scholarly journals Estimation of Groundwater Recharge in Kumamoto Area, Japan in 2016 by Mapping Land Cover Using GIS Data and SPOT 6/7 Satellite Images

2022 ◽  
Vol 14 (1) ◽  
pp. 545
Author(s):  
Hiroki Amano ◽  
Yoichiro Iwasaki
Keyword(s):  
Land Cover ◽  
Natural Disasters ◽  
Groundwater Recharge ◽  
Satellite Images ◽  
Sustainable Use ◽  
Paddy Fields ◽  
Fiscal Year ◽  
Domestic Water ◽  
Gis Data ◽  
Land Cover Map

Agricultural fields, grasslands, and forests are very important areas for groundwater recharge. However, these types of land cover in the Kumamoto area, Japan, were damaged by the Kumamoto earthquake and heavy rains in 2016. In this region, where groundwater provides almost 100% of the domestic water supply for a population of about 1 million, quantitative evaluation of changes in groundwater recharge due to land cover changes induced by natural disasters is important for the sustainable use of groundwater in the future. The objective of this study was to create a land cover map and estimate the groundwater recharge in 2016. Geographic information system (GIS) data and SPOT 6/7 satellite images were used to classify the Kumamoto area into nine categories. The maximum likelihood classifier of supervised classification was applied in ENVI 5.6. Eventually, the map was cleaned up with a 21 × 21 kernel filter, which is larger than the common size of 3 × 3. The created land cover map showed good performance of the larger filter size and sufficient validity, with overall accuracy of 91.7% and a kappa coefficient of 0.88. The estimated total groundwater recharge amount reached 757.56 million m3. However, if areas of paddy field, grassland, and forest had not been reduced due to the natural disasters, it is estimated that the total groundwater recharge amount would have been 759.86 million m3, meaning a decrease of 2.30 million m3 in total. The decrease of 2.13 million m3 in the paddy fields is temporary, because the paddy fields and irrigation channels have been improved and the recharge amount will recover. On the other hand, since the topsoil on the landslide scars will not recover easily in natural conditions, it is expected to take at least 100 years for the groundwater recharge to return to its original state. The recharge amount was estimated to decrease by 0.17 million m3 due to landslides. This amount is quite small compared to the total recharge amount. However, since the reduced recharge amount accounts for the annual water consumption for 1362 people, and 12.1% of the recharge decrease of 1.41 million m3 each year to fiscal year 2024 is expected by municipalities, we conclude that efforts should be made to compensate for the reduced amount due to the disasters.

scholarly journals LAND-COVER MAPS USING MULTIPLE CLASSIFIER SYSTEM FOR POST-DISASTER LANDSCAPE MONITORING

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 139-142 ◽  
Author(s):  
H. Hirayama ◽  
M. Tomita ◽  
R. C. Sharma ◽  
K. Hara
Keyword(s):  
Land Cover ◽  
Natural Disasters ◽  
Satellite Images ◽  
Land Cover Classification ◽  
Landscape Analysis ◽  
Multiple Classifier System ◽  
Classifier System ◽  
Multiple Classifier ◽  
Land Cover Map ◽  
Land Cover Maps

<p><strong>Abstract.</strong> Recently, land cover maps created from high resolution satellite images have been used for landscape analysis, in order to understand the impact of natural disasters on biodiversity and ecosystems. Conventional land cover classification methods, however, suffer from problems with isolated pixels (salt and pepper effect). Filtering can remove the isolated pixels, but can also result in loss of accurate information. The purpose of this study is to create a land cover map for landscape analysis of large-scale disturbances caused by the Great East Japan Earthquake of 2011, utilizing a Multiple Classifier System (MCS), which allows for reduction of isolated pixels while maintaining classification accuracy. RapidEye satellite images covering the Pacific Ocean side of the Tohoku district damaged by the earthquake and subsequent tsunami were obtained for 2010, 2011, 2012 and 2016, and land cover classification was implemented using individual classifiers and the MCS method. The results showed that the MCS land cover map was able to reduce the number of isolated pixels significantly (61-71%) compared with the individual classifiers, while maintaining very high accuracy (0.976-0.986) for all four years. These results indicate that MCS land cover maps have a great potential for analyzing disturbances following infrequent largescale natural disasters such as earthquakes and tsunami, and for monitoring the process of recovery afterwards. We expect that the results of this research will be useful in managing the recovery process in the region disturbed by the Great Eastern Japan Earthquake and Tsunami of 2011, and also for developing future Ecosystem-based Disaster Risk Reduction programs for the region.</p>

scholarly journals Land Cover Classification by Integrating NDVI Time Series and GIS Data to Evaluate Water Circulation in Aso Caldera, Japan

