scholarly journals Low-Cost Archaeological Investigation and Rapid Mapping of Ancient Stone Tidal Weirs in the Penghu Archipelago Using Google Earth

2019 ◽  
Vol 11 (17) ◽  
pp. 4536 ◽  
Author(s):  
Lei Luo ◽  
Xinyuan Wang ◽  
Jie Liu ◽  
Wenwu Zheng ◽  
Jing Zhen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Spatial Distribution ◽  
Low Cost ◽  
Google Earth ◽  
Gis Analysis ◽  
Archaeological Investigation ◽  
Rapid Mapping ◽  
The Taiwan Strait ◽  
Very High

This paper provides a brief history review of the use of ancient weirs in fishing on our planet, as well as a pilot study that involves investigating and mapping the coastal heritage of ancient stone tidal weirs (STWs) in the Penghu Archipelago which is located in the Taiwan Strait. The spatial distribution and morphological features of STWs across Penghu Archipelago were investigated and analyzed using very high-resolution (VHR) and freely available Google Earth (GE) imagery and geographic information system (GIS) analysis tools. A total of 539 ground-truthed STWs were identified from multiple temporal GE images, and these accounted for over 90% of the localized inventory databases. The proposed GE-based method was found to be more efficient, timely and effective compared to field and airborne surveys. This paper illustrates the utility of GE as a source of freely available VHR remote sensing imagery for archaeological surveys and heritage sustainability in coastal areas.

scholarly journals Analisis Garis Pantai Ladong Aceh Besar Tahun 2011-2015 dengan Menggunakan Sistem Informasi Geografis

2017 ◽  
Vol 3 (1) ◽  
pp. 84
Author(s):  
Chasandra Faradila ◽  
Ichsan Setiawan ◽  
Edy Miswar
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
River Estuary ◽  
Shoreline Change ◽  
Google Earth ◽  
Aerial Photo ◽  
System Technology ◽  
Geographic Information System Technology ◽  
Aceh Province ◽  
Information System Technology

The purpose of this study was to determine shoreline change along the coast of Ladong, Mesjid Raya subdistrict, Aceh Besar district, Aceh province in the 5 years period started from the year 2011, 2012, 2013, 2014 and 2015. Ground check was implemented in August 2016. This study utilized Geographic Information System technology (GIS), remote sensing and by utilizing Google Earth to capture aerial photo. The result from this research showed that each year the shoreline changes, either it was abrasion or accretion. In 2011-2012 the value of abrasion reached 1.1 ha and the value of accretion reached 0.5 ha. In 2012-2013 the value of abrasion reached 0.3 ha and the value of accretion reached 0.8 ha. In 2013-2014 the value of abrasion reached 1.2 ha and the value of accretion reached 0.2 ha. In 2014-2015 the value of abrasion reached 0.2 ha and the value of accretion reached 1.5 ha. The value of the annual abrasion average reached 0.56 ha and the value of the accretion was 0.58 ha. The largest abrasion happened in 2013-2014 which reached 1.2 ha. The total value of 5 year abrasion was 2.8 ha and the total value of accretion was recorded 2.9 ha. The process of abrasion and accretion also caused a change of the length of Ladong’s coastline every year, and the river estuary was one of the reasons that caused abrasion or accretion

scholarly journals Rapid Mapping and Annual Dynamic Evaluation of Quality of Urban Green Spaces on Google Earth Engine

2021 ◽  
Vol 10 (10) ◽  
pp. 670
Author(s):  
Qiang Chen ◽  
Cuiping Zhong ◽  
Changfeng Jing ◽  
Yuanyuan Li ◽  
Beilei Cao ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Urban Area ◽  
Google Earth ◽  
Green Spaces ◽  
Dynamic Evaluation ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Rapid Mapping ◽  
Google Earth Engine

