scholarly journals The use of index images for decoding the vegetation cover of inner mountain Dagestan, Russia

2021 ◽  
Vol 15 (4) ◽  
pp. 126-136
Author(s):  
R. T. Radzhabova ◽  
N. A. Alekseenko ◽  
B. M. Kuramagomedov ◽  
Z. Sh. Tazhudinova ◽  
Z. M. Sultanov
Keyword(s):  
Vegetation Cover ◽  
Satellite Images ◽  
Spatial Information ◽  
High Spatial Resolution ◽  
Imaging System ◽  
Field Research ◽  
Digital Processing ◽  
Google Earth ◽  
Multi Temporal ◽  
Sentinel 2

Aim. Selection and analysis of index images suitable for deciphering the vegetation cover in the conditions of inner mountain Dagestan.Methods. The study was carried out on the basis of multi-temporal satellite images of high spatial resolution, obtained by the imaging system of the Sentinel-2 series satellite, using methods of digital processing of geoimages. Processing was carried out using the capabilities of the Google Earth Engine geoservice.Results. Multi-temporal index images were obtained for the territory of inner mountain Dagestan. The time series of seasonal changes in the indices (NDVI, SAVI, EVI) were analyzed, making it possible to reveal the phenological patterns of vegetation and to map the vegetation cover on this basis. Schemes for decoding vegetation have been created by which areas are distinguished according to the following characteristics: devoid of vegetation, herbaceous vegetation of varying degrees of density or woody (deciduous and coniferous).Conclusion. When studying vegetation cover using index images in a range of natural conditions, it is necessary to take into account the natural features of the territory, as well using additional sources of spatial information including field research methods.

scholarly journals No-till identification by crop residues on the soil surface using the multi-temporal integral index minNDTI

2021 ◽  
Vol 4 (46) ◽  
pp. 1-1
Author(s):  
Alexander Saakian ◽  
◽  
Keyword(s):  
Satellite Images ◽  
Crop Residues ◽  
Soil Surface ◽  
Field Work ◽  
Google Earth ◽  
No Till ◽  
Multi Temporal ◽  
Time Periods ◽  
Direct Sowing ◽  
Sentinel 2

The study analyzed the possibility of using the multi-temporal spectral index minNDTI to identify farms using no-till. Using the Google earth engine platform, satellite images of the Sentinel 2 system were obtained, processed and analyzed for two time periods. Based on the data obtained, NDTI images were constructed for periods of fieldwork, as well as multi-temporal minNDTI images. As a result of the statistical analysis, significant differences were found between the NDTI values of the samples from the plowing and no-till options for two time periods in which the field work was carried out and between the multi-time values for two years of research. Based on the dynamics of the values of the multi-temporal index minNDTI, a map of the probability of assigning fields to direct sowing was constructed. Ключевые слова: NO-TILL, CROP RESIDUES, GIS, REMOTE SENSING, SPECTRAL INDICES, NDTI

ANALISIS TEMPORAL PERUBAHAN HUTAN MANGROVE MENGGUNAKAN CITRA SATELIT SENTINEL-2 (Studi Kasus di Pulau Tanakeke, Kabupaten Takalar)

2021 ◽  
pp. 777
Author(s):  
Andi Tenri Waru ◽  
Athar Abdurrahman Bayanuddin ◽  
Ferman Setia Nugroho ◽  
Nita Rukminasari
Keyword(s):  
Random Forest ◽  
Google Earth ◽  
Multi Temporal ◽  
Google Earth Engine ◽  
Sentinel 2

Pulau Tanakeke merupakan salah satu pulau dengan hutan mangrove yang luas di pesisir Sulawesi Selatan. Hutan mangrove ini menjadi ekosistem penting bagi masyarakat sekitar karena nilai ekologi maupun ekonominya. Namun, dalam kurun waktu sekitar tahun 1980-2000, keberadaan mangrove tersebut terancam oleh perubahan penggunaan lahan dan juga pemanfaatan yang berlebihan. Penelitian ini bertujuan untuk menganalisis perubahan temporal luas dan tingkat kerapatan hutan mangrove di Pulau Tanakeke antara tahun 2016 dan 2019. Metode analisis perubahan luasan hutan mangrove menggunakan data citra satelit Sentinel-2 multi temporal berdasarkan hasil klasifikasi hutan mangrove dengan menggunakan random forest pada platform Google Earth Engine. Akurasi keseluruhan hasil klasifikasi hutan mangrove tahun 2016 dan 2019 sebesar 91% dan 98%. Berdasarkan hasil analisis spasial diperoleh perubahan penurunan luasan mangrove yang signifikan dari 800,21 ha menjadi 640,15 ha. Kerapatan mangrove di Pulau Tanakeke sebagian besar tergolong kategori dalam kerapatan tinggi.