2020 ◽  
Vol 17 (18) ◽  
pp. 6605
Author(s):  
Hiroki Amano ◽  
Yoichiro Iwasaki
Keyword(s):  
Land Cover ◽  
Groundwater Recharge ◽  
Circulation Model ◽  
Water Circulation ◽  
2016 Kumamoto Earthquake ◽  
The 2016 Kumamoto Earthquake ◽  
Gis Data ◽  
Ndvi Time Series ◽  
Aso Caldera ◽  
Land Cover Maps

Grasslands in Aso caldera, Japan, are a type of land cover that is integral for biodiversity, tourist attractions, agriculture, and groundwater recharge. However, the area of grasslands has been decreasing in recent years as a result of natural disasters and changes in social conditions surrounding agriculture. The question of whether the decrease in spring water discharge in Aso caldera is related to the decrease in grasslands remains unanswered. To clarify this relationship, a water circulation model that considers land covers with different hydrological features is needed. In this study, by integrating Normalized Difference Vegetation Index (NDVI) time series and Geographic Information System (GIS) data, we generated land cover maps from the past (in 1981 and 1991) to the present (in 2015 and 2016), before and after the 2016 Kumamoto earthquake, and then for the future (in the 2040s); these maps formed the dataset for building a water circulation model. The results show that the area of grasslands, which are reported to have a higher groundwater recharge rate than that of forests, in 2016 had decreased to 68% of the area in 1981 as a result of afforestation and transformation into forests, as well as landslides induced by the earthquake. The area of grasslands is predicted to further drop to 60% by the 2040s. On the other hand, the area of forests (conifers and hardwoods) in 2016 had increased by 119% relative to that in 1981 because of the transformation of grasslands into forests, although these areas decreased as a result of landslides due to the 2016 Kumamoto earthquake. Quantification of groundwater recharge from grasslands and forests using the land cover maps generated for 1981, 1996, 2015, and 2016 shows that the annual increase in precipitation in these years significantly affected groundwater recharge; these effects were greater than those associated with the type of land cover. Thus, the groundwater recharge increased, despite the decrease in grasslands. However, when constant precipitation was assumed, the groundwater recharge presented a decreasing trend, indicating the importance of maintaining and conserving grasslands from the viewpoint of groundwater conservation.

Resolution Enhancement for Large-Scale Land Cover Mapping via Weakly Supervised Deep Learning

2021 ◽  
Vol 87 (6) ◽  
pp. 405-412
Author(s):  
Qiutong Yu ◽  
Wei Liu ◽  
Wesley Nunes Gonçalves ◽  
José Marcato Junior ◽  
Jonathan Li
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Land Cover ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Land Cover Mapping ◽  
Fusion Method ◽  
The Public ◽  
Land Cover Map ◽  
Weakly Supervised

Multispectral satellite imagery is the primary data source for monitoring land cover change and characterizing land cover globally. However, the consistency of land cover monitoring is limited by the spatial and temporal resolutions of the acquired satellite images. The public availability of daily high-resolution images is still scarce. This paper aims to fill this gap by proposing a novel spatiotemporal fusion method to enhance daily low spatial resolution land cover mapping using a weakly supervised deep convolutional neural network. We merge Sentinel images and moderate resolution imaging spectroradiometer (MODIS )-derived thematic land cover maps under the application background of massive remote sensing data and the large spatial resolution gaps between MODIS data and Sentinel images. The neural network training was conducted on the public data set SEN12MS, while the validation and testing used ground truth data from the 2020 IEEE Geoscience and Remote Sensing Society data fusion contest. The proposed data fusion method shows that the synthesized land cover map has significantly higher spatial resolution than the corresponding MODIS-derived land cover map. The ensemble approach can be implemented for generating high-resolution time series of satellite images by fusing fine images from Sentinel-1 and -2 and daily coarse images from MODIS.

Classification of Paddy Fields using Convolutional Neural Network with MODIS imagery in Northeast Asia

2020 ◽  
Author(s):  
Seungtaek Jeong ◽  
Jonghan Ko ◽  
Gwanyong Jeong ◽  
Myungjin Choi
Keyword(s):  
Neural Network ◽  
Land Cover ◽  
Convolutional Neural Network ◽  
Spatial Resolution ◽  
Satellite Images ◽  
High Spatial Resolution ◽  
Northeast Asia ◽  
Land Cover Classification ◽  
Paddy Fields ◽  
Continental Scale

