scholarly journals Uso de sensores remotos en la determinación del forraje disponible de Urochloa humidicola cv. Llanero bajo pastoreo en la Altillanura colombiana

2021 ◽  
Vol 9 (3) ◽  
pp. 376-382
Author(s):  
Raúl Alejandro Díaz Giraldo ◽  
Mauricio Álvarez de León ◽  
Otoniel Pérez López
Keyword(s):  
Near Infrared ◽  
Agricultural Research ◽  
Coefficient Of Determination ◽  
Research Centre ◽  
Landsat 8 ◽  
Pastoral Systems ◽  
Forage Availability ◽  
Vegetation Indexes ◽  
Remote Sensing Techniques ◽  
Sentinel 2A

Modernization of pastoral systems based on the use of Urochloa species in the Colombian Eastern Llanos need the use of remote sensing techniques from satellite platforms to estimate amount of offered forage. In the Carimagua Research Centre of the Colombian Corporation for Agricultural Research (Agrosavia), an Urochloa humidicola cv. Llanero pasture was evaluated using Landsat 8 and Sentinel 2A images. The NDVI, SAVI, EVI y GNDVI vegetation indexes determined by using the blue, green, red and near infrared bands; and the results analyzed with the R free software, to relate those indexes with forage availability field measures taken during the dry season. Forage availability ranged between 290 and 656 kg DM ha-1 and the vegetation indexes for the Landsat 8 and Sentinel 2A sensors were: NDVI = 0.67 (±0.037) and 0.69 (±0.061); SAVI = 0.48 (±0.048) and 0.41 (±0.046); EVI = 0.70 (±0.052) and 0.41 (±0.047); y GNDVI = 0.60 (±0.028) and 0.70 (±0.034), respectively. The relationships between vegetation indexes and forage availability were linear. The Coefficient of Determination (R2= 0.56‒0.72) and the Mean Square Error (MSR =63.95‒80.16) of the prediction equations were used. In conclusion, under the conditions of the study, the EVI for Landsat 8 and NDVI for Sentinel 2A were considered adequate for estimating forage availability of Urochloa humidicola cv. Llanero.

scholarly journals SENSORIAMENTO REMOTO APLICADO A MODELAGEM DE PRODUTIVIDADE DA CULTURA DA CANA-DE-AÇÚCAR

2019 ◽  
Vol 34 (2) ◽  
pp. 263-270
Author(s):  
Victor Costa Leda ◽  
Aline Kuramoto Golçalves ◽  
Natalia da Silva Lima
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Sao Paulo ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
São Paulo ◽  
Vegetation Indexes ◽  
Production Area ◽  
Remote Sensing Techniques ◽  
Sugarcane Productivity

