scholarly journals A 30 m terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine

2021 ◽  
Vol 13 (5) ◽  
pp. 2437-2456
Author(s):  
Bowen Cao ◽  
Le Yu ◽  
Victoria Naipal ◽  
Philippe Ciais ◽  
Wei Li ◽  
...  
Keyword(s):  
Digital Elevation Model ◽  
Google Earth ◽  
Landsat 8 ◽  
Spectral Features ◽  
Error Matrix ◽  
Cropland Area ◽  
Digital Elevation ◽  
Multi Temporal ◽  
Google Earth Engine ◽  
Elevation Model

Abstract. The construction of terraces is a key soil conservation practice on agricultural land in China providing multiple valuable ecosystem services. Accurate spatial information on terraces is needed for both management and research. In this study, the first 30 m resolution terracing map of the entire territory of China is produced by a supervised pixel-based classification using multisource and multi-temporal data based on the Google Earth Engine (GEE) platform. We extracted time-series spectral features and topographic features from Landsat 8 images and the Shuttle Radar Topography Mission digital elevation model (SRTM DEM) data, classifying cropland area (cultivated land of Globeland30) into terraced and non-terraced types through a random forest classifier. The overall accuracy and kappa coefficient were evaluated by 10 875 test samples and achieved values of 94 % and 0.72, respectively. For terrace class, the producer's accuracy (PA) was 79.945 %, and the user's accuracy (UA) was 71.149 %. The classification performed best in the Loess Plateau and southwestern China, where terraces are most numerous. Some northeastern, eastern-central, and southern areas had relatively high uncertainty. Typical errors in the mapping results are from the sloping cropland (non-terrace cropland with a slope of ≥ 5∘), low-slope terraces, and non-crop vegetation. Terraces are widely distributed in China, and the total terraced area was estimated to be 53.55 Mha (i.e., 26.43 % of China's cropland area) by pixel counting (PC) method and 58.46 ± 2.99 Mha (i.e., 28.85 % ± 1.48 % of China's cropland area) by error-matrix-based model-assisted estimation (EM) method. Elevation and slope were identified as the main features in the terrace/non-terrace classification, and multi-temporal spectral features (such as percentiles of NDVI, TIRS2, and BSI) were also essential. Terraces are more challenging to identify than other land use types because of the intra-class feature heterogeneity, interclass feature similarity, and fragmented patches, which should be the focus of future research. Our terrace mapping algorithm can be used to map large-scale terraces in other regions globally, and our terrace map will serve as a landmark for studies on multiple ecosystem service assessments including erosion control, carbon sequestration, and biodiversity conservation. The China terrace map is available to the public at https://doi.org/10.5281/zenodo.3895585 (Cao et al., 2020).

scholarly journals A 30-meter terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine

2020 ◽  
Author(s):  
Bowen Cao ◽  
Le Yu ◽  
Victoria Naipal ◽  
Philippe Ciais ◽  
Wei Li ◽  
...  
Keyword(s):  
Ecosystem Services ◽  
Digital Elevation Model ◽  
Google Earth ◽  
Landsat 8 ◽  
Spectral Features ◽  
Cropland Area ◽  
Digital Elevation ◽  
Multi Temporal ◽  
Google Earth Engine ◽  
Elevation Model

