USE OF LANDSAT-8 OLI IMAGERY AND LOCAL INDIGENOUS KNOWLEDGE FOR EELGRASS MAPPING IN EEYOU ISTCHEE

Abstract. The eastern coastline of James Bay (Eeyou Istchee) is known to be home to beds of subarctic eelgrass (Zostera marina L.). These eelgrass beds provide valuable habitat and food source for coastal and marine animals and contribute valuable ecosystem services such as stabilization of the shoreline all along the coast. Despite reports from Cree communities that eelgrass bed health has declined, limited research has been performed to assess and map the spatial distribution of eelgrass within the bay. This study aims to address that issue by evaluating the capability of Landsat-8 Operational Land Imager (OLI) imagery to establish a baseline map of eelgrass distribution in 2019 in the relatively turbid waters of Eeyou Istchee. Three images acquired in September 2019 were merged and classified using Random Forests into the following classes: Eelgrass, Turbid Water, Highly Turbid Water, and Optically Deep Water. The resulting classified image was validated against 108 ground truth data that were obtained from both the eelgrass health and Hydro-Quebec research team. The resulting overall accuracy was 78.7%, indicating the potential of the Random Forests classifier to estimate baseline eelgrass coverage in James Bay using Landsat-8 imagery. This project is part of a Cree driven project, the Coastal Habitat Comprehensive Research Program (CHCRP). The CHCRP aims to combine Cree's traditional knowledge with Western science to better understand environmental changes in the coastal ecosystems and ecosystem services of eastern James Bay. The study is funded by a MITACS grant sponsored by Niskamoon Corporation, an indigenous non-profit organization.

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Calibrating the Severity of Forest Defoliation by Pine Processionary Moth with Landsat and UAV Imagery

Sensors ◽

10.3390/s18103278 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3278 ◽

Cited By ~ 10

Author(s):

Kaori Otsu ◽

Magda Pla ◽

Jordi Vayreda ◽

Lluís Brotons

Keyword(s):

Forest Health ◽

Ground Truth ◽

Stress Index ◽

Coefficient Of Determination ◽

Thaumetopoea Pityocampa ◽

Landsat 8 ◽

Mediterranean Forests ◽

Ground Truth Data ◽

Pine Processionary Moth ◽

Processionary Moth

The pine processionary moth (Thaumetopoea pityocampa Dennis and Schiff.), one of the major defoliating insects in Mediterranean forests, has become an increasing threat to the forest health of the region over the past two decades. After a recent outbreak of T. pityocampa in Catalonia, Spain, we attempted to estimate the damage severity by capturing the maximum defoliation period over winter between pre-outbreak and post-outbreak images. The difference in vegetation index (dVI) derived from Landsat 8 was used as the change detection indicator and was further calibrated with Unmanned Aerial Vehicle (UAV) imagery. Regression models between predicted dVIs and observed defoliation degrees by UAV were compared among five selected dVIs for the coefficient of determination. Our results found the highest R-squared value (0.815) using Moisture Stress Index (MSI), with an overall accuracy of 72%, as a promising approach for estimating the severity of defoliation in affected areas where ground-truth data is limited. We concluded with the high potential of using UAVs as an alternative method to obtain ground-truth data for cost-effectively monitoring forest health. In future studies, combining UAV images with satellite data may be considered to validate model predictions of the forest condition for developing ecosystem service tools.

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Applying the Tropical Peatland Combustion Algorithm to Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) Imagery

Remote Sensing ◽

10.3390/rs12233958 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3958

Author(s):

Parwati Sofan ◽

David Bruce ◽

Eriita Jones ◽

M. Rokhis Khomarudin ◽

Orbita Roswintiarti

Keyword(s):