Heavy metal-induced stress in rice crops detected using multi-temporal Sentinel-2 satellite images

2018 ◽  
Vol 637-638 ◽  
pp. 18-29 ◽  
Author(s):  
Meiling Liu ◽  
Tiejun Wang ◽  
Andrew K. Skidmore ◽  
Xiangnan Liu
Keyword(s):  
Heavy Metal ◽  
Satellite Images ◽  
Induced Stress ◽  
Multi Temporal ◽  
Sentinel 2

scholarly journals Mapping Winter Wheat with Combinations of Temporally Aggregated Sentinel-2 and Landsat-8 Data in Shandong Province, China

2020 ◽  
Vol 12 (12) ◽  
pp. 2065 ◽  
Author(s):  
Feng Xu ◽  
Zhaofu Li ◽  
Shuyu Zhang ◽  
Naitao Huang ◽  
Zongyao Quan ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Google Earth ◽  
Shandong Province ◽  
Sensor Data ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Multi Temporal ◽  
Wheat Development ◽  
Development Phases ◽  
Sentinel 2

Winter wheat is one of the major cereal crops in China. The spatial distribution of winter wheat planting areas is closely related to food security; however, mapping winter wheat with time-series finer spatial resolution satellite images across large areas is challenging. This paper explores the potential of combining temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data available via the Google Earth Engine (GEE) platform for mapping winter wheat in Shandong Province, China. First, six phenological median composites of Landsat-8 OLI and Sentinel-2 MSI reflectance measures were generated by a temporal aggregation technique according to the winter wheat phenological calendar, which covered seedling, tillering, over-wintering, reviving, jointing-heading and maturing phases, respectively. Then, Random Forest (RF) classifier was used to classify multi-temporal composites but also mono-temporal winter wheat development phases and mono-sensor data. The results showed that winter wheat could be classified with an overall accuracy of 93.4% and F1 measure (the harmonic mean of producer’s and user’s accuracy) of 0.97 with temporally aggregated Landsat-8 and Sentinel-2 data were combined. As our results also revealed, it was always good to classify multi-temporal images compared to mono-temporal imagery (the overall accuracy dropped from 93.4% to as low as 76.4%). It was also good to classify Landsat-8 OLI and Sentinel-2 MSI imagery combined instead of classifying them individually. The analysis showed among the mono-temporal winter wheat development phases that the maturing phase’s and reviving phase’s data were more important than the data for other mono-temporal winter wheat development phases. In sum, this study confirmed the importance of using temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data combined and identified key winter wheat development phases for accurate winter wheat classification. These results can be useful to benefit on freely available optical satellite data (Landsat-8 OLI and Sentinel-2 MSI) and prioritize key winter wheat development phases for accurate mapping winter wheat planting areas across China and elsewhere.

scholarly journals Mapeamento e Quantificação da Cobertura Vegetal no Agreste Central de Pernambuco Utilizando Técnicas de Empilhamento e o NDVI (Mapping and Quantification of Vegetation Cover from Central Agreste Region of Pernambuco State Using NDVI Technique)

2011 ◽  
Vol 3 (3) ◽  
pp. 157
Author(s):  
Daniel Rodrigues Lira ◽  
Maria do Socorro Bezerra de Araújo ◽  
Everardo Valadares De Sá Barretto Sampaio ◽  
Hewerton Alves da Silva
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Regional Scale ◽  
Digital Processing ◽  
Sparse Vegetation ◽  
New Sensors