Abstract. The construction of terraces is a key soil conservation practice on agricultural land in China, providing multiple valuable ecosystem services. Accurate spatial information on terraces is needed for both management and research. In this study, the first 30 m resolution terracing map of the entire territory of China is produced by a supervised pixel-based classification using multi-source and multi-temporal data based on the Google Earth Engine (GEE) platform. We extracted time-series spectral features and topographic features from Landsat 8 images and the Shuttle Radar Topography Mission digital elevation model (SRTM DEM) data, classifying cropland area (cultivated land of Globeland30) into terraced and non-terraced type through a random forest classifier. The overall accuracy and kappa coefficient were evaluated by 10875 test samples and achieved values of 94 % and 0.72, respectively. The classification performed best in the Loess Plateau and southwestern China, where terraces are most numerous. Some northeastern, central eastern and southern area had relatively high uncertainty. Typical errors in the mapping results from the sloping cropland (non-terrace cropland with a slope of ≥ 5°), low-slope terraces, and non-crop vegetation. Terraces are widely distributed in China and the total terraced area was estimated to be 53.55 Mha (i.e., 26.43 % of China's cropland area) by pixel counting (PC) method and 58.46 ± 2.99 Mha (i.e., 28.85 % ± 1.48 % of China's cropland area) by error matrix-based model-assisted estimation (EM) method. Elevation and slope were identified as the main features in the terrace/non-terrace classification, and multi-temporal spectral features (such as percentiles of NDVI, TIRS2, BSI) were also essential. Terraces are more challenging to identify than other land use types because of the intra-class feature heterogeneity, inter-class feature similarity and fragmented patches, which should be the focus of future research. Our terrace mapping algorithm can be used to map large-scale terraces in other regions globally, and our terrace map will serve as a landmark for studies on multiple ecosystem services assessments including erosion control, carbon sequestration, and biodiversity conservation. The China terrace map is available to the public at https://doi.org/10.5281/zenodo.3895585 (Cao et al., 2020).

scholarly journals Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform

2021 ◽  
Vol 13 (22) ◽  
pp. 4683
Author(s):  
Masoumeh Aghababaei ◽  
Ataollah Ebrahimi ◽  
Ali Asghar Naghipour ◽  
Esmaeil Asadi ◽  
Jochem Verrelst
Keyword(s):  
Vegetation Index ◽  
Google Earth ◽  
Optimal Time ◽  
Quantitative Detection ◽  
Landsat 8 ◽  
Vegetation Types ◽  
Landsat Images ◽  
Multi Temporal ◽  
Google Earth Engine ◽  
Land Covers

Vegetation Types (VTs) are important managerial units, and their identification serves as essential tools for the conservation of land covers. Despite a long history of Earth observation applications to assess and monitor land covers, the quantitative detection of sparse VTs remains problematic, especially in arid and semiarid areas. This research aimed to identify appropriate multi-temporal datasets to improve the accuracy of VTs classification in a heterogeneous landscape in Central Zagros, Iran. To do so, first the Normalized Difference Vegetation Index (NDVI) temporal profile of each VT was identified in the study area for the period of 2018, 2019, and 2020. This data revealed strong seasonal phenological patterns and key periods of VTs separation. It led us to select the optimal time series images to be used in the VTs classification. We then compared single-date and multi-temporal datasets of Landsat 8 images within the Google Earth Engine (GEE) platform as the input to the Random Forest classifier for VTs detection. The single-date classification gave a median Overall Kappa (OK) and Overall Accuracy (OA) of 51% and 64%, respectively. Instead, using multi-temporal images led to an overall kappa accuracy of 74% and an overall accuracy of 81%. Thus, the exploitation of multi-temporal datasets favored accurate VTs classification. In addition, the presented results underline that available open access cloud-computing platforms such as the GEE facilitates identifying optimal periods and multitemporal imagery for VTs classification.

scholarly journals New discoveries on astronomical orientation of Inca site in Ollantaytambo, Peru

2015 ◽  
Vol 14 (2) ◽  
pp. 37-46
Author(s):  
Karolína Hanzalová ◽  
Jaroslav Klokočník ◽  
Jan Kostelecký
Keyword(s):  
Digital Elevation Model ◽  
Satellite Images ◽  
Observation Point ◽  
Google Earth ◽  
The Sun ◽  
Winter Solstice ◽  
Machu Picchu ◽  
Digital Elevation ◽  
Elevation Model ◽  
The Temple

