scholarly journals Comparative Evaluation of the Spectral and Spatial Consistency of Sentinel-2 and Landsat-8 OLI Data for Igneada Longos Forest

2019 ◽  
Vol 8 (2) ◽  
pp. 56 ◽  
Author(s):  
Maliheh Arekhi ◽  
Cigdem Goksel ◽  
Fusun Balik Sanli ◽  
Gizem Senel
Keyword(s):  
Vegetation Index ◽  
Data Retrieval ◽  
Forest Monitoring ◽  
Landsat 8 ◽  
Spring Season ◽  
Landsat 8 Oli ◽  
Atmospheric Effect ◽  
Spatial Consistency ◽  
The Mean ◽  
Sentinel 2

This study aims to test the spectral and spatial consistency of Sentinel-2 and Landsat-8 OLI data for the potential of monitoring longos forests for four seasons in Igneada, Turkey. Vegetation indices, including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI), were generated for the study area in addition to the five corresponding bands of Sentinel-2 and Landsat-8 OLI Images. Although the spectral consistency of the data was interpreted by cross-calibration analysis using the Pearson correlation coefficient, spatial consistency was evaluated by descriptive statistical analysis of investigated variables. In general, the highest correlation values were achieved for the images that were acquired in the spring season for almost all investigated variables. In the spring season, among the investigated variables, the Red band (B4), NDVI and EVI have the largest correlation coefficients of 0.94, 0.92 and 0.91, respectively. Regarding the spatial consistency, the mean and standard deviation values of all variables were consistent for all seasons except for the mean value of the NDVI for the fall season. As a result, if there is no atmospheric effect or data retrieval/acquisition error, either Landsat-8 or Sentinel-2 can be used as a combination or to provide the continuity data in longos monitoring applications. This study contributes to longos forest monitoring science in terms of remote sensing data analysis.

scholarly journals ACCURACY ANALYSIS OF SENTINEL 2A AND LANDSAT 8 OLI+ SATELLITE DATASETS OVER KANO STATE (NIGERIA) USING VEGETATION SPECTRAL INDICES

2020 ◽  
Vol XLIV-3/W1-2020 ◽  
pp. 65-72
Author(s):  
O. A. Isioye ◽  
E. A. Akomolafe ◽  
U. H. Ikwueze
Keyword(s):  
Vegetation Index ◽  
Vegetation Indices ◽  
Stress Index ◽  
Landsat 8 ◽  
Agricultural Practice ◽  
Landsat 8 Oli ◽  
Area Index ◽  
Spatial Consistency ◽  
The Mean ◽  
Sentinel 2A

Abstract. This study explores the capabilities of Sentinel-2 over Landsat-8 Operational Land Imager (OLI) imageries for vegetation monitoring in the vegetated region of Minjibir LGA in Kano State. Accurate vegetation mapping is essential for monitoring crop and sustainable agricultural practice. Vegetation indices, comprising the Normalized Difference Vegetation Index (NDVI), Green Chlorophyll Index (GCI), Leaf Area Index (LAI) and Moisture Stress Index (MSI) were determined for each year. The findings showed an increase in Sentinel 2A value of the vegetation indices with respect to Landsat 8 throughout the time of the study (2015–2019). The best average performance over the supervised classification was obtained using Sentinel-2A bands, which are dependent on the training sample and resolution. While the spectral consistency of the data was inferred by cross-calibration analysis using regression analysis. The spatial consistency was assessed by descriptive statistical analysis of examined variables. Regarding the spatial consistency, the mean and standard deviation values of all variables were steady for all seasons excluding for the mean value of the LAI and MSI. Based on this finding, it is recommended that Sentinel-2A data could be used as a complementary data source with Landsat 8 OLI in vegetation assessment.

scholarly journals Change Detection and Impact of Climate Changes to Iraqi Southern Marshes Using Landsat 2 MSS, Landsat 8 OLI and Sentinel 2 MSI Data and GIS Applications

2019 ◽  
Vol 9 (10) ◽  
pp. 2016 ◽  
Author(s):  
Bassim Mohammed Hashim ◽  
Maitham Abdullah Sultan ◽  
Mazin Najem Attyia ◽  
Ali A. Al Maliki ◽  
Nadhir Al-Ansari
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Maximum Temperature ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Large Area ◽  
Gis Applications ◽  
Average Maximum ◽  
Water Cover ◽  
Sentinel 2