In order to achieve the United Nations 2030 Sustainable Development Goals (SDGs) related to green spaces, monitoring dynamic urban green spaces (UGSs) in cities around the world is crucial. Continuous dynamic UGS mapping is challenged by large computation, time consumption, and energy consumption requirements. Therefore, a fast and automated workflow is needed to produce a high-precision UGS map. In this study, we proposed an automatic workflow to produce up-to-date UGS maps using Otsu’s algorithm, a Random Forest (RF) classifier, and the migrating training samples method in the Google Earth Engine (GEE) platform. We took the central urban area of Beijing, China, as the study area to validate this method, and we rapidly obtained an annual UGS map of the central urban area of Beijing from 2016 to 2020. The accuracy assessment results showed that the average overall accuracy (OA) and kappa coefficient (KC) were 96.47% and 94.25%, respectively. Additionally, we used six indicators to measure quality and temporal changes in the UGS spatial distribution between 2016 and 2020. In particular, we evaluated the quality of UGS using the urban greenness index (UGI) and Shannon’s diversity index (SHDI) at the pixel level. The experimental results indicate the following: (1) The UGSs in the center of Beijing increased by 48.62 km2 from 2016 to 2020, and the increase was mainly focused in Chaoyang, Fengtai, and Shijingshan Districts. (2) The average proportion of relatively high and above levels (UGI > 0.5) in six districts increased by 2.71% in the study area from 2016 to 2020, and this proportion peaked at 36.04% in 2018. However, our result revealed that the increase was non-linear during this assessment period. (3) Although there was no significant increase or decrease in SHDI values in the study area, the distribution of the SHDI displayed a noticeable fluctuation in the northwest, southwest, and northeast regions of the study area between 2016 and 2020. Furthermore, we discussed and analyzed the influence of population on the spatial distribution of UGSs. We found that three of the five cold spots were located in the east and southeast of Haidian District. Therefore, the proposed workflow could provide rapid mapping and dynamic evaluation of the quality of UGS.

scholarly journals Prediction of Tsunami Hazard Levels Based on GIS Analysis in South Bengkulu Regency

2020 ◽  
Vol 4 (9) ◽  
pp. 54-57
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Sea Level Rise ◽  
Sea Level ◽  
Geographic Information ◽  
Tsunami Hazard ◽  
Gis Analysis ◽  
High Class ◽  
Hazard Levels ◽  
Very High

This study aimed at a prediction of tsunami hazard levels in South Bengkulu Regency, that is calculated based data on sea-level rise, distance from the coastline, distance from the nearest rivers, and beach slope. Measurement is carried out using Geographic Information System (GIS) analysis with overlay techniques and the methods of scoring/weighting. The results showed in South Bengkulu Regency the tsunami hazard levels of very high class 504.65 Km (44.8%), high class 160.77 Km (13.7%), somewhat high class 131.09 Km (11.2%), low class 64.92 Km (5.6 %) and very low class 250.39 Km (21.2%).

scholarly journals Mapping Tea Plantations from VHR Images Using OBIA and Convolutional Neural Networks

2020 ◽  
Vol 12 (18) ◽  
pp. 2935
Author(s):  
Zixia Tang ◽  
Mengmeng Li ◽  
Xiaoqin Wang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Random Forest ◽  
Google Earth ◽  
Fine Tuning ◽  
Economic Plant ◽  
Remote Sensing Images ◽  
Object Based ◽  
Tea Plantations ◽  
Very High

Tea is an important economic plant, which is widely cultivated in many countries, particularly in China. Accurately mapping tea plantations is crucial in the operations, management, and supervision of the growth and development of the tea industry. We propose an object-based convolutional neural network (CNN) to extract tea plantations from very high resolution remote sensing images. Image segmentation was performed to obtain image objects, while a fine-tuned CNN model was used to extract deep image features. We conducted feature selection based on the Gini index to reduce the dimensionality of deep features, and the selected features were then used for classifying tea objects via a random forest. The proposed method was first applied to Google Earth images and then transferred to GF-2 satellite images. We compared the proposed classification with existing methods: Object-based classification using random forest, Mask R-CNN, and object-based CNN without fine-tuning. The results show the proposed method achieved a higher classification accuracy than other methods and produced smaller over- and under-classification geometric errors than Mask R-CNN in terms of shape integrity and boundary consistency. The proposed approach, trained using Google Earth images, achieved comparable results when transferring to the classification of tea objects from GF-2 images. We conclude that the proposed method is effective for mapping tea plantations using very high-resolution remote sensing images even with limited training samples and has huge potential for mapping tea plantations in large areas.

scholarly journals MRENet: Simultaneous Extraction of Road Surface and Road Centerline in Complex Urban Scenes from Very High-Resolution Images

2021 ◽  
Vol 13 (2) ◽  
pp. 239
Author(s):  
Zhenfeng Shao ◽  
Zifan Zhou ◽  
Xiao Huang ◽  
Ya Zhang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
High Resolution ◽  
Google Earth ◽  
Road Surface ◽  
Remote Sensing Images ◽  
Surface Extraction ◽  
The Road ◽  
Centerline Extraction ◽  
Very High