SENSORIAMENTO REMOTO APLICADO A MODELAGEM DE PRODUTIVIDADE DA CULTURA DA CANA-DE-AÇÚCAR   VICTOR COSTA LEDA1, ALINE KURAMOTO GOLÇALVES2, NATALIA DA SILVA LIMA3   1 Departamento de Solos e Recursos Ambientais, Universidade Paulista “Júlio de Mesquita Filho” – Unesp, Fazenda Experimental Lageado, Avenida Universitária, nº 3780, Altos do Paraíso, CEP 18610-034, Botucatu, São Paulo, Brasil, [email protected]. 2 Departamento de Solos e Recursos Ambientais, Universidade Paulista “Júlio de Mesquita Filho” – Unesp, Fazenda Experimental Lageado, Avenida Universitária, nº 3780, Altos do Paraíso, CEP 18610-034, Botucatu, São Paulo, Brasil, [email protected]. 3 Departamento de Solos e Recursos Ambientais, Universidade Paulista “Júlio de Mesquita Filho” – Unesp, Fazenda Experimental Lageado, Avenida Universitária, nº 3780, Altos do Paraíso, CEP 18610-034, Botucatu, São Paulo, Brasil, [email protected].   RESUMO: O trabalho objetivou modelar as correlações de produtividade da cana-de-açúcar com índices de vegetação obtidos por meio de análise de imagens orbitais. Para análise, foram elaborados modelos matemáticos que expliquem a produtividade da cana-de-açúcar por meio das técnicas de geoprocessamento e sensoriamento remoto. O experimento foi realizado na área de produção comercial da Agrícola Rio Claro, parceira do grupo Zilor, que está localizada nos municípios de Lençóis Paulista e Pratânia, SP. A área ocupa aproximadamente 6000 ha, com altimetrias variando entre 600 e 700 m. Foi constatado que as modelagens foram satisfatórias, variando o coeficiente de determinação entre 0,15 a 0,97, sendo que, em períodos de colheita com elevados coeficientes de determinação, podem geralmente ser encontradas áreas de forma aglomerada, o que sugere uma menor incidência de variáveis. Enquanto áreas que apresentaram coeficientes de determinação baixos, podem ser explicadas devido a fatores como, dispersão dos talhões na área, classes de solo, precipitação e variedades da cultura, provavelmente distintos.   Palavras-chaves: índices de vegetação, Landsat 8, regressão linear múltipla.   REMOTE SENSING FOR THE SUGARCANE PRODUCTIVITY MODELING   ABSTRACT: The aim of this study was to model the sugarcane productivity correlations with vegetation indexes obtained through orbital image analysis. From the analysis was elaborated      mathematical models to explain sugarcane productivity through geoprocessing and remote sensing techniques. The experiment was carried out in the commercial production area of Agrícola Rio Claro, a partner of the Zilor group, located in the municipalities of Lençóis Paulista and Pratânia, SP, with approximately 6,000 hectares, with altimetry varying between 600 and 700 meters. It was verified that the modeling was satisfactory, varying the coefficient of determination between 0,15 and 0,97. Once      in periods with high determination coefficients, areas of agglomerated form can usually be found, which suggests a lower incidence of variables. While, in periods with low determination coefficients, can be explain due to listed factors that occurred as dispersion of the stands in the area, classes of soil, precipitation and probably different varieties of the crop.   Keywords: vegetation index, landsat8, multiple linear regression.

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.

scholarly journals Fuzzy Object-Based Image Analysis Methods Using Sentinel-2A and Landsat-8 Data to Map and Characterize Soil Surface Residue

2019 ◽  
Vol 11 (21) ◽  
pp. 2583 ◽  
Author(s):  
Payam Najafi ◽  
Hossein Navid ◽  
Bakhtiar Feizizadeh ◽  
Iraj Eskandari ◽  
Thomas Blaschke
Keyword(s):  
Image Analysis ◽  
Agricultural Research ◽  
Soil Surface ◽  
Control Measures ◽  
Landsat 8 ◽  
Surface Residue ◽  
Ground Truth Data ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Sentinel 2A

Soil degradation, defined as the lowering and loss of soil functions, is becoming a serious problem worldwide and threatens agricultural production and terrestrial ecosystems. The surface residue of crops is one of the most effective erosion control measures and it increases the soil moisture content. In some areas of the world, the management of soil surface residue (SSR) is crucial for increasing soil fertility, maintaining high soil carbon levels, and reducing the degradation of soil due to rain and wind erosion. Standard methods of measuring the residue cover are time and labor intensive, but remote sensing can support the monitoring of conservation tillage practices applied to large fields. We investigated the potential of per-pixel and object-based image analysis (OBIA) for detecting and estimating the coverage of SSRs after tillage and planting practices for agricultural research fields in Iran using tillage indices for Landsat-8 and novel indices for Sentinel-2A. For validation, SSR was measured in the field through line transects at the beginning of the agricultural season (prior to autumn crop planting). Per-pixel approaches for Landsat-8 satellite images using normalized difference tillage index (NDTI) and simple tillage index (STI) yielded coefficient of determination (R2) values of 0.727 and 0.722, respectively. We developed comparable novel indices for Sentinel-2A satellite data that yielded R2 values of 0.760 and 0.759 for NDTI and STI, respectively, which means that the Sentinel data better matched the ground truth data. We tested several OBIA methods and achieved very high overall accuracies of up to 0.948 for Sentinel-2A and 0.891 for Landsat-8 with a membership function method. The OBIA methods clearly outperformed per-pixel approaches in estimating SSR and bear the potential to substitute or complement ground-based techniques.