O mapeamento e monitoramento da cobertura vegetal receberam consideráveis impulsos nas últimas décadas, com o advento do sensoriamento remoto, processamento digital de imagens e políticas de combate ao desmatamento, além dos avanços nas pesquisas e gerações de novos sensores orbitais e sua distribuição de forma mais acessível aos usuários, tornam as imagens de satélite um dos produtos do sensoriamento remoto mais utilizado para análises da cobertura vegetal das terras. Os índices de cobertura vegetal deste trabalho foram obtidos usando o NDVI - Normalized Difference Vegetation Index para o Agreste central de Pernambuco indicou 39,7% de vegetação densa, 13,6% de vegetação esparsa, 14,3% de vegetação rala e 10,5% de solo exposto. O NDVI apresentou uma caracterização satisfatória para a classificação do estado da vegetação do ano de 2007 para o Agreste Central pernambucano, porém ocorreu uma confusão com os índices de nuvens, sombras e solos exposto, necessitando de uma adaptação na técnica para um melhor aprimoramento da diferenciação desses elementos, constituindo numa recombinação de bandas após a elaboração e calculo do NDVI.Palavras-chave: Geoprocessamento; sensoriamento remoto; índice de vegetação. Mapping and Quantification of Vegetation Cover from Central Agreste Region of Pernambuco State Using NDVI Technique ABSTRACTIn recent decades, advanced techniques for mapping and monitoring vegetation cover have been developed with the advent of remote sensing. New tools for digital processing, the generation of new sensors and their orbital distribution more accessible have facilitated the acquisition and use of satellite images, making them one of the products of remote sensing more used for analysis of the vegetation cover. The aim of this study was to assess the vegetation cover from Central Agreste region of Pernambuco State, using satellite images TM / LANDSAT-5. The images were processed using the NDVI (Normalized Difference Vegetation Index) technique, generating indexes used for classification of vegetation in dense, sparse and scattered. There was a proportion of 39.7% of dense vegetation, 13.6% of sparse vegetation, 14.3% of scattered vegetation and 10.5% of exposed soil. NDVI technique has been used as a useful tool in the classification of vegetation on a regional scale, however, needs improvement to a more precise differentiation among levels of clouds, shadow, exposed soils and vegetation. Keywords: Geoprocessing, remote sensing, vegetation index

scholarly journals HORIZONTAL DEFORMATIONS OF THE SUKIL RIVERBED WITHIN THE PRE-CARPATHIAN HEIGHT IN 1880-2019

2021 ◽  
pp. 197-211
Keyword(s):  
Satellite Images ◽  
Single Channel ◽  
Field Research ◽  
Earth Satellite ◽  
Natural Increase ◽  
Google Earth ◽  
Historical Maps ◽  
Third Stage ◽  
The Village

The article presents the results of the study of horizontal riverbed deformations of the Sukil river in the area from the town of Bolekhiv to its confluence with the Svicha river during 1880–2019. The studied section of the riverbed is located within the Precarpathian height and is marked by significant dynamics of the riverbed, which is mainly due to frequent floods, including catastrophic ones. The analysis of long-term horizontal riverbed deformations of the Sukil river and identification of the main factors of their manifestation were carried out in three stages. The first stage involved an assessment of the riverbed displacement over a long-term period of tens of years and was performed based on topographic maps of 1880, 1929-1939, and 1990. The second stage focused on the analysis of the riverbed displacement during a short-term period of 5-7 years and was conducted on the basis of Google Earth satellite images of 2006, 2011, and 2017–2019. The third stage was dedicated to the verification of the obtained results by field research and to the identification of the main reasons for the development of horizontal riverbed deformations. The analysis of historical maps and satellite images was mainly conducted by cartographic methods using ArcGIS 10.1. The riverbed of the Sukil river has significant differences in the development of horizontal deformations on the section of Bolekhiv – the village of Podorozhnie (the mouth of the river). According to the type of manifestation and scale of the riverbed deformations development, two sections (hereinafter dynamic sections) with significant horizontal deformations have been identified: the first one – from Bolekhiv to the village of Lysovychi; the second one – from the village of Lysovychi to the village of Podorozhnie (the Sukil mouth). On dynamic section 1, the horizontal deformations are differently manifested depending on the type of the riverbed. The maximum displacements which were found on the meandering sections are approximately 340 m. They were recorded during the period of 1880–1939. On the sections with a “transitional” type of riverbeds (in the late 19th-early 20th century they were braided, and now they are single channel), the deformations are small (up to 60 m) and are manifested mainly within the boundaries of the riverbed. On dynamic section 2, the Sukil riverbed is meandering and the deformations are much larger. The maximum riverbed displacements reach approximately 500 m (during the period of 1880–1939). For dynamic section 2 as well as for the whole section of the Sukil riverbed from Bolekhiv to the mouth, a certain tendency of the riverbed changes on the plan has been revealed. Thus, from 1889 to 1990 we observe a decrease in the meandering of the riverbed caused by anthropogenic influence, in particular, by the straightening of the riverbed in the 70-80s of the last century and by change in the position of the mouth; since 1990, a natural increase in the Sukil riverbed’s meandering has been observed. Key words: horizontal deformations; riverbed types; Sukil; meandering; historical maps; remote sensing.

