-N) polygon datasets. The main challenge with using these data for LULC classification was their high level of noise, as the Landsat images all contained varying degrees of cloud cover (causes of attribute noise) and the OSM polygons contained locational errors and class labeling errors (causes of class noise). A second challenge arose from the imbalanced class distribution in the extracted training data, which occurred due to wide discrepancies in the area coverage of each OSM-LU/OSM-N class. To address the first challenge, three relatively noise-tolerant algorithms – naive bayes (NB), decision tree (C4.5 algorithm), and random forest (RF) – were evaluated for image classification. To address the second challenge, artificial training samples were generated for the minority classes using the synthetic minority over-sampling technique (SMOTE). Image classification accuracies were calculated for a four-class, five-class, and six-class LULC system to assess the capability of the proposed methods for mapping relatively broad as well as more detailed LULC types, and the highest overall accuracies achieved were 84.0% (four-class SMOTE-RF result), 81.0% (five-class SMOTE-RF result), and 72.0% (six-class SMOTE-NB result). RF and NB had relatively similar overall accuracies, while those of C4.5 were much lower. SMOTE led to higher classification accuracies for RF and C4.5, and in some cases for NB, despite the noise in the training set. The main advantages of the proposed methods are their cost- and time-efficiency, as training data for supervised classification is automatically extracted from the crowdsourced datasets and no pre-processing for cloud detection/cloud removal is performed.

The high demand for unmanned aerial systems (UASs) reflects the notable impact that these systems have had on the remote sensing field in recent years. Such systems can be used to discover new findings and develop strategic plans in related scientific fields. In this work, a case study is performed to describe a novel approach that uses a UAS with two different sensors and assesses the possibility of monitoring peatland in a small area of a plantation forest in West Kalimantan, Indonesia. First, a multicopter drone with an onboard camera was used to collect aerial images of the study area. The structure from motion (SfM) method was implemented to generate a mosaic image. A digital surface model (DSM) and digital terrain model (DTM) were used to compute a canopy height model (CHM) and explore the vegetation height. Second, a multicopter drone combined with a thermal infrared camera (Zenmuse-XT) was utilized to collect both spatial and temporal thermal data from the study area. The temperature is an important factor that controls the oxidation of tropical peats by microorganisms, root respiration, the soil water content, and so forth. In turn, these processes can alter the greenhouse gas (GHG) flux in the area. Using principal component analysis (PCA), the thermal data were processed to visualize the thermal characteristics of the study site, and the PCA successfully extracted different feature areas. The trends in the thermal information clearly show the differences among land cover types, and the heating and cooling of the peat varies throughout the study area. This study shows the potential for using UAS thermal remote sensing to interpret the characteristics of thermal trends in peatland environments, and the proposed method can be used to guide strategical approaches for monitoring the peatlands in Indonesia.

Invasive plant species represent a serious threat to biodiversity precipitating a sustained global effort to eradicate or at least control the spread of this phenomenon. Current distribution ranges of many invasive species are likely to be modified in the future by land cover and climate change. Thus, invasion management can be made more effective by forecasting the potential spread of invasive species. Rhododendron ponticum (L.) is an aggressive invasive species which appears well suited to western areas of the UK. We made use of MAXENT modeling environment to develop a current distribution model and to assess the likely effects of land cover and climatic conditions (LCCs) on the future distribution of this species in the Snowdonia National park in Wales. Six global circulation models (GCMs) and two representative concentration pathways (RCPs), together with a land cover simulation for 2050 were used to investigate species' response to future environmental conditions. Having considered a range of environmental variables as predictors and carried out the AICc-based model selection, we find that under all LCCs considered in this study, the range of R. ponticum in Wales is likely to contract in the future. Land cover and topographic variables were found to be the most important predictors of the distribution of R. ponticum. This information, together with maps indicating future distribution trends will aid the development of mitigation practices to control R. ponticum.

This study presents the advantages of detailed landscape analysis by UAV (drone hereafter) photogrammetry compared with satellite remote sensing data. First, satellite data are used for generating a coarse-scale land use/land-cover (LULC) map of the study region using conventional GIS techniques. The Advanced Land Observation Satellite-2 (ALOS-2) Phased Array L-band Synthetic Aperture Radar-2 (PALSAR-2) L-Band backscattering data are processed with a multilayer perceptron (MLP) supervised classification for generating a categorical map. The satellite-derived classification map resulted in eight general land-cover types with a ground resolution of 7.5 m, providing a moderate-resolution representation of the island landscapes. Second, the drone’s image data are used to collect ground survey information and microscale information of the local site by implementing a structure from motion (SfM) technique to develop mosaicked orthorectified images of the sites. The orthophoto and digital surface model (DSM) derived from the drone-based data had resolutions of 0.05 m and 0.1 m, respectively. The SAR-based LULC map showed an overall accuracy of 78.1%, and the drone-based LULC map had an overall average accuracy of 92.3%. The subset area of the SAR map was compared with the drone-based map and showed average Kappa statistics of 0.375, demonstrating that satellite data cause challenges in correctly delineating the local land environment. The terrain information generated by the SfM method provided a good representation of the topography showing the drastic changes in the environment. The results indicate the usefulness of drone-based landscape analysis for future land-use planning at a local village scale.