Automatic extraction of the road surface and road centerline from very high-resolution (VHR) remote sensing images has always been a challenging task in the field of feature extraction. Most existing road datasets are based on data with simple and clear backgrounds under ideal conditions, such as images derived from Google Earth. Therefore, the studies on road surface extraction and road centerline extraction under complex scenes are insufficient. Meanwhile, most existing efforts addressed these two tasks separately, without considering the possible joint extraction of road surface and centerline. With the introduction of multitask convolutional neural network models, it is possible to carry out these two tasks simultaneously by facilitating information sharing within a multitask deep learning model. In this study, we first design a challenging dataset using remote sensing images from the GF-2 satellite. The dataset contains complex road scenes with manually annotated images. We then propose a two-task and end-to-end convolution neural network, termed Multitask Road-related Extraction Network (MRENet), for road surface extraction and road centerline extraction. We take features extracted from the road as the condition of centerline extraction, and the information transmission and parameter sharing between the two tasks compensate for the potential problem of insufficient road centerline samples. In the network design, we use atrous convolutions and a pyramid scene parsing pooling module (PSP pooling), aiming to expand the network receptive field, integrate multilevel features, and obtain more abundant information. In addition, we use a weighted binary cross-entropy function to alleviate the background imbalance problem. Experimental results show that the proposed algorithm outperforms several comparative methods in the aspects of classification precision and visual interpretation.

scholarly journals Extraction of Information on Trees outside Forests Based on Very High Spatial Resolution Remote Sensing Images

Forests ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 835 ◽  
Author(s):  
Bin Sun ◽  
Zhihai Gao ◽  
Longcai Zhao ◽  
Hongyan Wang ◽  
Wentao Gao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Northern China ◽  
Sandy Land ◽  
Forest Protection ◽  
Feature Spaces ◽  
Very High Spatial Resolution ◽  
Very High

The sparse Ulmus pumila L. woodland in the Otingdag Sandy Land of China is indispensable in maintaining the ecosystem stability of the desertified grasslands. Many studies of this region have focused on community structure and analysis of species composition, but without consideration of spatial distribution. Based on a combination of spectral and multiscale spatial variation features, we present a method for automated extraction of information on the U. pumila trees of the Otingdag Sandy Land using very high spatial resolution remote sensing imagery. In this method, feature images were constructed using fused 1-m spatial resolution GF-2 images through analysis of the characteristics of the natural geographical environment and the spatial distribution of the U. pumila trees. Then, a multiscale Laplace transform was performed on the feature images to generate multiscale Laplacian feature spaces. Next, local maxima and minima were obtained by iteration over the multiscale feature spaces. Finally, repeated values were removed and vector data (point data) were generated for automatic extraction of the spatial distribution and crown contours of the U. pumila trees. Results showed that the proposed method could overcome the lack of universality common to image classification methods. Validation indicated the accuracy of information extracted from U. pumila test data reached 82.7%. Further analysis determined the parameter values of the algorithm applicable to the study area. Extraction accuracy was improved considerably with a gradual increase of the Sigma parameter; however, the probability of missing data also increased markedly after the parameter reached a certain level. Therefore, we recommend the Sigma value of the algorithm be set to 90 (±5). The proposed method could provide a reference for information extraction, spatial distribution mapping, and forest protection in relation to the U. pumila woodland of the Otingdag Sandy Land, which could also support improved ecological protection across much of northern China.

scholarly journals Designation of Flood Risk Zones Using the Geographic Information System Technique and Remote Sensing Data in Wasit, Iraq

2021 ◽  
Vol 15 (3) ◽  
pp. 129-140
Author(s):  
Kouther Hasheem Rasn ◽  
Qutaiba Abdulwahhab Nsaif ◽  
Mudhar A. Al-Obaidi ◽  
Yakubu Mandafiya John
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Geographic Information System ◽  
Flood Risk ◽  
Flood Hazard ◽  
Remote Sensing Data ◽  
Geographic Information ◽  
Risk Area ◽  
Sensing Data ◽  
Very High

Floods are a great concern for people and infrastructure, and this is an issue which has increased in several regions around the globe in recent years. This study aims to evaluate flood risk areas and create a flood risk map using integrated remote sensing data and a geographic information system (GIS) in the Wasit governorate – eastern Iraq. Specifically, GIS‑based multi‑criteria analysis (MCA) was used to map flood hazard areas using a four‑criteria layer which is as follows: flow accumulation, slope, rainfall, and elevation. These four layers are standardized and combined using the overlay approach in ArcGIS software and a final map was produced. The study area was divided into five zones based on the results map, namely: very low, low, medium, high, and very high, according to the flood risk area. The resulting map indicates that over 60% of the study area is likely to experience a high and very high level of propensity of flooding. This study could be useful for government planners and decision‑makers to predict potential flooding areas and enhance flood management plans.

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