scholarly journals Geoinformatics Based Sprawl Dynamics Analysis of Berhampur Development Authority Area, Odisha, India

2019 ◽  
Vol 8 (4) ◽  
pp. 10471-10477
Keyword(s):  
Urban Areas ◽  
Satellite Images ◽  
Spatial Information ◽  
Spatial Dimension ◽  
High Resolution Satellite Images ◽  
Multi Temporal ◽  
Authority Area ◽  
Arc Gis ◽  
Spatio Temporal ◽  
Gis Software

Urban and Regional planners need accurate and authentic spatio-temporal information of urban sprawls for efficient and sustainable planning of towns & cities worldwide. Geoinformatics powered with temporal high resolution satellite images, Geographic Information System (GIS), mobile technology, etc is now emerged as the most powerful tool for mapping and monitoring the sprawls of urban habitations. In this paper an attempt is made for analysing the dynamics of sprawls of three statutory towns of Berhampur Development Authority (BeDA) area of Ganjam District, Odisha state, India. The spatial information of urban sprawl of each town has been generated using openly available toposheets and multi -sensor & multi - temporal satellite images and the spatio temporal characteristics of sprawls has been analysed in Arc GIS software. The sprawl area as well as the population of the three towns have been analysed and the future scenario of sprawl-population dynamics has been forecasted for the years 2021 and 2031.The result of this paper highlights that sprawls of the three towns i.e Berhampur, Chhatrapur and Gopalpur will expand their spatial dimension by 22,18 and 97 percent by 2031 whereas population of the three towns will increase by 43, 19 and 15 percent between 2011 -2031.Finally the result indicates that there will be decrease in population density in the three towns which will ultimately force the Development Authority to plan more basic infrastructures and transportation in the newly expanded urban areas.

scholarly journals Estimating Proportion of Vegetation Cover at the Vicinity of Archaeological Sites Using Sentinel-1 and -2 Data, Supplemented by Crowdsourced OpenStreetMap Geodata

2020 ◽  
Vol 10 (14) ◽  
pp. 4764 ◽  
Author(s):  
Athos Agapiou
Keyword(s):  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Hazard Analysis ◽  
Archaeological Site ◽  
Google Earth ◽  
Archaeological Sites ◽  
Radar Images ◽  
Optical Images ◽  
Sentinel 2

Monitoring vegetation cover is an essential parameter for assessing various natural and anthropogenic hazards that occur at the vicinity of archaeological sites and landscapes. In this study, we used free and open access to Copernicus Earth Observation datasets. In particular, the proportion of vegetation cover is estimated from the analysis of Sentinel-1 radar and Sentinel-2 optical images, upon their radiometric and geometric corrections. Here, the proportion of vegetation based on the Radar Vegetation Index and the Normalized Difference Vegetation Index is estimated. Due to the medium resolution of these datasets (10 m resolution), the crowdsourced OpenStreetMap service was used to identify fully and non-vegetated pixels. The case study is focused on the western part of Cyprus, whereas various open-air archaeological sites exist, such as the archaeological site of “Nea Paphos” and the “Tombs of the Kings”. A cross-comparison of the results between the optical and the radar images is presented, as well as a comparison with ready products derived from the Sentinel Hub service such as the Sentinel-1 Synthetic Aperture Radar Urban and Sentinel-2 Scene classification data. Moreover, the proportion of vegetation cover was evaluated with Google Earth red-green-blue free high-resolution optical images, indicating that a good correlation between the RVI and NDVI can be generated only over vegetated areas. The overall findings indicate that Sentinel-1 and -2 indices can provide a similar pattern only over vegetated areas, which can be further elaborated to estimate temporal changes using integrated optical and radar Sentinel data. This study can support future investigations related to hazard analysis based on the combined use of optical and radar sensors, especially in areas with high cloud-coverage.

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