scholarly journals Satellite Imagery for Monitoring and Mapping Soil Chromium Pollution in a Mine Waste Dump

2021 ◽  
Author(s):  
Vahid Khosravi ◽  
Faramarz Doulati Ardejani ◽  
Asa Gholizadeh ◽  
Mohammadmehdi Saberioon
Keyword(s):  
Satellite Imagery ◽  
Satellite Images ◽  
Near Infrared ◽  
Prediction Models ◽  
Fitness Function ◽  
Reflectance Spectroscopy ◽  
Landsat 8 ◽  
Least Squares Regression ◽  
Cr Concentration ◽  
Sentinel 2A

Weathering and oxidation of sulphide minerals in mine wastes release toxic elements in surrounding environments. As an alternative to traditional sampling and chemical analysis methods, the capability of proximal and remote sensing techniques was investigated in this study to predict Chromium (Cr) concentration in 120 soil samples collected from a dumpsite in Sarcheshmeh copper mine, Iran. The samples mineralogy and Cr concentration were determined and were then subjected to laboratory reflectance spectroscopy in the range of Visible--Near Infrared--Shortwave Infrared (VNIR–SWIR: 350–2500 nm). The raw spectra were pre-processed using Savitzky–Golay First-Derivative (SG-FD) and Savitzky–Golay Second-Derivative (SG-SD) algorithms. The important wavelengths were determined using correlation analysis, Partial Least Squares Regression (PLSR) and Genetic Algorithm (GA). Artificial Neural Networks (ANN), Stepwise Multiple Linear Regression (SMLR) and PLSR data mining methods were applied to the selected spectral variables to assess Cr concentration. The developed models were then applied to the selected bands of Aster, Hyperion, Sentinel-2A and Landsat 8-OLI satellite images of the area. Afterwards, rasters obtained from the best prediction model were segmented using a binary fitness function. According to the outputs of the laboratory reflectance spectroscopy, the highest prediction accuracy was obtained using ANN applied to the SD pre-processed spectra with R2 = 0.91, RMSE = 8.73 mg.kg-1 and RPD = 2.76. SD-ANN also showed an acceptable performance on mapping the spatial distribution of Cr using Ordinary Kriging (OK) technique. Using satellite images, SD-SMLR provided the best prediction models with R2 values of 0.61 and 0.53 for Hyperion and Sentinel-2A, respectively. This led to the higher visual similarity of the segmented Hyperion and Sentinel-2A images with the Cr distribution map. The findings of this study indicated that applying the best prediction models obtained by spectroscopy to the selected wavebands of Hyperion and Sentinel-2A satellite imagery could be considered as a promising technique for rapid, cost-effective and eco-friendly assessment of Cr concentration in highly heterogeneous mining areas of Sarcheshmeh in Iran.

scholarly journals Aboveground Biomass Estimates of Araucaria angustifolia (Bertol.) Kuntze, Using Vegetation Indexes in Wolrdview-2 Image

2019 ◽  
Vol 11 (11) ◽  
pp. 93
Author(s):  
Luiz Carlos Pietrowski Basso ◽  
Vagner Alex Pesck ◽  
Mailson Roik ◽  
Afonso Figueiredo Filho ◽  
Thiago Floriani Stepka ◽  
...  
Keyword(s):  
Relative Error ◽  
Near Infrared ◽  
Direct Method ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Coefficient Of Determination ◽  
Araucaria Angustifolia ◽  
Spectral Bands ◽  
Vegetation Indexes ◽  
Satellite Image Processing