Spatial Resolution ◽

Fire Detection ◽

Ground Truth ◽

Thermal Infrared ◽

Landsat 8 ◽

Ground Truth Data ◽

Spectral Instrument ◽

Tropical Peatland ◽

Tropical Environments ◽

Sentinel 2

This study establishes a new technique for peatland fire detection in tropical environments using Landsat-8 and Sentinel-2. The Tropical Peatland Combustion Algorithm (ToPeCAl) without longwave thermal infrared (TIR) (henceforth known as ToPeCAl-2) was tested on Landsat-8 Operational Land Imager (OLI) data and then applied to Sentinel-2 Multi Spectral Instrument (MSI) data. The research is aimed at establishing peatland fire information at higher spatial resolution and more frequent observation than from Landsat-8 data over Indonesia’s peatlands. ToPeCAl-2 applied to Sentinel-2 was assessed by comparing fires detected from the original ToPeCAl applied to Landsat-8 OLI/Thermal Infrared Sensor (TIRS) verified through comparison with ground truth data. An adjustment of ToPeCAl-2 was applied to minimise false positive errors by implementing pre-process masking for water and permanent bright objects and filtering ToPeCAl-2’s resultant detected fires by implementing contextual testing and cloud masking. Both ToPeCAl-2 with contextual test and ToPeCAl with cloud mask applied to Sentinel-2 provided high detection of unambiguous fire pixels (>95%) at 20 m spatial resolution. Smouldering pixels were less likely to be detected by ToPeCAl-2. The detected smouldering pixels from ToPeCAl-2 applied to Sentinel-2 with contextual testing and with cloud masking were only 35% and 56% correct, respectively; this needs further investigation and validation. These results demonstrate that even in the absence of TIR data, an adjusted ToPeCAl algorithm (ToPeCAl-2) can be applied to detect peatland fires at 20 m resolution with high accuracy especially for flaming. Overall, the implementation of ToPeCAl applied to cost-free and available Landsat-8 and Sentinel-2 data enables regular peatland fire monitoring in tropical environments at higher spatial resolution than other satellite-derived fire products.

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Methods for Earth-Observing Satellite Surface Reflectance Validation

Remote Sensing ◽

10.3390/rs11131543 ◽

2019 ◽

Vol 11 (13) ◽

pp. 1543

Author(s):

Badawi ◽

Helder ◽

Leigh ◽

Jing

Keyword(s):

Solar Spectrum ◽

Ground Truth ◽

Landsat 8 ◽

Surface Reflectance ◽

Radiometric Calibration ◽

Ground Truth Data ◽

Initial Validation ◽

Reliable Performance ◽

The Mean ◽

The U.S

In this study an initial validation of the Landsat 8 (L8) Operational Land Imager (OLI) Surface Reflectance (SR) product was performed. The OLI SR product is derived from the L8 Top-of-Atmosphere product via the Landsat Surface Reflectance Code (LaSRC) software and generated by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center. The goal of this study is to develop and evaluate proper validation methodology for the OLI L2 SR product. Validation was performed using near-simultaneous ground truth SR measurements during Landsat 8 overpasses at 13 sites located in the U.S., Brazil, Chile and France. The ground truth measurements consisted of field spectrometer measurements, automated hyperspectral ground measurements operated by the Radiometric Calibration Network (RadCalNet) and derived SR measurements from Airborne Observation Platforms (AOP) operated by the National Ecological Observatory Network (NEON). The 13 sites cover a broad range of 0–0.5 surface reflectance units across the reflective solar spectrum. Results show that the mean reflectance difference between OLI L2 SR products and ground truth measurements for the 13 validation sites and all bands was under 2.5%. The largest uncertainties of 11% and 8% were found in the CA and Blue bands, respectively; whereas, the longer wavelength bands were within 4% or less. Results consistently indicated similarity between the OLI L2 SR product and ground truth data, especially in longer wavelengths over dark and bright targets, while less reliable performance was observed in shorter wavelengths and sparsely vegetated targets.

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PEMETAAN HABITAT PERAIRAN DANGKAL MENGGUNAKAN CITRA RESOLUSI MENENGAH DENGAN METODE KLASIFIKASI BERBASIS PIKSEL (STUDI KASUS PULAU TIKUS)

JURNAL ENGGANO ◽

10.31186/jenggano.5.1.78-90 ◽

2020 ◽

Vol 5 (1) ◽

pp. 78-90

Author(s):

Ari Anggoro ◽

Zamdial Zamdial ◽

Dede Hartono ◽

Deddy Bakhtiar ◽

Nurlaila Ervina Herliany ◽

...

Keyword(s):

Maximum Likelihood ◽

Shallow Water ◽

Ground Truth ◽

Small Island ◽

Benthic Habitat ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Ground Truth Data ◽

