Basic Remote Sensing Using Landsat Data

2015 ◽  
pp. 31-46
Keyword(s):  
Remote Sensing ◽  
Landsat Data

scholarly journals Estimating Forest Canopy Cover by Multiscale Remote Sensing in Northeast Jiangxi, China

Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 433
Author(s):  
Xiaolan Huang ◽  
Weicheng Wu ◽  
Tingting Shen ◽  
Lifeng Xie ◽  
Yaozu Qin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Forest Canopy ◽  
Canopy Cover ◽  
Google Earth ◽  
Tree Canopy ◽  
Landsat Data ◽  
High Resolution Data ◽  
Modis Ndvi ◽  
Tree Canopy Cover

This research was focused on estimation of tree canopy cover (CC) by multiscale remote sensing in south China. The key aim is to establish the relationship between CC and woody NDVI (NDVIW) or to build a CC-NDVIW model taking northeast Jiangxi as an example. Based on field CC measurements, this research used Google Earth as a complementary source to measure CC. In total, 63 sample plots of CC were created, among which 45 were applied for modeling and the remaining 18 were employed for verification. In order to ascertain the ratio R of NDVIW to the satellite observed NDVI, a 20-year time-series MODIS NDVI dataset was utilized for decomposition to obtain the NDVIW component, and then the ratio R was calculated with the equation R = (NDVIW/NDVI) *100%, respectively, for forest (CC >60%), medium woodland (CC = 25–60%) and sparse woodland (CC 1–25%). Landsat TM and OLI images that had been orthorectified by the provider USGS were atmospherically corrected using the COST model and used to derive NDVIL. R was multiplied for the NDVIL image to extract the woody NDVI (NDVIWL) from Landsat data for each of these plots. The 45 plots of CC data were linearly fitted to the NDVIWL, and a model with CC = 103.843 NDVIW + 6.157 (R2 = 0.881) was obtained. This equation was applied to predict CC at the 18 verification plots and a good agreement was found (R2 = 0.897). This validated CC-NDVIW model was further applied to the woody NDVI of forest, medium woodland and sparse woodland derived from Landsat data for regional CC estimation. An independent group of 24 measured plots was utilized for validation of the results, and an accuracy of 83.0% was obtained. Thence, the developed model has high predictivity and is suitable for large-scale estimation of CC using high-resolution data.

scholarly journals Melt Pond Retrieval Based on the LinearPolar Algorithm Using Landsat Data

2021 ◽  
Vol 13 (22) ◽  
pp. 4674
Author(s):  
Yuqing Qin ◽  
Jie Su ◽  
Mingfeng Wang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Relative Error ◽  
Large Scale ◽  
The Arctic ◽  
Landsat Data ◽  
Melt Pond ◽  
Melt Ponds ◽  
Very High ◽  
Sentinel 2

The formation and distribution of melt ponds have an important influence on the Arctic climate. Therefore, it is necessary to obtain more accurate information on melt ponds on Arctic sea ice by remote sensing. The present large-scale melt pond products, especially the melt pond fraction (MPF), still require verification, and using very high resolution optical satellite remote sensing data is a good way to verify the large-scale retrieval of MPF products. Unlike most MPF algorithms using very high resolution data, the LinearPolar algorithm using Sentinel-2 data considers the albedo of melt ponds unfixed. In this paper, by selecting the best band combination, we applied this algorithm to Landsat 8 (L8) data. Moreover, Sentinel-2 data, as well as support vector machine (SVM) and iterative self-organizing data analysis technique (ISODATA) algorithms, are used as the comparison and verification data. The results show that the recognition accuracy of the LinearPolar algorithm for melt ponds is higher than that of previous algorithms. The overall accuracy and kappa coefficient results achieved by using the LinearPolar algorithm with L8 and Sentinel-2A (S2), the SVM algorithm, and the ISODATA algorithm are 95.38% and 0.88, 94.73% and 0.86, and 92.40%and 0.80, respectively, which are much higher than those of principal component analysis (PCA) and Markus algorithms. The mean MPF (10.0%) obtained from 80 cases from L8 data based on the LinearPolar algorithm is much closer to Sentinel-2 (10.9%) than the Markus (5.0%) and PCA algorithms (4.2%), with a mean MPF difference of only 0.9%, and the correlation coefficients of the two MPFs are as high as 0.95. The overall relative error of the LinearPolar algorithm is 53.5% and 46.4% lower than that of the Markus and PCA algorithms, respectively, and the root mean square error (RMSE) is 30.9% and 27.4% lower than that of the Markus and PCA algorithms, respectively. In the cases without obvious melt ponds, the relative error is reduced more than that of those with obvious melt ponds because the LinearPolar algorithm can identify 100% of dark melt ponds and relatively small melt ponds, and the latter contributes more to the reduction in the relative error of MPF retrieval. With a wider range and longer time series, the MPF from Landsat data are more efficient than those from Sentinel-2 for verifying large-scale MPF products or obtaining long-term monitoring of a fixed area.

