scholarly journals Remote Detection of Cyanobacterial Blooms and Chlorophyll-a Analysis in a Eutrophic Reservoir Using Sentinel-2

2021 ◽  
Vol 13 (15) ◽  
pp. 8570
Author(s):  
Manuel Viso-Vázquez ◽  
Carolina Acuña-Alonso ◽  
Juan Luis Rodríguez ◽  
Xana Álvarez
Keyword(s):  
Chlorophyll A ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Water Governance ◽  
Water Security ◽  
Cyanobacterial Blooms ◽  
Innovative Technology ◽  
Moisture Index ◽  
Water Index ◽  
Sentinel 2

Harmful cyanobacterial blooms have been one of the most challenging ecological problems faced by freshwater bodies for more than a century. The use of satellite images as a tool to analyze these blooms is an innovative technology that will facilitate water governance and help develop measures to guarantee water security. To assess the viability of Sentinel-2 for identifying cyanobacterial blooms and chlorophyl-a, different bands of the Sentinel-2 satellite were considered, and those most consistent with cyanobacteria analysis were analyzed. This analysis was supplemented by an assessment of different indices and their respective correlations with the field data. The indices assessed were the following: Normalized Difference Water Index (NDWI), Normalized Differences Vegetation Index (NDVI), green Normalized Difference Vegetation Index (gNDVI), Normalized Soil Moisture Index (NSMI), and Toming’s Index. The green band (B3) obtained the best correlating results for both chlorophyll (R2 = 0.678) and cyanobacteria (R2 = 0.931). The study by bands of cyanobacteria composition can be a powerful tool for assessing the physiology of strains. NDWI gave an R2 value of 0.849 for the downstream point with the concentration of cyanobacteria. Toming’s Index obtained a high R2 of 0.859 with chlorophyll-a and 0.721 for the concentration of cyanobacteria. Notable differences in correlation for the upstream and downstream points were obtained with the indices. These results show that Sentinel-2 will be a valuable tool for lake monitoring and research, especially considering that the data will be routinely available for many years and the images will be frequent and free.

scholarly journals Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1486
Author(s):  
Chris Cavalaris ◽  
Sofia Megoudi ◽  
Maria Maxouri ◽  
Konstantinos Anatolitis ◽  
Marios Sifakis ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Model Development ◽  
Wheat Yield ◽  
Growth Period ◽  
Yield Data ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

scholarly journals Hasil Studi Pola Kebakaran Lahan Gambut melalui Citra Satelit Sentinel-2 dengan Pengimplementasian Machine Learning Metode Random Forest : Kajian Literatur

2021 ◽  
Vol 4 (2) ◽  
pp. 81
Author(s):  
Annisa Rizky Kusuma ◽  
Fauzan Maulana Shodiq ◽  
Muhammad Faris Hazim ◽  
Dany Puguh Laksono
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Ground Water ◽  
Water Level ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Ground Water Level ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Sentinel 2

Kebakaran lahan gambut merupakan peristiwa yang sulit diprediksi perilakunya. Karakteristik tanah gambut yang kompleks dan faktor-faktor alam lain seperti arah angin, status vegetasi, dan kandungan air membuat kasus ini menjadi salah satu kasus menarik yang masih menjadi objek penelitian yang belum tuntas hingga saat ini. Ketika memasuki musim kemarau kondisi kadar air di dalam tanah gambut akan semakin berkurang, maka potensi terjadinya kebakaran akan semakin tinggi. Pada studi ini dilakukan analisis faktor penyebab kebakaran dengan area cakupan yang luas melalui satelit Sentinel-2. Citra satelit yang diperoleh nantinya akan diolah oleh machine learning untuk memprediksi penyebaran api. Hasil literatur yang telah dilakukan diperoleh bahwa Ground Water Level (GWL), kematangan gambut, suhu, curah hujan dan kelembaban, serta kerapatan vegetasi dapat diidentifikasi melalui perhitungan indeks. Indeks yang digunakan diantaranya indeks Differenced Normalized Difference Vegetation Index (dNDVI) dan Normalized Difference Water Index (NDWI) yang diolah dengan algoritma machine learning metode Random Forest memilki akurasi mencapai 96%.

scholarly journals Analysis of vegetation cover and built-up areas in the Percut watershed landscape, North Sumatra Province using sentinel-2 imagery