Marshes represent a unique ecosystem covering a large area of southern Iraq. In a major environmental disaster, the marshes of Iraq were drained, especially during the 1990s. Since then, droughts and the decrease in water imports from the Tigris and Euphrates rivers from Turkey and Iran have prevented them from regaining their former extent. The aim of this research is to extract the values of the normalized difference vegetation index (NDVI) for the period 1977–2017 from Landsat 2 MSS (multispectral scanner), Landsat 8 OLI (operational land imager) and Sentinel 2 MSI (multi-spectral imaging mission) satellite images and use supervised classification to quantify land and water cover change. The results from the two satellites (Landsat 2 and Landsat 8) are compared with Sentinel 2 to determine the best tool for detecting changes in land and water cover. We also assess the potential impacts of climate change through the study of the annual average maximum temperature and precipitation in different areas in the marshes for the period 1981–2016. The NDVI analysis and image classification showed the degradation of vegetation and water bodies in the marshes, as vast areas of natural vegetation and agricultural lands disappeared and were replaced with barren areas. The marshes were influenced by climatic change, including rising temperature and the diminishing amount of precipitation during 1981–2016.

scholarly journals Mapping the Natural Distribution of Bamboo and Related Carbon Stocks in the Tropics Using Google Earth Engine, Phenological Behavior, Landsat 8, and Sentinel-2

2020 ◽  
Vol 12 (18) ◽  
pp. 3109 ◽  
Author(s):  
Manjunatha Venkatappa ◽  
Sutee Anantsuksomsri ◽  
Jose Alan Castillo ◽  
Benjamin Smith ◽  
Nophea Sasaki
Keyword(s):  
Vegetation Index ◽  
Google Earth ◽  
Carbon Stocks ◽  
Landsat 8 ◽  
Threshold Values ◽  
Landsat 8 Oli ◽  
Natural Distribution ◽  
Siem Reap ◽  
Google Earth Engine ◽  
Sentinel 2

Although vegetation phenology thresholds have been developed for a wide range of mapping applications, their use for assessing the distribution of natural bamboo and the related carbon stocks is still limited, especially in Southeast Asia. Here, we used Google Earth Engine (GEE) to collect time-series of Landsat 8 Operational Land Imager (OLI) and Sentinel-2 images and employed a phenology-based threshold classification method (PBTC) to map the natural bamboo distribution and estimate carbon stocks in Siem Reap Province, Cambodia. We processed 337 collections of Landsat 8 OLI for phenological assessment and generated 121 phenological profiles of the average vegetation index for three vegetation land cover categories from 2015 to 2018. After determining the minimum and maximum threshold values for bamboo during the leaf-shedding phenology stage, the PBTC method was applied to produce a seasonal composite enhanced vegetation index (EVI) for Landsat collections and assess the bamboo distributions in 2015 and 2018. Bamboo distributions in 2019 were then mapped by applying the EVI phenological threshold values for 10 m resolution Sentinel-2 satellite imagery by accessing 442 tiles. The overall Landsat 8 OLI bamboo maps for 2015 and 2018 had user’s accuracies (UAs) of 86.6% and 87.9% and producer’s accuracies (PAs) of 95.7% and 97.8%, respectively, and a UA of 86.5% and PA of 91.7% were obtained from Sentinel-2 imagery for 2019. Accordingly, carbon stocks of natural bamboo by district in Siem Reap at the province level were estimated. Emission reductions from the protection of natural bamboo can be used to offset 6% of the carbon emissions from tourists who visit this tourism-destination province. It is concluded that a combination of GEE and PBTC and the increasing availability of remote sensing data make it possible to map the natural distribution of bamboo and carbon stocks.

scholarly journals Monitoring environmental supporting conditions of a raised bog using remote sensing techniques