scholarly journals Contribuição do Estudo Aeromagnetométrico e de Imagens Orbitais (TM LANDSAT 5) ao Conhecimento do Arcabouço Geológico do Distrito Fluorítico de Santa Catarina (Brasil) e suas Implicações para a Prospecção de Fluorita

2002 ◽  
Vol 29 (2) ◽  
pp. 37
Author(s):  
ROSEMARY HOFF ◽  
ARTUR CEZAR BASTOS NETO ◽  
SÍLVIA BEATRIZ ALVES ROLIM
Keyword(s):  
Remote Sensing ◽  
Landsat Data ◽  
Santa Catarina ◽  
Structural Patterns ◽  
Relative Chronology ◽  
Geological Evolution ◽  
Previous Existence

Geophysical (aeromagnetometry) and remote sensing (TM LANDSAT) data allowed to recognize different structural patterns at the Santa Catarina fluorite district. These features correspond to N-S and NNE-SSW structural lineaments with potential environments for fluorite prospection. In detail, the used treatment allowed to identificate minor structures associated with these lineaments and showed that two principal structures carring mineralization prolongs themselves for dozens of kilometers in areas where previous existence were not even suspected. So a serie of data were searched about origin, evolution and relative chronology for the structures present in several directions, just like questions about the geological evolution of the district.

scholarly journals Mapping Forest Canopy Height in Mountainous Areas Using ZiYuan-3 Stereo Images and Landsat Data

Forests ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 105 ◽  
Author(s):  
Mingbo Liu ◽  
Chunxiang Cao ◽  
Yongfeng Dang ◽  
Xiliang Ni
Keyword(s):  
Remote Sensing ◽  
Forest Canopy ◽  
Remote Sensing Data ◽  
Canopy Height ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Landsat Data ◽  
Sensing Data ◽  
Forest Height ◽  
Reflectance Data

Forest canopy height is an important parameter for studying biodiversity and the carbon cycle. A variety of techniques for mapping forest height using remote sensing data have been successfully developed in recent years. However, the demands for forest height mapping in practical applications are often not met, due to the lack of corresponding remote sensing data. In such cases, it would be useful to exploit the latest, cheaper datasets and combine them with free datasets for the mapping of forest canopy height. In this study, we proposed a method that combined ZiYuan-3 (ZY-3) stereo images, Shuttle Radar Topography Mission global 1 arc second data (SRTMGL1), and Landsat 8 Operational Land Imager (OLI) surface reflectance data. The method consisted of three procedures: First, we extracted a digital surface model (DSM) from the ZY-3, using photogrammetry methods and subtracted the SRTMGL1 to obtain a crude canopy height model (CHM). Second, we refined the crude CHM and correlated it with the topographically corrected Landsat 8 surface reflectance data, the vegetation indices, and the forest types through a Random Forest model. Third, we extrapolated the model to the entire study area covered by the Landsat data, and obtained a wall-to-wall forest canopy height product with 30 m × 30 m spatial resolution. The performance of the model was evaluated by the Random Forest’s out-of-bag estimation, which yielded a coefficient of determination (R2) of 0.53 and a root mean square error (RMSE) of 3.28 m. We validated the predicted forest canopy height using the mean forest height measured in the field survey plots. The validation result showed an R2 of 0.62 and a RMSE of 2.64 m.