This study estimates CO2 sequestration by forests in Japan using Land Remote Sensing Satellite (Landsat) Operational Land Imager (OLI) and the Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) remote sensing data for the in-depth retrieval of forest growth stages (tree age). Landsat imagery was used to develop a detailed forest cover map, while the PALSAR data were used to estimate the volume information. The volume was converted to tree age information for each of the three forest types in Japan. An estimation of CO2 sequestration values for each forest type and for each tree age from the forest inventory data was made. The forest cover map results in four classes, and the overall accuracy yields approximately 74%. For the volume estimation, Root Mean Square Error (RMSE) was computed with the ground reference information resulting in 105.58 m3/ha. The final result showed that total CO2 sequestration in Japan based on tree age forest subclasses yields 85.0 Mt∙CO2 (coniferous), 4.76 Mt∙CO2 (evergreen broadleaf) and 21.61 Mt∙CO2 (deciduous broadleaf), which in total is 111.27 Mt∙CO2. Using remote sensing techniques to quantitatively estimate CO2 sequestration in Japanese forests has been shown both to have advantages and to offer further possibilities.

Global land cover products have been created for global environmental studies by several institutions and organizations. The Global Mapping Project coordinated by the International Steering Committee for Global Mapping (ISCGM) has been periodically producing global land cover datasets asone of the eight basic global datasets. It has produced a new fifteen-second (approximately 500 m resolution at the equator) global land cover dataset – GLCNMO2013 (or GLCNMO version 3). This paper describes the method of producing GLCNMO2013. GLCNMO2013 has 20 land cover classes, and they were mapped by improved methods from GLCNMO version 2. In GLCNMO2013, five classes,which are urban, mangrove, wetland, snow/ice, and waterwere independently classified. The remaining 15 classes were divided into 4 groups and mapped individually by supervised classification. 2006 polygons of training data collected for GLCNMO2008 were used for supervised classification. In addition, about 3000 polygons of new training data were collected globally using Google Earth, MODIS Normalized Difference Vegetation Index (NDVI) seasonal change patterns, existing regional land cover maps, and existing four global land cover products. The primary data of this product were Moderate Resolution Imaging Spectroradiometer (MODIS) data of 2013. GLCNMO2013 was validated at 1006 sampled points. The overall accuracy of GLCNMO2013 was 74.8%, and the overall accuracy for eight aggregated classes was 90.2%. The accuracy of the GLCNMO2013 was not improved compared with the GLCNMO2008 at heterogeneous land covers. It is necessary to prepare the training data for mosaic classes and heterogeneous land covers for improving the accuracy.

Achieving more timely, accurate and transparent information on the distribution and dynamics of the world’s land cover is essential to understanding the fundamental characteristics, processes and threats associated with human-nature-climate interactions. Higher resolution (~30–50 m) land cover mapping is expected to advance the understanding of the multi-dimensional interactions of the human-nature-climate system with the potentiality of representing most of the biophysical processes and characteristics of the land surface. However, mapping at 30-m resolution is complicated with existing manual techniques, due to the laborious procedures involved with the analysis and interpretation of huge volumes of satellite data. To cope with this problem, an automated technique was explored for the production of a high resolution land cover map at a national scale. The automated technique consists of the construction of a reference library by the optimum combination of the spectral, textural and topographic features and predicting the results using the optimum random forests model. The feature-rich reference library-driven automated technique was used to produce the Japan 30-m resolution land cover (JpLC-30) map of 2013–2015. The JpLC-30 map consists of seven major land cover types: water bodies, deciduous forests, evergreen forests, croplands, bare lands, built-up areas and herbaceous. The resultant JpLC-30 map was compared to the existing 50-m resolution JAXA High Resolution Land-Use and Land-Cover (JHR LULC) map with reference to Google Earth™ images. The JpLC-30 map provides more accurate and up-to-date land cover information than the JHR LULC map. This research recommends an effective utilization of the spectral, textural and topographic information to increase the accuracy of automated land cover mapping.