2021 ◽  
Vol 912 (1) ◽  
pp. 012089
Author(s):  
B Slamet ◽  
O K H Syahputra ◽  
H Kurniawan ◽  
M Saraan ◽  
M M Harahap
Keyword(s):  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Health Condition ◽  
Land Conversion ◽  
Water Index ◽  
The North ◽  
Normalized Difference Water Index ◽  
North Sumatra ◽  
Sentinel 2

Abstract Changes in land cover have an impact on the health condition of a watershed. This research was conducted by utilizing Sentinel-2 imagery for the recording period 2020 and 2021. Three indices were used in this study, namely, the Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI). NDBI analysis indicates there is an increase in the built-up area of 2,092.62 hectares which means land conversion. NDWI classification shows an increase in the wetness area of 308.58 hectares, mainly occurring in the downstream part of the watershed, located to the north. There is an increase in the area of non-vegetated areas reaching 288.96 hectares in the Percut watershed based on the results of the NDVI analysis.

scholarly journals COMPARISON OF VEGETATION INDICES FROM RPAS AND SENTINEL-2 IMAGERY FOR DETECTING PERMANENT PASTURES

2018 ◽  
Vol XLII-3 ◽  
pp. 1381-1387 ◽  
Author(s):  
M. Piragnolo ◽  
G. Lusiani ◽  
F. Pirotti
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Vegetation Indices ◽  
Monitoring And Control ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Permanent pastures (PP) are defined as grasslands, which are not subjected to any tillage, but only to natural growth. They are important for local economies in the production of fodder and pastures (Ali et al. 2016). Under these definitions, a pasture is permanent when it is not under any crop-rotation, and its production is related to only irrigation, fertilization and mowing. Subsidy payments to landowners require monitoring activities to determine which sites can be considered PP. These activities are mainly done with visual field surveys by experienced personnel or lately also using remote sensing techniques. The regional agency for SPS subsidies, the Agenzia Veneta per i Pagamenti in Agricoltura (AVEPA) takes care of monitoring and control on behalf of the Veneto Region using remote sensing techniques. The investigation integrate temporal series of Sentinel-2 imagery with RPAS. Indeed, the testing area is specific region were the agricultural land is intensively cultivated for production of hay harvesting four times every year between May and October. The study goal of this study is to monitor vegetation presence and amount using the Normalized Difference Vegetation Index (NDVI), the Soil-adjusted Vegetation Index (SAVI), the Normalized Difference Water Index (NDWI), and the Normalized Difference Built Index (NDBI). The overall objective is to define for each index a set of thresholds to define if a pasture can be classified as PP or not and recognize the mowing.

scholarly journals PREDICTING HABITAT DISTRIBUTION OF ENDEMIC AND CRITICALLY ENDANGERED DIPTEROCARPUS LITTORALIS IN NUSAKAMBANGAN, INDONESIA

REINWARDTIA ◽  
2017 ◽  
Vol 16 (1) ◽  
pp. 11
Author(s):  
Iyan Robiansyah
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Critically Endangered ◽  
Habitat Distribution ◽  
Moisture Index ◽  
Water Index ◽  
Maxent Model

ROBIANSYAH, I. 2017. Predicting habitat distribution of endemic and critically endangered Dipterocarpus littoralis in Nusakambangan, Indonesia. Reinwardtia 16(1): 11 - 18. - The tree species Dipterocarpus littoralis (Bl.) Kurz. is endemic to Nusakambangan and categorized as critically endangered. In the present study, the habitat suitability of the species in Nusakambangan was predicted using logistic regression analysis and Maxent model. Three topographic variables (elevation, slope, and aspect), distance from river and coastline, and one vegetation index (Normalized Difference Vegetation Index (NDVI)) as well as two water content indexes (Normalized Difference Water Index (NDWI) and Normalized Difference Moisture Index (NDMI)) were used as predictors of the models. Employing initial number of 82 presence and 250 absence data of D. littoralis, both models were able to predict the suitable areas for the species with fairly high success rate. The AUC and Kappa value for logistic regression were 0.77 ± 0.027 and 0.34 ± 0.058, respectively, while the respected values for Maxent were 0.91 ± 0.062 and 0.37 ± 0.025. Logistic regression analysis identified a total area of 26.13 km2 to be suitable for D. littoralis, while a smaller suitable area (7.85 km2) was predicted by Maxent model. Coastal areas in the west part of the island were predicted by both models as areas with high suitability for D. littoralis. Furthermore, distance from coastline and river, elevation, NDVI, NDWI and NDMI were suggested to be very important for the species ecology and distribution. The results of this study may serve as a basis for population reinforcement and reintroduction programs of D. littoralis and guide for ecosystem management of Nusakambangan Island as a whole. 