<p>This paper deals with astronomical orientation of Incas objects in Ollantaytambo, which is located about 35 km southeast from Machu Picchu, about 40 km northwest from Cusco, and lies in the Urubamba valley. Everybody writing about Ollantaytambo, shoud read Protzen. (1)  He devoted his monograph to description and interpretation of that locality. Book of Salazar and Salazar (2) deals, among others, with the orientation of objects in Ollantaytambo with respect to the cardinal direction. Zawaski and Malville (3) documented astronomical context of major monuments of nine sites in Peru, including Ollantaytambo. We tested astronomical orientation in these places and confirm or disprove hypothesis about purpose of Incas objects. For assessment orientation of objects we used our measurements and also satellite images on Google Earth and digital elevation model from ASTER. The satellite images were used to estimate the astronomical-solar-solstice orientation, together with terrestrial images from Salazar and Salazar (2). The digital elevation model is useful in the mountains, where we need the actual horizon for a calculation of sunset and sunrise on specific days (solstices), which were for Incas people very important. We tested which astronomical phenomenon is connected with objects in Ollantaytambo. First, we focused on Temple of the Sun, also known the Wall of six monoliths.  We tested winter solstice sunrise and the rides of the Pleiades for the epochs 2000, 1500 and 1000 A.D. According with our results the Temple isn´t connected neither with winter solstice sunrise nor with the Pleiades. Then we tested also winter solstice sunset. We tried to use the line from an observation point near ruins of the Temple of Sun, to west-north, in direction to sunset. The astronomical azimuth from this point was about 5° less then we need. From this results we found, that is possible to find another observation point. By Salazar and Salazar (2) we found observation point at the corner (east rectangle) of the pyramid by <em>Pacaritanpu,</em> down by the riverside. There is a line connecting the east rectangular “platform” at the river, going along the Inca road up to vicinity of the Temple of the Sun and then in the direction to the Inca face. Using a digital elevation model we found the astronomical azimuth, which is needed for confirm astronomical orientation of the Temple. So, finally we are able to demonstrate a possibility of the solar-solstice orientation in Ollantaytambo.</p>

Geoinformation mapping of landscapes on the example of the Primorskiy Ridge (Baikal region)

2020 ◽  
Vol 954 (12) ◽  
pp. 20-30
Author(s):  
Yu.V. Vanteeva ◽  
Е.А. Rasputina ◽  
S.V. Solodyankina
Keyword(s):  
Digital Elevation Model ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Topographic Wetness Index ◽  
Space Images ◽  
Topographic Position Index ◽  
Digital Elevation ◽  
Different Seasons ◽  
Position Index ◽  
Elevation Model

The authors present the results of geoinformation mapping the Primorskiy Ridge landscapes using Landsat 8 satellite images, the digital elevation model SRTM and the factor-dynamic classification of geosystems. At the first stage, the remote sensing data for different seasons were classified using the ISODATA method. Then, using the digital elevation model, the landforms were classified basing upon the topographic position index. According to combining the classification parameters of one of the space images and digital elevation model, each polygon is automatically assigned to a certain preliminary type of landscapes using boolean expressions. Legend adjustments were made basing upon the fieldwork materials. As a result, a digital landscape map of the southern part of the Primorsky Ridge was created; it reflects the landscape structure at the level of facies groups and contains attributive information about the landform, altitude, slope and aspect, topographic wetness index. The analysis of the landscape pattern showed a high fragmentation of landscape polygons, formed due to overlay operations, which indicates the need for generalization of landscape contours.

scholarly journals STUDI PENDAHULUAN PENGINDERAAN JAUH DAN SISTEM INFORMASI GEOGRAFIS UNTUK ANALISIS MEDAN SKALA TINJAU DI SEBAGIAN PROVINSI SUMATERA SELATAN

GEOMATIKA ◽  
2018 ◽  
Vol 24 (2) ◽  
pp. 107
Author(s):  
Heratania Aprilia Setyowati ◽  
Ratna Nurani ◽  
Sigit Heru Murti Budi Santosa
Keyword(s):  
Digital Elevation Model ◽  
Shuttle Radar Topography Mission ◽  
Landsat 8 ◽  
Digital Elevation ◽  
Elevation Model