The present research aims to evaluate the biomass estimates of Araucaria angustifolia (Bertol.) Kuntze trees obtained by the direct method, then present results generated from a 2.0 m resolution spectral image Worldview-2 satellite. The quantification of the biomass in the field was first carried out of 29 trees of the specie of interest with DBH &ge; 40 cm and then with the image aid the crowns were delimited for analysis. From the spectral bands (B2-blue, B3-green, B4-yellow, B5-red, B6-near red, B7-near infrared 2 and B8-near infrared 2), it was possible to obtain vegetation indexes proposed by the literature (NDVI, NDVI_2, RS and SAVI_0,25) and later incorporated with dendrometric data a correlation matrix was formed. Additionally, mathematical equations were used to estimate biomass and carbon as a function of dendrometric variables and information obtained from the satellite image processing. From these equations, the ones that presented better results were those that contained independent dendrometric variables (DBH) and those that contained vegetation indices (NDVI_2 and NDVI). For the dendrometers, the relative error found was 14.42% and 14.32% for biomass and carbon respectively, while for the digital ones, NDVI_2 found a relative error of 37.82% and an adjusted coefficient of determination of 0.88 in the biomass equations. In the carbon equations, the NDVI variable presented the best results, being 38.56% the relative error and 0.87 the determination coefficient.

scholarly journals Analysis of Landsat-8 OLI Imagery for Estimating Exposed Bedrock Fractions in Typical Karst Regions of Southwest China Using a Karst Bare-Rock Index

2018 ◽  
Vol 10 (9) ◽  
pp. 1321 ◽  
Author(s):  
Jie Pei ◽  
Li Wang ◽  
Ni Huang ◽  
Jing Geng ◽  
Jianhua Cao ◽  
...  
Keyword(s):  
Near Infrared ◽  
Southwest China ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Independent Experiment ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Karst Rocky Desertification ◽  
Rocky Desertification ◽  
Bare Rock

Karst rocky desertification (KRD) has become the primary ecoenvironmental problem in the karst regions of southwest China. The rapid and efficient acquisition of exposed bedrock fractions (EBF) is crucial for the monitoring and assessment of KRD degree and distribution within the highly heterogeneous landscapes. Remote-sensing indices provide a useful method for the quick mapping of the EBF at large scales. The currently available rock indices, however, are faced with insensitivity to bedrock change characteristics, which greatly limits their performances and suitability. To address this problem, we proposed a novel karst bare-rock index (KBRI) that applies shortwave-infrared (SWIR) and near-infrared (NIR) bands from Landsat-8 OLI imagery to maximally distinguish between exposed bedrock and other land cover types in southwest China. A linear regression model was thus established between KBRI and the EBF derived from in situ measurements. The model developed here was then validated with an independent experiment and applied over a large geographic area to produce regional maps of EBF in southwest China. Experimental results showed good performance on root mean square error (5.59%), mean absolute error (4.63%), root mean absolute percentage error (13.59%), and coefficient of determination (0.72), respectively. The advantages of the proposed method are reflected in its simplicity and minimal requirements for auxiliary data while still achieving comparatively better accuracy than existing related indices. Thus, the KBRI has the great potential for the application in other regions around the world with the similar geological backgrounds, thereby helping to address the similar or other related environmental issues. Results of this study provide baseline data for the KRD assessment and karst-ecosystem management in southwest China.

scholarly journals Modelagem de variáveis dendrométricas em povoamentos florestais utilizando imagens de média resolução espacial (Modeling of biophysical variables in forest stands using medium spatial resolution images)

2016 ◽  
Vol 9 (6) ◽  
pp. 1969
Author(s):  
Elisiane Alba ◽  
Emanuel Araújo Silva ◽  
Juliana Marchesan ◽  
Letícia Pedrali ◽  
Rudiney Soares Pereira ◽  
...  
Keyword(s):  
Population Density ◽  
Near Infrared ◽  
Tree Density ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Landsat 8 Oli ◽  
Forest Stands ◽  
Infrared Band ◽  
Radiometric Correction ◽  
Vegetation Indexes