scholarly journals Development of a New Phenology Algorithm for Fine Mapping of Cropping Intensity in Complex Planting Areas Using Sentinel-2 and Google Earth Engine

2021 ◽  
Vol 10 (9) ◽  
pp. 587
Author(s):  
Yan Guo ◽  
Haoming Xia ◽  
Li Pan ◽  
Xiaoyang Zhao ◽  
Rumeng Li ◽  
...  
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Growth Cycle ◽  
Google Earth ◽  
Henan Province ◽  
Cropping Intensity ◽  
Water Index ◽  
Google Earth Engine ◽  
Sentinel 2

Cropping intensity is a key indicator for evaluating grain production and intensive use of cropland. Timely and accurately monitoring of cropping intensity is of great significance for ensuring national food security and improving the level of national land management. In this study, we used all Sentinel-2 images on the Google Earth Engine cloud platform, and constructed an improved peak point detection method to extract the cropping intensity of a heterogeneous planting area combined with crop phenology. The crop growth cycle profiles were extracted from the multi-temporal normalized difference vegetation index (NDVI) and land surface water index (LSWI) datasets. Results show that by 2020, the area of single cropping, double cropping, and triple cropping in the Henan Province are 52,236.9 km2, 74,334.1 km2, and 1927.1 km2, respectively; the corresponding producer accuracies are 86.12%, 93.72%, and 91.41%, respectively; the corresponding user accuracies are 88.99%, 92.29%, and 71.26%, respectively. The overall accuracy is 90.95%, and the Kappa coefficient is 0.81. Using the sown area in the statistical yearbook data of cities in the Henan Province to verify the extraction results of this paper, the R2 is 0.9717, and the root mean square error is 1715.9 km2. This study shows that using all the Sentinel-2 data, the phenology algorithm, and cloud computing technology has great potential in producing a high spatio-temporal resolution dataset for crop remote sensing monitoring and agricultural policymaking in complex planting areas.

scholarly journals MAPPING AND MONITORING WETLANDS USING SENTINEL-2 SATELLITE IMAGERY

2017 ◽  
Vol IV-4/W4 ◽  
pp. 271-277 ◽  
Author(s):  
G. Kaplan ◽  
U. Avdan
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Kappa Coefficient ◽  
Water Index ◽  
Object Based ◽  
The World ◽  
Normalized Difference Water Index ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Mapping and monitoring of wetlands as one of the world`s most valuable natural resource has gained importance with the developed of the remote sensing techniques. This paper presents the capabilities of Sentinel-2 successfully launched in June 2015 for mapping and monitoring wetlands. For this purpose, three different approaches were used, pixel-based, object-based and index-based classification. Additional, for more successful extraction of wetlands, a combination of object-based and index-based method was proposed. It was proposed the use of object-based classification for extraction of the wetlands boundaries and the use of Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) for classifying the contents within the wetlands boundaries. As a study area in this paper Sakarbasi spring in Eskisehir, Turkey was chosen. The results showed successful mapping and monitoring of wetlands with kappa coefficient of 0.95.

scholarly journals Integration of Sentinel-2 Derived Spectral Indices and In-situ Forest Inventory to Predict Forest Biomass

2021 ◽  
Vol 64 (2) ◽  
pp. 119-130
Author(s):  
Areeba Binte Imran ◽  
Samia Ahmed ◽  
Waqar Ahmed ◽  
Muhammad Zia-ur-Rehman ◽  
Arif Iqbal ◽  
...  
Keyword(s):  
Linear Regression ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Forest Biomass ◽  
Above Ground Biomass ◽  
Stepwise Linear Regression ◽  
Water Index ◽  
Global Environmental ◽  
Normalized Difference Water Index ◽  
Sentinel 2