Maximum Likelihood Classification ◽

Medium Resolution

Pulau Tikus adalah pulau kecil yang terletak di Kota Bengkulu yang memiliki potensi terumbu karang disekitar perairan dangkal. Tujuan penelitian ini untuk memetakan kawasan habitat perairan dangkal ekosistem terumbu karang Pulau Tikus menggunakan citra satelit Landsat 8 OLI dan menguji akurasi klasifikasi peta habitat perairan dangkal di Pulau Tikus. Metode klasifikasi yang digunakan adalah klasifikasi terbimbing maximum likelihood classification. Hasil klasifikasi citra Landsat 8 OLI berdasarkan skema klasifikasi yang digunakan dari lima kelas habitat di Pulau Tikus tersebut yaitu karang hidup seluas 71,46 ha, karang campur pasir 106,9425 ha, karang mati 67,365 ha, makro alga 31,815 ha, dan pasir 40,05 ha. Uji akurasi dari perbandingan hasil klasifikasi citra dan data lapangan mendapatkan total akurasi keseluruhan yaitu sebesar 77%.SHALLOW WATER HABITATS MAPPING USING A MEDIUM RESOLUTION IMAGE WITH CLASSIFICATION METHOD PIKSEL-BASED (CASE STUDY OF THE TIKUS ISLAND). Tikus Island is a small island which located in Bengkulu City has the potential of coral reefs around the shallow water. The aims of this research were to map the area of benthic habitat in Tikus Island Bengkulu using Landsat 8 OLI satellite imagery and to test the accuracy on the benthic habitat map in Tikus Island. The method used supervised classification using maximum likelihood classification. The result of Landsat 8 OLI classification base on the five class habitats classification scheme used obtained in Tikus island showed coral reef (71,46 ha), coral mix sand (106,9425 ha), dead coral (67,365 ha), macroalgae (31,815 ha), and sand (40,05 ha). Accuracy test from the comparison of classification results and ground truth data get a total overall accuracy of 77%.

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Geospatial Techniques for Paddy Crop Acreage and Yield Estimation

Current Journal of Applied Science and Technology ◽

10.9734/cjast/2020/v39i1430704 ◽

2020 ◽

pp. 71-79

Author(s):

Krishna Desai ◽

N. L. Rajesh ◽

U. K. Shanwad ◽

N. Ananda ◽

B. G. Koppalkar ◽

...

Keyword(s):

Biomass Yield ◽

Satellite Image ◽

Ground Truth ◽

Landsat 8 ◽

Yield Estimation ◽

Crop Acreage ◽

Relative Deviation ◽

Ground Truth Data ◽

Ground Truthing ◽

Paddy Crop

Paddy crop acreage and yield estimation using geospatial technology were carried out in North Eastern Dry Zone (Zone-2) covering Shorapur taluk, Yadgir district, Karnataka state, India, during rabi late sown or summer 2016-17 season. The study area is located between 16° 20ꞌ to 17° 45ꞌ north latitude and 76° 04ꞌ to 77° 42ꞌ east longitude, at an elevation of 428 meters above mean sea level. The RESOURCESAT-1 LISS III satellite image of 31st January 2017, 24th February 2017, 20th March 2017 and LANDSAT-8 of 15th April 2017 were used for paddy crop acreage estimation at taluk level. Paddy signatures were identified using ground truth GPS data and then, these temporal imageries were subjected to NDVI classification and estimated the paddy biomass and further validated with the ground-truthing in corresponding to Green Seeker NDVI value. The estimated paddy crop acreage through imagery NDVI were 2145.75 ha, 17602.21 ha, 19838 ha and 23004.01 ha area during Jan-2017, Feb-2017, March-2017 and April-2017 respectively. When these results were compared with acreage estimates as reported by the State Department of Agriculture, shown a relative deviation of 11.41, 35.78, 23.01& 3.89 per cent for Jan-2017, Feb-2017, March-2017 and April-2017 respectively. Therefore, LandSat-8 NDVI paddy acreage has showed significantly on par with the ground truth data at the crop harvest stage. Relative deviation of 10.75 for yield comparison among imagery NDVI biomass yield with the DOA yield estimation infer that NDVI biomass yield estimation would give better result at 90 days after sowing. Positive correlation of NDVI values with estimated acreage and yield, indicates that application of remote sensing techniques for forecasting paddy biomass yield is more accurate, economical and could be beneficial to the policy makers for quick decisions.

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Extraction of Spread Surface Water Body using Supervised and Unsupervised Classification Techniques

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f8421.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 2345-2350

Keyword(s):

Surface Water ◽

Ground Truth ◽

Unsupervised Classification ◽

Water Area ◽

Landsat 8 ◽

Classification Methods ◽

Statistical Parameters ◽

Ground Truth Data ◽

Classification Techniques ◽

Water Board

In this paper different classification techniques are applied to extract spread surface water area in the Nagarjuna sagar reservoir, Andhra Pradesh from Landsat-8 (OLI) image. In addition, the separability of reservoir features are tested to evaluate the thematic correctness of the classified data. This is to evaluate the application of a supervised and unsupervised classification techniques using the ERDAS software to extract the changes of surface water features for the period of 2014 to 2019. Furthermore, the statistical parameters are evaluated for the classification techniques. In supervised and unsupervised classification methods the minimum distance classifier gives better result (overall accuracy is 98.01%) than other classification methods. These obtained results are validated with ground truth data which is provided by Central Water-board Commission(CWC).