<p class="Papertext">Beragam cara dapat digunakan untuk mengetahui karakteristik suatu wilayah, salah satunya adalah analisis medan yang merupakan studi sistematik yang memanfaatkan data penginderaan jauh untuk menggali asal muasal, riwayat geomorfologi, dan komponen suatu bentang lahan. Tujuan dari studi pendahuluan ini untuk mengetahui karakteristik medan yang ada di sebagian daerah Sumatera Selatan melalui analisis medan dengan pembuatan sekuen medan yang berbasis citra penginderaan jauh. Citra Landsat 8 digunakan untuk mendapatkan informasi tutupan lahan dan bentuk lahan. Citra SRTM (<em>Shuttle Radar Topography Mission</em>) digunakan untuk menghasilkan data DEM (<em>Digital Elevation Model</em>), <em>h</em><em>illshade</em>, dan <em>s</em><em>lope</em> yang selanjutnya diturunkan menjadi peta topografi. Peta Geologi digunakan untuk menurunkan informasi mengenai jenis tanah. Peta arah aliran dan akumulasi air digunakan untuk menurunkan informasi kondisi drainase. Selanjutnya semua peta di<em>overlay</em> dan digunakan untuk menarik garis sekuen medan sebagai dasar identifikasi karakteristik medan. Berdasarkan hasil studi pendahuluan ini, dapat dikenali bahwa karakteristik medan sebagian Sumatera Selatan berbentuk lahan vulkanik, struktural dan fluvial dengan proses geomorfologi berupa erosi vertikal, transportasi, deposisi, dan sedimentasi. Aplikasi Penginderaan Jauh dan SIG dengan metode sekuen medan dapat digunakan untuk mengetahui karakteristik medan suatu wilayah.</p><p><em><br /></em></p>

scholarly journals Evaluation of the Accuracy of Digital Elevation Model Produced from Different Open Source Data

2019 ◽  
Vol 25 (8) ◽  
pp. 100-112
Author(s):  
Raghad Hadi Hasan
Keyword(s):  
Open Source ◽  
Digital Elevation Model ◽  
Thermal Emission ◽  
Shuttle Radar Topography Mission ◽  
Google Earth ◽  
Reference Points ◽  
Aster Gdem ◽  
Digital Elevation ◽  
Elevation Model ◽  
High Level

This study aims to estimate the accuracy of digital elevation models (DEM) which are created with exploitation of open source Google Earth data and comparing with the widely available DEM datasets, Shuttle Radar Topography Mission (SRTM), version 3, and Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM), version 2. The GPS technique is used in this study to produce digital elevation raster with a high level of accuracy, as reference raster, compared to the DEM datasets. Baghdad University, Al Jadriya campus, is selected as a study area. Besides, 151 reference points were created within the study area to evaluate the results based on the values of RMS.Furthermore, the Geographic Information System (GIS) was utilized to analyze, imagine and interpolate data in this study. The result of the statistical analysis revealed that RMSE of DEM related to the differences between the reference points and Google Earth, SRTM DEM and ASTER GDEM are 6.9, 5.5 and 4.8, respectively. What is more, a finding of this study shows convergence the level of accuracy for all open sources used in this study.  

scholarly journals ANALISIS CITRA SATELIT LANDSAT 8 DAN DEMNAS UNTUK IDENTIFIKASI PROSPEK PANAS BUMI DI KABUPATEN ACEH TENGAH, PROVINSI ACEH

2021 ◽  
Vol 16 (3) ◽  
pp. 166-184
Author(s):  
Lano Adhitya Permana ◽  
Husin Setia Nugraha ◽  
Sukaesih
Keyword(s):  
Principal Component Analysis ◽  
Surface Temperature ◽  
Digital Elevation Model ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Principal Component ◽  
Landsat 8 ◽  
Fracture Density ◽  
Digital Elevation ◽  
Elevation Model