  Forest biomass estimation is the central part of sustainable forest management to assess carbon stocks and carbon emissions from forest ecosystem. Sentinel-2 is state-of-art sensor with refined spatial and recurrent temporal resolution data. The present study explored the potential of Sentinel-2 derived vegetation indices for above ground biomass prediction using four regression models (linear, exponential, power and logarithmic). Sentinel-2 indices includes Global environmental monitoring index, transformed normalized difference vegetation index, normalized difference water index, normalized difference infrared index and red-edge normalized difference vegetation index. The performances of Sentinel-2 indices were assessed by simple single variable (index) based regression for GEMI, TNDVI, NDII, NDWI and RENDVI versus AGB values. Further, stepwise linear regression was also developed in which used all indices entered into stepwise selection and the best index was selected in the final model. Results showed that linear model of all indices performance best compared to the rest three models and R2 values 0.12, 0.39, 0.46, 0.44 and 0.37 for Global environmental monitoring index, transformed normalized. Vegetation index, normalized difference water index, infrared index and red-edge vegetation index, respectively. Normalized difference water index was considered the best index among five computed indices in simple linear as well as in stepwise linear regression, whereas rest of the indices were removed because they were not significant under the stepwise criteria. Further, the accuracy of normalized difference water index model was determined by root mean square error and final prediction model has 28.27 t/ha error for both simple linear and stepwise linear regression. Therefore, normalized difference water index was selected for biomass mapping and resultant biomass showed up to 339 t/ha in the study area. The resultant biomass map also showed consistency with global datasets which include global forest canopy height and global forest tree cover change maps. The study suggest that Sentinel-2 product has great potential to estimate above ground  biomass with accuracy and can be used for large scale mapping in combination with national forest inventory for carbon emission accounting.    

scholarly journals Surface Moisture and Vegetation Cover Analysis for Drought Monitoring in the Southern Kruger National Park Using Sentinel-1, Sentinel-2, and Landsat-8

2018 ◽  
Vol 10 (9) ◽  
pp. 1482 ◽  
Author(s):  
Marcel Urban ◽  
Christian Berger ◽  
Tami Mudau ◽  
Kai Heckel ◽  
John Truckenbrodt ◽  
...  
Keyword(s):  
Vegetation Structure ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Laser Scanner ◽  
Observation Time ◽  
Drought Monitoring ◽  
Landsat 8 ◽  
Moisture Index ◽  
Surface Moisture ◽  
Sentinel 2

During the southern summer season of 2015 and 2016, South Africa experienced one of the most severe meteorological droughts since the start of climate recording, due to an exceptionally strong El Niño event. To investigate spatiotemporal dynamics of surface moisture and vegetation structure, data from ESA’s Copernicus Sentinel-1/-2 and NASA’s Landsat-8 for the period between March 2015 and November 2017 were utilized. In combination, these radar and optical satellite systems provide promising data with high spatial and temporal resolution. Sentinel-1 C-band data was exploited to derive surface moisture based on a hyper-temporal co-polarized (vertical-vertical—VV) radar backscatter change detection approach, describing dynamics between dry and wet seasons. Vegetation information from a TLS (Terrestrial Laser Scanner)-derived canopy height model (CHM), as well as the normalized difference vegetation index (NDVI) from Sentinel-2 and Landsat-8, were utilized to analyze vegetation structure types and dynamics with respect to the surface moisture index (SurfMI). Our results indicate that our combined radar–optical approach allows for a separation and retrieval of surface moisture conditions suitable for drought monitoring. Moreover, we conclude that it is crucial for the development of a drought monitoring system for savanna ecosystems to integrate land cover and vegetation information for analyzing surface moisture dynamics derived from Earth observation time series.

scholarly journals APPLICATION OF REMOTE SENSING FOR HIGH MOUNTAIN ECOSYSTEM CONDITION ASSESSMENT (SOUTH WEST RILA MOUNTAIN - BULGARIA)

2018 ◽  
pp. 35-40
Author(s):  
Kostadin Katrandzhiev
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Actual Condition ◽  
High Mountain ◽  
Mountain Ecosystems ◽  
Water Index ◽  
Mountain Ecosystem ◽  
South West ◽  
Normalized Difference Water Index ◽  
Sentinel 2

On basis of multispectral satellite data from Sentinel 2, an assessment of high mountain ecosystems condition is executed in selected territories of South West Rila Mountain. To define their actual condition, values of Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Differential Greenness Index (NDGI) were computed. The obtained values of these indices are presented as graphic images and thematic maps showing spatial distribution of the actual condition of high mountain ecosystems in the studied territories of Rila Mountain. The obtained results can be used for further assessment of ecosystem services provided by described ecosystems.

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