Remote Sensing-Based Evapotranspiration Modelling for Agricultural Water Management in the Limpopo Basin

2018 ◽  
pp. 50-85
Author(s):  
Berhanu F. Alemaw ◽  
Thebeyame Ronald Chaoka ◽  
Brigton Munyai
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Actual Evapotranspiration ◽  
Data Sets ◽  
Landsat Data ◽  
Agricultural Water ◽  
Agricultural Water Management ◽  
Factors Influencing ◽  
Five Factors ◽  
Semi Arid

The study was motivated by the need to determine the spatial variation of ET and to test the applicability of RS based methods in arid to semi-arid climates with limited ground-based measurements. In this paper we present results of an effort of determining spatial actual evapotranspiration in the Limpopo basin, the Notwane subcatchment in the south-eastern part of Botswana, using remote sensing data from MODIS and Landsat Data sets. The Simplified Surface Energy Balance Index (S-SEBI) was applied to determine actual evapotranspiration using the seven bands of Landsat and MODIS surface reflectance and temperature channels. Three different dates were used to estimate ET from both Landsat and MODIS scenes. The estimated ET values from the two sensors show approximately equally comparable results. An assessment was also conducted to determine the factors influencing evapotranspiration. No strong correlation was identified for ET against the five factors investigated: Net radiation, NDVI, Surface Temperature, emissivity and surface albedo.

scholarly journals UHI SPATIO-TEMPORAL ANALYSIS WITH GEOSPATIAL TECHNIQUES: A CASE OF AHMEDABAD CITY.

2021 ◽  
pp. 194-200
Author(s):  
Darshana Rawal ◽  
Vishal Gupta
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Land Surface ◽  
Google Earth ◽  
Landsat Data ◽  
Land Use Land Cover ◽  
Barren Land ◽  
Thermal Data ◽  
Spatio Temporal

Spatio-temporal changes in land use land cover (LULC) have been relevant factors in causing the changes in Urban Heat Island (UHI) pattern across rural and urban areas all over the world. Studies conducted have shown that the relation between LULC on scale of the UHI can be an important factor assessing the condition not only for a country but for environment of a city also. Over the years it is reflected in health of vegetation and urbanization pattern of cities. As the thermal remote sensing has been evolved, the measurement of the temperature through satellite products has become possible. Thermal data derived through remote sensing gives us birds-eye-view to see how the thermal data varies in the entire city. In this study such relations are shown over Ahmedabad city of India for the period of 2007 to 2020 using Landsat series satellite data. Land Surface Temperature (LST) is calculated using Google Earth Engine Platform Surface Brightness Temperature for Landsat data and using Radiative Transfer Equation for Landsat data. LST is correlated with land use land cover mainly Built-up, Vegetation, Barren land, Water & Other and corresponding Land Use and Land Cover respectively, and it is found that LST is positively related with all indices except for Normalize Difference Vegetation Index (NDVI) with strong negative correlation and R 2 of 0.51.

Producing Daily Landsat Snow Cover Time-Series Data

2021 ◽  
Author(s):  
Fatemeh Zakeri ◽  
Gregoire Mariethoz
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Snow Cover ◽  
Time Series Data ◽  
Landsat Imagery ◽  
Swiss Alps ◽  
Series Data ◽  
Landsat Data ◽  
Climate Data ◽  
Data Set

<p>Snow cover maps are critical for hydrological studies as well as climate change impacts assessment. Remote sensing plays a vital role in providing snow cover information. However, acquisition limitations such as clouds, shadows, or revisiting time limit accessing daily complete snow cover maps obtained from remote sensing. This study explores the generation of synthetic daily Landsat time-series data focusing on snow cover using available Landsat data and climate data for 2020 in the Western Swiss Alps (Switzerland). <br>Landsat surface reflectance is predicted using all available Landsat imagery from 1984 to2020 and ERA5 reanalysis precipitation and air temperature daily data in this study. For a given day where there is no Landsat data, the proposed procedure computes a similarity metric to find a set of days having a similar climatic pattern and for which satellite data is available. These best match images constitute possible snow cover scenarios on the target day and can be used as stochastic input to impact models. <br>Visual comparison and quantitative assessment are used to evaluate the accuracy of the generated images. In both accuracy assessments, some real Landsat data are omitted from the searching data set, and synthetic images are compared visually with real Landsat images. In the quantitative evaluation, the RSME between the real and artificial images is computed in a cross-validation fashion. Both accuracy procedures demonstrate that the combination of Landsat and climate data can predict Landsat's daily reflectance focusing on snow cover.</p>

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