Objetivou-se avaliar as imagens Landsat 8/OLI na obtenção de estimativas do volume florestal e densidade populacional de plantios de E. grandis. Para tanto, utilizaram-se 42 unidades amostrais de povoamentos com 18 e 25 anos, mensurando-se os parâmetros dendrométricos Diâmetro à Altura do Peito (DAP), altura total e densidade de árvores. Foi realizada a correção radiométrica da imagem Landsat 8/OLI, obtendo a reflectância de superfície das bandas e índices de vegetação, a qual foi relacionada com as variáveis florestais, ajustando equações de estimativas por meio do método forward. Para os plantios com 18 anos, a equação ajustada explicou 87% da variabilidade do volume com as variáveis SAVI e NDVI presentes no modelo. A densidade populacional foi explicada pelo SR e DVI (R²=0,56). Aos 25 anos, o modelo contendo a banda do infravermelho próximo (B5) e o índice SR respondeu a 92% da variação total do volume florestal.  Nesta idade, a densidade populacional não apresentou correlação positiva. As propriedades espectrais da imagem apresentaram sensibilidade às variáveis dendrométricas, permitindo o monitoramento do desenvolvimento dos povoamentos florestais, justificando a aplicabilidade deste método.    A B S T R A C T This study aims at evaluate Landsat 8/OLI images in obtaining of estimates of the volume and tree density in plantations E. grandis. Therefore, was used 42 sampling unities of stands with 18 e 25 years, measurand the dendrometric parameters Diameter at Breast Height, total height and tree density. Was performed the radiometric correction of the Landsat 8/OLI image, obtaining the surface reflectance of the bands and vegetation indexes, which was related with variables forestry, adjusting equation of estimates through of the method forward. For plantations with 18 years, adjusting equation explained 87% of the volume variability with the variables SAVI and NDVI present in the model. Already the population density was explained by indexes SR and DVI (R²= 0.56). At 25 years, the model containg the near infrared band (B5) and the SR index responded to 92% of the total variation of the volume forestry. This age, the population density showed no positive correlation. The spectral properties of the image demonstrated sensitivity to variables dendrometric, allowing the monitoring of the development of forest stands, justifying the applicability of this method. Keywords: index vegetation, spectral reflectance, wood volume.   

scholarly journals IDENTIFICATION OF MANGROVE FORESTS USING MULTISPECTRAL SATELLITE IMAGERIES

2019 ◽  
Vol 16 (1) ◽  
pp. 63
Author(s):  
Anang Dwi Purwanto ◽  
Wikanti Asriningrum
Keyword(s):  
Mangrove Forest ◽  
Near Infrared ◽  
Image Data ◽  
Short Wave ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Visual Identification ◽  
Red Edge ◽  
Swir Band ◽  
Sentinel 2A

The visual identification of mangrove forests is greatly constrained by combinations of RGB composite. This research aims to determine the best combination of RGB composite for identifying mangrove forest in Segara Anakan, Cilacap using the Optimum Index Factor (OIF) method. The OIF method uses the standard deviation value and correlation coefficient from a combination of three image bands. The image data comprise Landsat 8 imagery acquired on 30 May 2013, Sentinel 2A imagery acquired on 18 March 2018 and images from SPOT 6 acquired on 10 January 2015. The results show that the band composites of 564 (NIR+SWIR+Red) from Landsat 8 and 8a114 (Vegetation Red Edge+SWIR+Red) from Sentinel 2A are the best RGB composites for identifying mangrove forest, in addition to those of 341 (Red+NIR+Blue) from SPOT 6. The near-infrared (NIR) and short-wave infrared (SWIR) bands play an important role in determining mangrove forests. The properties of vegetation are reflected strongly at the NIR wavelength and the SWIR band is very sensitive to evaporation and the identification of wetlands.