2018 ◽  
Vol 380 ◽  
pp. 9-15 ◽  
Author(s):  
Saheba Bhatnagar ◽  
Bidisha Ghosh ◽  
Shane Regan ◽  
Owen Naughton ◽  
Paul Johnston ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Edge Detection ◽  
Environmental Conditions ◽  
Vegetation Index ◽  
Limiting Factor ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Raised Bog ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Abstract. Conventional methods of monitoring wetlands and detecting changes over time can be time-consuming and costly. Inaccessibility and remoteness of many wetlands is also a limiting factor. Hence, there is a growing recognition of remote sensing techniques as a viable and cost-effective alternative to field-based ecosystem monitoring. Wetlands encompass a diverse array of habitats, for example, fens, bogs, marshes, and swamps. In this study, we concentrate on a natural wetland – Clara Bog, Co. Offaly, a raised bog situated in the Irish midlands. The aim of the study is to identify and monitor the environmental conditions of the bog using remote sensing techniques. Environmental conditions in this study refer to the vegetation composition of the bog and whether it is in an intact (peat-forming) or degraded state. It can be described using vegetation, the presence of water (soil moisture) and topography. Vegetation indices (VIs) derived from satellite data have been widely used to assess variations in properties of vegetation. This study uses mid-resolution data from Sentinel-2 MSI, Landsat 8 OLI for VI analysis. An initial study to delineate the boundary of the bog using the combination of edge detection and segmentation techniques namely, entropy filtering, canny edge detection, and graph-cut segmentation is performed. Once the bog boundary is defined, spectra of the delineated area are studied. VIs like NDVI, ARVI, SAVI, NDWI, derived using Sentinel-2 MSI and Landsat 8 OLI are analysed. A digital elevation model (DEM) was also used for better classification. All of these characteristics (features) serve as a basis for classifying the bog into broad vegetation communities (termed ecotopes) that indicate the quality of raised bog habitat. This analysis is validated using field derived ecotopes. The results show that, by using spectral information and vegetation index clustering, an additional linkage can be established between spectral RS signatures and wetland ecotopes. Hence, the benefit of the study is in understanding ecosystem (bog) environmental conditions and in defining appropriate metrics by which changes in the conditions can be monitored.

scholarly journals The Utility of the Upcoming HyspIRI’s Simulated Spectral Settings in Detecting Maize Gray Leafy Spot in Relation to Sentinel-2 MSI, VENµS, and Landsat 8 OLI Sensors

Agronomy ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 846
Author(s):  
Mbulisi Sibanda ◽  
Onisimo Mutanga ◽  
Timothy Dube ◽  
John Odindi ◽  
Paramu L. Mafongoya
Keyword(s):  
Food Security ◽  
Precision Agriculture ◽  
Leaf Spot ◽  
Gray Leaf Spot ◽  
Economic Losses ◽  
Leaf Spot Disease ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Maize Crop ◽  
Sentinel 2

Considering the high maize yield loses caused by incidences of disease, as well as incomprehensive monitoring initiatives in crop farming, there is a need for spatially explicit, cost-effective, and consistent approaches for monitoring, as well as for forecasting, food-crop diseases, such as maize Gray Leaf Spot. Such approaches are valuable in reducing the associated economic losses while fostering food security. In this study, we sought to investigate the utility of the forthcoming HyspIRI sensor in detecting disease progression of Maize Gray Leaf Spot infestation in relation to the Sentinel-2 MSI and Landsat 8 OLI spectral configurations simulated using proximally sensed data. Healthy, intermediate, and severe categories of maize crop infections by the Gray Leaf Spot disease were discriminated based on partial least squares–discriminant analysis (PLS-DA) algorithm. Comparatively, the results show that the HyspIRI’s simulated spectral settings slightly performed better than those of Sentinel-2 MSI, VENµS, and Landsat 8 OLI sensor. HyspIRI exhibited an overall accuracy of 0.98 compared to 0.95, 0.93, and 0.89, which were exhibited by Sentinel-2 MSI, VENµS, and Landsat 8 OLI sensor sensors, respectively. Furthermore, the results showed that the visible section, red-edge, and NIR covered by all the four sensors were the most influential spectral regions for discriminating different Maize Gray Leaf Spot infections. These findings underscore the potential value of the upcoming hyperspectral HyspIRI sensor in precision agriculture and forecasting of crop-disease epidemics, which are necessary to ensure food security.

scholarly journals Comparison of Multi-Temporal PlanetScope Data with Landsat 8 and Sentinel-2 Data for Estimating Airborne LiDAR Derived Canopy Height in Temperate Forests

2020 ◽  
Vol 12 (11) ◽  
pp. 1876 ◽  
Author(s):  
Katsuto Shimizu ◽  
Tetsuji Ota ◽  
Nobuya Mizoue ◽  
Hideki Saito
Keyword(s):  
Time Series ◽  
Prediction Accuracy ◽  
Time Series Data ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Forest Monitoring ◽  
Series Data ◽  
Landsat 8 ◽  
Multi Temporal ◽  
Sentinel 2

