We proposed a mapping method for landscape aesthetic demand and potential supply area based on viewsheds, which is a direct method that provides robust results. Moreover, we mapped the aesthetic value of Hokkaido as a case study in Asia. The Aichi Biodiversity Target refers to the importance of ecosystem service (ES) mapping methodologies. However, ES mapping in policy and practice has rarely been reported. Robust, reliable indicators are required. Recently, studies estimating aesthetic value have used geotagged photos on social networking services instead of survey results of user preferences. The methods used in these studies were cost effective and provided spatially explicit results. However, these methods used the photography positions. Using the photographed sites is a more direct method to estimate the aesthetic demand. Therefore, we used geotagged photos on Flickr and viewsheds from each photography position to identify the photographed sites. The demand area was estimated using the viewshed. The potential supply area was estimated using MaxEnt. The demand and potential supply areas were concentrated in natural parks. Comparing the demand and potential supply areas indicates areas with potential supply despite their low demand in forest, farmland, and natural parks. This method will contribute to CES research and decision-making.

Summary Degradation of floodplains continues with an increasing number of floodplain lakes disconnected from the fluvial dynamics of rivers. Limited understanding is available as to how historical geomorphic formation processes (i.e. geomorphic legacy) determine contemporary ecosystem structure and function. We tested the hypothesis that geomorphic legacy mediates morphometry and results in heterogeneity of macrophyte distributions in disconnected floodplain lakes. The distribution of macrophyte cover was examined in relation to environmental factors, including water nutrient level, morphometry of lakes and patch shelter level across and among three types of lakes along the Ishikari River, Japan. Artificial lakes (isolated by channelisation), natural oxbow lakes and marsh lakes have been disconnected for more than 40 years from natural flood pulses because of dyke construction. The presence of macrophytes (in 5 × 5 m areas) was predicted well by a combination of local water depth and bed slope. Lake average depth, higher values indicating lakes that are more deeply incised with a steeper‐sloped littoral zone, had the strongest and most negative influence on total macrophyte cover across lakes. Cover was least in artificial lakes because of greater average depth. Predicted area of macrophyte cover was significantly less than occupied by actual cover in artificial lakes compared with other lake types. Macrophyte cover in artificial lakes was particularly vulnerable to external factors such as waves and wind. This study underscored the significance of geomorphic legacy in explaining a large proportion of heterogeneity of total macrophyte cover in the study lakes. Artificial lakes did not have the macrophyte habitat quality of natural lakes. When lake morphometry needs to be altered, local conditions as well as patch‐scale properties should be carefully examined in the light of the geomorphic legacy left by dynamic river–floodplain interactions.

The number of intense tropical cyclones is expected to increase in the future, causing severe damage to forest ecosystems. Remote sensing plays an important role in detecting changes in land cover caused by these tropical storms. Remote sensing techniques have been widely used in different phases of disaster risk management because they can deliver information rapidly to the concerned parties. Although remote sensing technology is already available, an examination of appropriate methods based on the type of disaster is still missing. Our goal is to compare the suitability of three different conventional classification methods for fast and easy change detection analysis using high-spatial-resolution and high-temporal-resolution remote sensing imagery to identify areas with windthrow and landslides caused by typhoons. In August 2016, four typhoons hit Hokkaido, the northern island of Japan, creating large areas of windthrow and landslides. We compared the normalized difference vegetation index (NDVI) filtering method, the spectral angle mapper (SAM) method, and the support vector machine (SVM) method to identify windthrow and landslides in two different study areas in southwestern Hokkaido. These methodologies were evaluated using PlanetScope data with a resolution of 3 m/px and validated with reference data based on Worldview2 data with a very high resolution of 0.46 m/px. The results showed that all three methods, when applied to high-spatial-resolution imagery, can deliver sufficient results for windthrow and landslide detection. In particular, the SAM method performed better at windthrow detection, and the NDVI filtering method performed better at landslide detection.

The development of UAV technologies offers practical methods to create landcover maps for monitoring and management of areas affected by natural disasters such as landslides. The present study aims at comparing the capability of two different types of UAV to deliver precise information, in order to characterize vegetation at landslide areas over a period of months. For the comparison, an RGB UAV and a Multispectral UAV were used to identify three different classes: vegetation, bare soil, and dead matter, from April to July 2021. The results showed high overall accuracy values (>95%) for the Multispectral UAV, as compared to the RGB UAV, which had lower overall accuracies. Although having lower overall accuracies, the vegetation class of the RGB UAV presented high producer’s and user’s accuracy over time, comparable to the Multispectral UAV results. Image quality played an important role in this study, where higher accuracy values were found on cloudy days. Both RGB and Multispectral UAVs presented similar patterns of vegetation, bare soil, and dead matter classes, where the increase in vegetation class was consistent with the decrease in bare soil and dead matter class. The present study suggests that the Multispectral UAV is more suitable in characterizing vegetation, bare soil, and dead matter classes on landslide areas while the RGB UAV can deliver reliable information for vegetation monitoring.