scholarly journals Radiometric Correction of Landsat-8 and Sentinel-2A Scenes Using Drone Imagery in Synergy with Field Spectroradiometry

2018 ◽  
Vol 10 (11) ◽  
pp. 1687 ◽  
Author(s):  
Joan-Cristian Padró ◽  
Francisco-Javier Muñoz ◽  
Luis Ávila ◽  
Lluís Pesquer ◽  
Xavier Pons
Keyword(s):  
Near Infrared ◽  
Field Measurements ◽  
Systematic Sampling ◽  
Landsat 8 ◽  
Radiometric Correction ◽  
Multispectral Data ◽  
Central Hypothesis ◽  
Sentinel 2A ◽  
Sentinel 2

The main objective of this research is to apply unmanned aerial system (UAS) data in synergy with field spectroradiometry for the accurate radiometric correction of Landsat-8 (L8) and Sentinel-2 (S2) imagery. The central hypothesis is that imagery acquired with multispectral UAS sensors that are well calibrated with highly accurate field measurements can fill in the scale gap between satellite imagery and conventional in situ measurements; this can be possible by sampling a larger area, including difficult-to-access land covers, in less time while simultaneously providing good radiometric quality. With this aim and by using near-coincident L8 and S2 imagery, we applied an upscaling workflow, whereby: (a) UAS-acquired multispectral data was empirically fitted to the reflectance of field measurements, with an extensive set of radiometric references distributed across the spectral domain; (b) drone data was resampled to satellite grids for comparison with the radiometrically corrected L8 and S2 official products (6S-LaSRC and Sen2Cor-SNAP, respectively) and the CorRad-MiraMon algorithm using pseudo-invariant areas, such as reflectance references (PIA-MiraMon), to examine their overall accuracy; (c) then, a subset of UAS data was used as reflectance references, in combination with the CorRad-MiraMon algorithm (UAS-MiraMon), to radiometrically correct the matching bands of UAS, L8, and S2; and (d) radiometrically corrected L8 and S2 scenes obtained with UAS-MiraMon were intercompared (intersensor coherence). In the first upscaling step, the results showed a good correlation between the field spectroradiometric measurements and the drone data in all evaluated bands (R2 > 0.946). In the second upscaling step, drone data indicated good agreement (estimated from root mean square error, RMSE) with the satellite official products in visible (VIS) bands (RMSEVIS < 2.484%), but yielded poor results in the near-infrared (NIR) band (RMSENIR > 6.688% was not very good due to spectral sensor response differences). In the third step, UAS-MiraMon indicated better agreement (RMSEVIS < 2.018%) than the other satellite radiometric correction methods in visible bands (6S-LaSRC (RMSE < 2.680%), Sen2Cor-SNAP (RMSE < 2.192%), and PIA-MiraMon (RMSE < 3.130%), but did not achieve sufficient results in the NIR band (RMSENIR < 7.530%); this also occurred with all other methods. In the intercomparison step, the UAS-MiraMon method achieved an excellent intersensor (L8-S2) coherence (RMSEVIS < 1%). The UAS-sampled area involved 51 L8 (30 m) pixels, 143 S2 (20 m) pixels, and 517 S2 (10 m) pixels. The drone time needed to cover this area was only 10 min, including areas that were difficult to access. The systematic sampling of the study area was achieved with a pixel size of 6 cm, and the raster nature of the sampling allowed for an easy but rigorous resampling of UAS data to the different satellite grids. These advances improve human capacities for conventional field spectroradiometry samplings. However, our study also shows that field spectroradiometry is the backbone that supports the full upscaling workflow. In conclusion, the synergy between field spectroradiometry, UAS sensors, and Landsat-like satellite data can be a useful tool for accurate radiometric corrections used in local environmental studies or the monitoring of protected areas around the world.

Sign in / Sign up

Export Citation Format

Share Document