Developing accurate methods for estimating forest structures is essential for efficient forest management. The high spatial and temporal resolution data acquired by CubeSat satellites have desirable characteristics for mapping large-scale forest structural attributes. However, most studies have used a median composite or single image for analyses. The multi-temporal use of CubeSat data may improve prediction accuracy. This study evaluates the capabilities of PlanetScope CubeSat data to estimate canopy height derived from airborne Light Detection and Ranging (LiDAR) by comparing estimates using Sentinel-2 and Landsat 8 data. Random forest (RF) models using a single composite, multi-seasonal composites, and time-series data were investigated at different spatial resolutions of 3, 10, 20, and 30 m. The highest prediction accuracy was obtained by the PlanetScope multi-seasonal composites at 3 m (relative root mean squared error: 51.3%) and Sentinel-2 multi-seasonal composites at the other spatial resolutions (40.5%, 35.2%, and 34.2% for 10, 20, and 30 m, respectively). The results show that RF models using multi-seasonal composites are 1.4% more accurate than those using harmonic metrics from time-series data in the median. PlanetScope is recommended for canopy height mapping at finer spatial resolutions. However, the unique characteristics of PlanetScope data in a spatial and temporal context should be further investigated for operational forest monitoring.

scholarly journals Evaluation of the Consistency of Simultaneously Acquired Sentinel-2 and Landsat 8 Imagery on Plastic Covered Greenhouses

2020 ◽  
Vol 12 (12) ◽  
pp. 2015 ◽  
Author(s):  
Manuel Ángel Aguilar ◽  
Rafael Jiménez-Lao ◽  
Abderrahim Nemmaoui ◽  
Fernando José Aguilar ◽  
Dilek Koc-San ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Remote Sensing Techniques ◽  
Image Pairs ◽  
Study Sites ◽  
High Level ◽  
Sentinel 2

Remote sensing techniques based on medium resolution satellite imagery are being widely applied for mapping plastic covered greenhouses (PCG). This article aims at testing the spectral consistency of surface reflectance values of Sentinel-2 MSI (S2 L2A) and Landsat 8 OLI (L8 L2 and the pansharpened and atmospherically corrected product from L1T product; L8 PANSH) data in PCG areas located in Spain, Morocco, Italy and Turkey. The six corresponding bands of S2 and L8, together with the normalized difference vegetation index (NDVI), were generated through an OBIA approach for each PCG study site. The coefficient of determination (r2) and the root mean square error (RMSE) were computed in sixteen cloud-free simultaneously acquired image pairs from the four study sites to evaluate the coherence between the two sensors. It was found that the S2 and L8 correlation (r2 > 0.840, RMSE < 9.917%) was quite good in most bands and NDVI. However, the correlation of the two sensors fluctuated between study sites, showing occasional sun glint effects on PCG roofs related to the sensor orbit and sun position. Moreover, higher surface reflectance discrepancies between L8 L2 and L8 PANSH data, mainly in the visible bands, were always observed in areas with high-level aerosol values derived from the aerosol quality band included in the L8 L2 product (SR aerosol). In this way, the consistency between L8 PANSH and S2 L2A was improved mainly in high-level aerosol areas according to the SR aerosol band.

ANALISIS PERBANDINGAN METODE NORMALIZED VEGETATION INDEX DAN MAXIMUM LIKELIHOOD CLASSIFICATION UNTUK ANALISIS RUANG TERBUKA HIJAU (Studi Kasus di Kota Pekalongan, Jawa Tengah)

2021 ◽  
pp. 513
Author(s):  
Mohammad Slamet Sigit Prakoso ◽  
Rizki Dwi Safitri
Keyword(s):  
Maximum Likelihood ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Maximum Likelihood Classification

Ruang Terbuka Hijau (RTH) adalah suatu tempat yang luas dan terbuka yang dimaksudkan untuk penghijauan suatu kota, di mana di dalamnya ditumbuhi pepohonan. Dalam analisis ruang terbuka hijau dapat menggunakan beberapa metode, di antaranya yaitu metode Normalized Difference Vegetation Index (NDVI) dan metode Maximum Likelihood Classification. Tujuan penelitian ini untuk mengetahui perbedaan hasil dari analisis metode NDVI dan Maximum Likelihood Classification yang digunakan untuk mengetahui ruang terbuka hijau di Kota Pekalongan. Metode yang digunakan pada penelitian ini yaitu dengan menggunakan metode NDVI dan metode Maximum Likelihood Classification. Data yang digunakan yaitu Citra Landsat 8 OLI. Pengolahan data menggunakan software Arcgis 10.3. Hasil dari pengolahan berupa peta ruang terbuka hijau dari masing - masing metode. Secara kuantitatif dari hasil perhitungan luas metode NDVI, luas permukiman sebesar 3.016,53 ha, persawahan 609,39 ha, hutan kota 573,3 ha, dan badan air seluas 482,04 ha. Sedangkan untuk metode Maximum Likelihood Classification didapatkan hasil luas permukiman 2.278,26 ha, persawahan 1.141,83 ha, hutan kota 738,18 ha, dan badan air seluas 522,99 ha. Berdasarkan luasan RTH terhadap luas Kota Pekalongan, pada metode NDVI sebesar 25,2%, sedangkan untuk metode Maximum Likelihood Classification sebesar 40,1%. Dari hasil analisis diperoleh perbedaan luasan yang cukup signifikan yaitu pada luasan persawahan dan permukiman. Perbedaan hasil analisis terjadi akibat perbedaan klasifikasi warna citra pada saat pengolahan data.