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Localized Learning of Downscaled Soil Moisture

10.20944/preprints201902.0046.v1 ◽

2019 ◽

Author(s):

Michael Lewis ◽

Andmorgan Fisher ◽

Clint Smith ◽

John Qu ◽

Paul Houser

Keyword(s):

Soil Moisture ◽

Large Scale ◽

Deterministic Model ◽

Ground Truth ◽

Small Scale ◽

Landsat 8 ◽

Ground Truth Data ◽

Local Soil ◽

Validation Experiment ◽

L Band

If given the correct remotely sensed information, machine learning can accurately describe soil moisture conditions in a heterogeneous region at the large scale based on soil moisture readings at the small scale through rule transference across scale. This paper reviews an approach to increase soil moisture resolution over a sample region over Australia using the Soil Moisture Active Passive (SMAP) sensor and Landsat 8 only and a validation experiment using Sentinal-2 and the Advanced Microwave Scanning Radiometer (AMSR-E) over Nevada. This approach uses an inductive localized approach, replacing the need to obtain a deterministic model in favor of a learning model. This model is adaptable to heterogeneous conditions within a single scene unlike traditional polynomial fitting models and has fixed variables unlike most learning models. For the purposes of this analysis, the SMAP 36 km soil moisture product is considered fully valid and accurate. Landsat bands coinciding in collection date with a SMAP capture are down sampled to match the resolution of the SMAP product. A series of indices describing the Soil-Vegetation-Atmosphere Triangle (SVAT) relationship are then produced, including two novel variables, using the down sampled Landsat bands. These indices are then related to the local coincident SMAP values to identify a series of rules or trees to identify the local rules defining the relationship between soil moisture and the indices. The defined rules are then applied to the Landsat image in the native Landsat resolution to determine local soil moisture. Ground truth comparison is done via a series of grids using point soil moisture samples and air-borne L-band Multibeam Radiometer (PLMR) observations done under the SMAPEx-5 campaign. This paper uses a random forest due to its highly accurate learning against local ground truth data yet easily understandable rules. The predictive power of the inferred learning soil moisture algorithm did well with a mean absolute error of 0.054 over an airborne L-band retrieved surface over the same region.

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Validation of Visually Interpreted Corine Land Cover Classes with Spectral Values of Satellite Images and Machine Learning

Remote Sensing ◽

10.3390/rs13050857 ◽

2021 ◽

Vol 13 (5) ◽

pp. 857

Author(s):

Orsolya Gyöngyi Varga ◽

Zoltán Kovács ◽

László Bekő ◽

Péter Burai ◽

Zsuzsanna Csatáriné Szabó ◽

...

Keyword(s):

Land Cover ◽

Statistical Tests ◽

Ground Truth ◽

Relevant Information ◽

Landsat 8 ◽

Visual Interpretation ◽

Accurate Analysis ◽

Ground Truth Data ◽

Corine Land Cover ◽

Linear Discriminant

We analyzed the Corine Land Cover 2018 (CLC2018) dataset to reveal the correspondence between land cover categories of the CLC and the spectral information of Landsat-8, Sentinel-2 and PlanetScope images. Level 1 categories of the CLC2018 were analyzed in a 25 km × 25 km study area in Hungary. Spectral data were summarized by land cover polygons, and the dataset was evaluated with statistical tests. We then performed Linear Discriminant Analysis (LDA) and Random Forest classifications to reveal if CLC L1 level categories were confirmed by spectral values. Wetlands and water bodies were the most likely to be confused with other categories. The least mixture was observed when we applied the median to quantify the pixel variance of CLC polygons. RF outperformed the LDA’s accuracy, and PlanetScope’s data were the most accurate. Analysis of class level accuracies showed that agricultural areas and wetlands had the most issues with misclassification. We proved the representativeness of the results with a repeated randomized test, and only PlanetScope seemed to be ungeneralizable. Results showed that CLC polygons, as basic units of land cover, can ensure 71.1–78.5% OAs for the three satellite sensors; higher geometric resolution resulted in better accuracy. These results justified CLC polygons, in spite of visual interpretation, can hold relevant information about land cover considering the surface reflectance values of satellites. However, using CLC as ground truth data for land cover classifications can be questionable, at least in the L1 nomenclature.