&lt;p&gt;A satellite image-based classification for crop types can provide information on an arable land area and its changes over time. The classified information is also useful as a base dataset for various geospatial projects to retrieve crop growth and production processes for a wide area. Convolutional neural network (CNN) algorithms based on a deep neural network technique have been frequently applied for land cover classification using satellite images with a high spatial resolution, producing consistent classification outcomes. However, it is still challenging to adopt the coarse resolution images such as Moderate Resolution Imaging Spectroradiometer (MODIS) for classification purposes mainly because of uncertainty from mixed pixels, which can cause difficulty in collecting and labeling actual land cover data. Nevertheless, using coarse images is a very efficient approach for obtaining high temporal and continuous land spectral information for comparatively extensive areas (e.g., those at national and continental scales). In this study, we will classify paddy fields applying a CNN algorithm to MODIS images in Northeast Asia. Time series features of vegetation indices that appear only in paddy fields will be created as 2-dimensional images to use inputs for the classification algorithm. We will use reference land cover maps with a high spatial resolution in Korea and Japan as training and test datasets, employing identified data in person for validation. The current research effort would propose that the CNN-based classification approach using coarse spatial resolution images could have its applicability and reliability for the land cover classification process at a continental scale, providing a direction of its solution for the cause of errors in satellite images with a low spatial resolution.&lt;/p&gt;

The UK Land Cover Map 2000: Construction of a Parcel-Based Vector Map from Satellite Images

2002 ◽  
Vol 39 (1) ◽  
pp. 15-25 ◽  
Author(s):  
R. M. Fuller ◽  
G. M. Smith ◽  
J. M. Sanderson ◽  
R. A. Hill ◽  
A. G. Thomson
Keyword(s):  
Land Cover ◽  
Satellite Images ◽  
Vector Map ◽  
Land Cover Map ◽  
The Uk

scholarly journals Urban Growth Assessment of Chennai District by Remote Sensing and Gis Techniques

2020 ◽  
Vol 8 (6) ◽  
pp. 5119-5125
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Urban Growth ◽  
Satellite Images ◽  
Remote Sensing And Gis ◽  
Land Use Land Cover ◽  
Gis Techniques ◽  
Land Cover Map ◽  
Landsat Satellite

Urban growth of Chennai district is exponential and heading towards extreme urbanisation. Hence this necessitates the study of urban growth in Chennai district. The recent advancement in Remote sensing and GIS has an excellent ability to derive various data from the satellite images obtained .This helps us to map, monitor and picturise various aspects of development with respect to their demands. The basic principle of remote sensing is followed as the methodology. By following the methodology correctly and by proper processing of the data acquired from the satellite images, the exact requirements of information can be obtained. The Change in the urban growth of the Chennai district for three decades from 1989 to 2019 have been found by using remote sensing and GIS techniques. The satellite images of various years are obtained from Landsat satellite from the USGS Earth Explorer .The Land use characteristics of Chennai district of each year can be obtained by preparing the land use land cover map of Chennai district by the use of landsat satellite images. The two software namely ArcGIS and ERDAS Imagine are used to create the Land use land cover map. From the Land use land cover map of Chennai district, the change detection and statistical analysis of three decades are done and these analysis clearly shows that the urban growth of Chennai district is constantly increasing and there is a huge decrease in other natural features such as vegetation, water body and barren land. By performing urban trend analysis the urban growth of Chennai district for the upcoming years are predicted to prove the urban agglomeration in Chennai district.

scholarly journals Impact of Land Management on Water Resources, a South African Context

2021 ◽  
Vol 13 (2) ◽  
pp. 701
Author(s):  
Mary Nkosi ◽  
Fhumulani I. Mathivha ◽  
John O. Odiyo
Keyword(s):  
Water Resources ◽  
Land Cover ◽  
South African ◽  
Land Management ◽  
Sustainable Use ◽  
Land Use Land Cover ◽  
Domestic Water ◽  
African Context ◽  
Water Scarce ◽  
South African Water

Globally, the changes exerted on the land cover have shown greater impacts on the quality and quantity of water resources and thus affecting catchment’s hydrological response (i.e., runoff, evapotranspiration, infiltration, amongst others). South Africa is a water-scarce country faced with domestic water supply challenges. A systematic review was conducted on the overview impacts of land use/land cover changes on water resources. Despite the country’s best efforts in ensuring the protection and sustainable use of water resources, the review indicated that water quality has been compromised in most parts of the country thus affecting water availability. The increase in water demand with development presents the need for better integrated strategic approaches and a change in behaviour towards water resource and land management. Thus, the review suggested a few possible solutions that will promote sustainable development, while protecting and preserving the integrity of South African water resources.

Sign in / Sign up

Export Citation Format

Share Document