scholarly journals USABILITY, STRENGTH AND PRACTICALITY OF THE UPCOMING HYSPIRI IN DETECTING MAIZE GRAY LEAFY SPOT IN RELATION TO SENTINEL-2 MSI, VENμS AND LANDSAT 8 OLI SPECTRAL BAND-SETTINGS

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 1015-1022
Author(s):  
M. Sibanda ◽  
O. Mutanga ◽  
T. Dube ◽  
J. Odindi ◽  
P. L. Mafongoya
Keyword(s):  
Disease Progression ◽  
Precision Agriculture ◽  
Spectral Band ◽  
Maize Yield ◽  
Gray Leaf Spot ◽  
Added Value ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
To Come ◽  
Sentinel 2

Abstract. Considering the high maize yield loses that are caused by diseases incidences as well as incomprehensive monitoring initiatives in the crop farming sector of agriculture, there is a need to come up with spatially explicit, cheap, fast and consistent approaches for monitoring as well as forecasting food crop diseases, such as maize gray leaf spot. This study, therefore, we sought to investigate the usability, strength and practicality of the forthcoming HyspIRI in detecting disease progression of Maize Gray leafy spot infections in relation to the Sentinel-2 MSI, Landsat 8 OLI spectral configurations. Maize Gray leafy spot disease progression that were discriminated based on partial least squares –discriminant analysis (PLS-DA) algorithm were (i) healthy, (ii) intermediate and (ii) severely infected maize crops. Comparatively, the results show that the HyspIRI’s simulated spectral settings slightly performed better than those of Sentinel-2 MSI, VENμS and Landsat 8 OLI sensor. HyspIRI exhibited an overall accuracy of 0.98 compared to 0.95, 0.93 and 0.89 exhibited by Sentinel-2 MSI, VENμS and Landsat 8 OLI sensor sensors, respectively. Further, the results showed that the visible section the red-edge and NIR covered by all the four sensors were the most influential spectral regions for discriminating different Maize Gray leafy spot infections. These findings underscore the added value and potential scientific breakthroughs likely to be brought about by the upcoming hyperspectral HyspIRI sensor in precision agriculture and forecasting of crop disease epidemics to ensure food security.

Evaluating the Persistence of Post-Wildfire Ash: A Multi-Platform Spatiotemporal Analysis

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 68
Author(s):  
Sarah A. Lewis ◽  
Peter R. Robichaud ◽  
Andrew T. Hudak ◽  
Eva K. Strand ◽  
Jan U. H. Eitel ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Field Measurements ◽  
Cost Effective ◽  
Spatiotemporal Analysis ◽  
Short Wave ◽  
Hydrologic Model ◽  
Landsat 8 ◽  
Data Volume ◽  
Sentinel 2

As wildland fires amplify in size in many regions in the western USA, land and water managers are increasingly concerned about the deleterious effects on drinking water supplies. Consequences of severe wildfires include disturbed soils and areas of thick ash cover, which raises the concern of the risk of water contamination via ash. The persistence of ash cover and depth were monitored for up to 90 days post-fire at nearly 100 plots distributed between two wildfires in Idaho and Washington, USA. Our goal was to determine the most ‘cost’ effective, operational method of mapping post-wildfire ash cover in terms of financial, data volume, time, and processing costs. Field measurements were coupled with multi-platform satellite and aerial imagery collected during the same time span. The image types spanned the spatial resolution of 30 m to sub-meter (Landsat-8, Sentinel-2, WorldView-2, and a drone), while the spectral resolution spanned visible through SWIR (short-wave infrared) bands, and they were all collected at various time scales. We that found several common vegetation and post-fire spectral indices were correlated with ash cover (r = 0.6–0.85); however, the blue normalized difference vegetation index (BNDVI) with monthly Sentinel-2 imagery was especially well-suited for monitoring the change in ash cover during its ephemeral period. A map of the ash cover can be used to estimate the ash load, which can then be used as an input into a hydrologic model predicting ash transport and fate, helping to ultimately improve our ability to predict impacts on downstream water resources.

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