Gabungan beberapa analisis pada citra satelit Landsat dan Digital Elevation Model Nasional (DEMNAS) dapat dipergunakan untuk mengidentifikasi indikasi area prospek panas bumi. Analisis dilakukan di Kabupaten Aceh Tengah yang diawali dari informasi keberadaan mata air panas pada peta geologi regional lembar Takengon. Metoda penginderaan jauh seperti metoda Fault and Fracture Density (FFD) dan interpretasi circular feature diterapkan pada citra DEMNAS. Sedangkan metoda Land Surface Temperature (LST) dan Direct Principal Component Analysis (DPCA) diterapkan pada citra Landsat 8. Kenampakan circular feature, anomali LST dan indikator adanya mineral ubahan bersuhu tinggi, dapat digunakan untuk memperkirakan keberadaan sumber panas. Sedangkan penerapan FFD digunakan untuk memperoleh indikator adanya zona dengan permeabilitas tinggi yang diperlukan dalam sistem panas bumi.   Hasil penelitian menunjukkan bahwa indikasi sumber panas diperkirakan berada pada komplek vulkanik Gunung Telege yang berada di daerah Kecamatan Atu Lintang. Hal ini diperlihatkan dengan adanya circular feature dan anomali LST yang terdapat di daerah tersebut. Penerapan metoda FFD mengindikasikan adanya zona outflow yang berada di sekitar manifestasi mata air panas yang terletak di sebelah barat laut Gunung Telege. Sedangkan dari hasil penerapan metoda DPCA sulit untuk diinterpretasi dikarenakan belum adanya pemisahan yang tegas antara indikator zona argilik lanjut dan zona propilitik dari hasil DPCA tersebut. Hal ini kemungkinan disebabkan adanya nilai pencampuran antar beberapa indikasi mineral dalam satu piksel yang sama. Secara umum, penggunaan metoda penginderaan jauh di Kabupaten Aceh Tengah dapat membantu untuk memberikan petunjuk awal adanya kemungkinan sistem panas bumi di daerah tersebut

scholarly journals Geração de Modelo Digital de Elevação utilizando dados do SRTM como subsídio ao planejamento e gestão territorial do município de Lucena(PB)/Generation of Digital Elevation Model using SRTM data as grant to territorial planning and management (...)

2015 ◽  
Vol 26 (45) ◽  
pp. 151
Author(s):  
Erika Rodrigues Dias
Keyword(s):  
Digital Elevation Model ◽  
Three Dimensional ◽  
Shuttle Radar Topography Mission ◽  
Google Earth ◽  
Detailed Knowledge ◽  
Radar Images ◽  
Territorial Planning ◽  
Planning And Management ◽  
Digital Elevation ◽  
Elevation Model

<p>Uma das grandes preocupações da atualidade encontra-se no uso racional das terras, conciliando aspectos sociais, econômicos e ambientais tornando necessário o planejamento territorial através de um conhecimento detalhado da superfície territorial. Dessa forma, é de fundamental importância a representação do terreno. Assim, este trabalho teve por objetivo gerar um modelo digital de elevação – MDE, utilizando imagens de radar SRTM com a finalidade de servir como subsídio à gestão e planejamento territorial. Os materiais utilizados nesse trabalho foram imagens de radar da missão Shuttle Radar Topography Mission – SRTM, imagens obtidas do Google Earth e softwares específicos. Como resultados foram gerados diversos produtos cartográficos que possibilitaram o reconhecimento territorial do município como os mapas de hipsometria e clinografia da área em estudo e a representação tridimensional do relevo visando servir como subsídio à gestão territorial e planejamento do meio físico.</p><p><strong>Palavras-Chave</strong>: Modelo Digital de Elevação, SRTM, Geotecnologias.</p><p><strong>Abstract</strong></p><p>A major concern of today is in the rational use of land, combining social, economic and environmental aspects making it necessary to territorial planning with a detailed knowledge of land area. Thus, it is fundamental to representation of the terrain. Thus, this study aimed to generate a digital elevation model - MDE using SRTM radar images in order to serve as a resource management and territorial planning. The materials used in this work were the mission radar images Shuttle Radar Topography Mission - SRTM, images obtained from Google Earth and specific software. The results were generated several cartographic products enabled the territorial recognition of the city as hypsometry maps and clinografia of the study area and the three-dimensional relief representation to serve as subsidy for territorial planning and management of the physical environment.<strong> </strong></p><p><strong> Keywords</strong>:Digital Elevation Model, SRTM, Geotechnology.</p><p> </p><p> </p>

Sign in / Sign up

Export Citation Format

Share Document