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Evaluation of Spatial Generalization Characteristics of a Robust Classifier as Applied to Coral Reef Habitats in Remote Islands of the Pacific Ocean

Remote Sensing ◽

10.3390/rs10111774 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1774 ◽

Cited By ~ 5

Author(s):

Justin Gapper ◽

Hesham El-Askary ◽

Erik Linstead ◽

Thomas Piechota

Keyword(s):

Remote Sensing ◽

Pacific Ocean ◽

Ground Truth ◽

Temporal Analysis ◽

Spectral Signature ◽

Landsat 8 ◽

Ground Truth Data ◽

Linear Discriminant ◽

Area Of Interest

This study was an evaluation of the spectral signature generalization properties of coral across four remote Pacific Ocean reefs. The sites under consideration have not been the subject of previous studies for coral classification using remote sensing data. Previous research regarding using remote sensing to identify reefs has been limited to in-situ assessment, with some researchers also performing temporal analysis of a selected area of interest. This study expanded the previous in-situ analyses by evaluating the ability of a basic predictor, Linear Discriminant Analysis (LDA), trained on Depth Invariant Indices calculated from the spectral signature of coral in one location to generalize to other locations, both within the same scene and in other scenes. Three Landsat 8 scenes were selected and masked for null, land, and obstructed pixels, and corrections for sun glint and atmospheric interference were applied. Depth Invariant Indices (DII) were then calculated according to the method of Lyzenga and an LDA classifier trained on ground truth data from a single scene. The resulting LDA classifier was then applied to other locations and the coral classification accuracy evaluated. When applied to ground truth data from the Palmyra Atoll location in scene path/row 065/056, the initial model achieved an accuracy of 80.3%. However, when applied to ground truth observations from another location within the scene, namely, Kingman Reef, it achieved an accuracy of 78.6%. The model was then applied to two additional scenes (Howland Island and Baker Island Atoll), which yielded an accuracy of 69.2% and 71.4%, respectively. Finally, the algorithm was retrained using data gathered from all four sites, which produced an overall accuracy of 74.1%.

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Land Surface Temperature Retrieval from LANDSAT-8 Ther-mal Infrared Sensor Data and Validation with Infrared Ther-mometer Camera

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.20.27418 ◽

2018 ◽

Vol 7 (4.20) ◽

pp. 601

Author(s):

Muhammad Mejbel Salih ◽

Oday Zakariya Jasim ◽

Khalid I. Hassoon ◽

Aysar Jameel Abdalkadhum

Keyword(s):

Land Cover ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Ground Truth ◽

Sensor Data ◽

Landsat 8 ◽

Infrared Sensor ◽

Ground Truth Data ◽

Land Cover Types

This paper illustrates a proposed method for the retrieval of land surface temperature (LST) from the two thermal bands of the LAND-SAT-8 data. LANDSAT-8, the latest satellite from Landsat series, launched on 11 February 2013, using LANDSAT-8 Operational Line Imager and Thermal Infrared Sensor (OLI & TIRS) satellite data. LANDSAT-8 medium spatial resolution multispectral imagery presents particular interest in extracting land cover, because of the fine spectral resolution, the radiometric quantization of 12 bits. In this search a trial has been made to estimate LST over Al-Hashimiya district, south of Babylon province, middle of Iraq. Two dates images acquired on 2nd &18th of March 2018 to retrieve LST and compare them with ground truth data from infrared thermometer camera (all the meas-urements contacted with target by using type-k thermocouple) at the same time of images capture. The results showed that the rivers had a higher LST which is different to the other land cover types, of less than 3.47 C ◦, and the LST different for vegetation and residential area were less than 0.4 C ◦ with correlation coefficient of the two bands 10 and 11 Rbnad10= 0.70, Rband11 = 0.89 respectively, for the im-aged acquired on the 2nd of march 2018 and Rband10= 0.70 and Rband11 = 0.72 on the 18th of march 2018. These results confirm that the proposed approach is effective for the retrieval of LST from the LANDSAT-8 Thermal bands, and the IR thermometer camera data which is an effective way to validate and improve the performance of LST retrieval. Generally the results show that the closer measure-ment taken from the scene center time, a better quality to classify the land cover. The purpose of this study is to assess the use of LAND-SAT-8 data to specify temperature differences in land cover and compare the relationship between land surface temperature and land cover types.

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