Combining SPOT 5 imagery with plotwise and standwise forest data to estimate volume and biomass in mountainous coniferous site

2013 ◽  
Vol 5 (2) ◽  
Author(s):  
Petar Dimitrov ◽  
Eugenia Roumenina
Keyword(s):  
Near Infrared ◽  
Vegetation Indices ◽  
Correlation Coefficients ◽  
Test Site ◽  
National Forest Inventory ◽  
Linear Regression Models ◽  
Infrared Band ◽  
Topographic Correction ◽  
Maximal Correlation ◽  
Spot 5

AbstractIn this study, regression-based prediction of volume and aboveground biomass (AGB) of coniferous forests in a mountain test site was conducted. Two datasets — one with applied topographic correction and one without applied topographic correction — consisting of four spectral bands and six vegetation indices were generated from SPOT 5 multispectral image. The relationships between these data and ground data from field plots and national forest inventory polygons were examined. Strongest correlations of volume and AGB were observed with the near infrared band, regardless of the topographic correction. The maximal correlation coefficients when using plotwise data were −0.83 and −0.84 for the volume and AGB, respectively. The maximal correlation with standwise data was −0.63 for both parameters. The SCS+C topographic correction did not significantly affect the correlations between spectral data and forest parameters, but visually removed much of the topographically induced shading. Simple linear regression models resulted in relative RMSE of 32–33% using the plotwise data, and 43–45% using the standwise data. The importance of the source and the methodology used to obtain ground data for the successful modelling was pointed out.

scholarly journals TOPOGRAPHIC EFFECT ON SPECTRAL VEGETATION INDICES FROM LANDSAT TM DATA: IS TOPOGRAPHIC CORRECTION NECESSARY?

2016 ◽  
Vol 22 (1) ◽  
pp. 95-107 ◽  
Author(s):  
Eder Paulo Moreira* ◽  
Márcio de Morisson Valeriano ◽  
Ieda Del Arco Sanches ◽  
Antonio Roberto Formaggio
Keyword(s):  
Near Infrared ◽  
Incidence Angle ◽  
Spectral Band ◽  
Vegetation Indices ◽  
Topographic Effect ◽  
Topographic Correction ◽  
Spectral Bands ◽  
Spectral Vegetation Indices ◽  
Current Availability ◽  
Elevation Model

The full potentiality of spectral vegetation indices (VIs) can only be evaluated after removing topographic, atmospheric and soil background effects from radiometric data. Concerning the former effect, the topographic effect was barely investigated in the context of VIs, despite the current availability correction methods and Digital elevation Model (DEM). In this study, we performed topographic correction on Landsat 5 TM spectral bands and evaluated the topographic effect on four VIs: NDVI, RVI, EVI and SAVI. The evaluation was based on analyses of mean and standard deviation of VIs and TM band 4 (near-infrared), and on linear regression analyses between these variables and the cosine of the solar incidence angle on terrain surface (cos i). The results indicated that VIs are less sensitive to topographic effect than the uncorrected spectral band. Among VIs, NDVI and RVI were less sensitive to topographic effect than EVI and SAVI. All VIs showed to be fully independent of topographic effect only after correction. It can be concluded that the topographic correction is required for a consistent reduction of the topographic effect on the VIs from rugged terrain.

scholarly journals The Use of UAV Mounted Sensors for Precise Detection of Bark Beetle Infestation

2019 ◽  
Vol 11 (13) ◽  
pp. 1561 ◽  
Author(s):  
Tomáš Klouček ◽  
Jan Komárek ◽  
Peter Surový ◽  
Karel Hrach ◽  
Přemysl Janata ◽  
...  
Keyword(s):  
Bark Beetle ◽  
Near Infrared ◽  
Carbon Fixation ◽  
Water Cycle ◽  
Low Cost ◽  
Vegetation Indices ◽  
Nutrient Cycle ◽  
Visual Interpretation ◽  
Infrared Band ◽  
Individual Level

The bark beetle (Ips typographus) disturbance represents serious environmental and economic issue and presents a major challenge for forest management. A timely detection of bark beetle infestation is therefore necessary to reduce losses. Besides wood production, a bark beetle outbreak affects the forest ecosystem in many other ways including the water cycle, nutrient cycle, or carbon fixation. On that account, (not just) European temperate coniferous forests may become endangered ecosystems. Our study was performed in the unmanaged zone of the Krkonoše Mountains National Park in the northern part of the Czech Republic where the natural spreading of bark beetle is slow and, therefore, allow us to continuously monitor the infested trees that are, in contrast to managed forests, not being removed. The aim of this work is to evaluate possibilities of unmanned aerial vehicle (UAV)-mounted low-cost RGB and modified near-infrared sensors for detection of different stages of infested trees at the individual level, using a retrospective time series for recognition of still green but already infested trees (so-called green attack). A mosaic was created from the UAV imagery, radiometrically calibrated for surface reflectance, and five vegetation indices were calculated; the reference data about the stage of bark beetle infestation was obtained through a combination of field survey and visual interpretation of an orthomosaic. The differences of vegetation indices between infested and healthy trees over four time points were statistically evaluated and classified using the Maximum Likelihood classifier. Achieved results confirm our assumptions that it is possible to use a low-cost UAV-based sensor for detection of various stages of bark beetle infestation across seasons; with increasing time after infection, distinguishing infested trees from healthy ones grows easier. The best performance was achieved by the Greenness Index with overall accuracy of 78%–96% across the time periods. The performance of the indices based on near-infrared band was lower.

scholarly journals Spectral characterization of forest plantations with Landsat 8/OLI images for forest planning and management

2017 ◽  
Vol 52 (11) ◽  
pp. 1072-1079 ◽  
Author(s):  
Elisiane Alba ◽  
Eliziane Pivotto Mello ◽  
Juliana Marchesan ◽  
Emanuel Araújo Silva ◽  
Juliana Tramontina ◽  
...  
Keyword(s):  
Near Infrared ◽  
Vegetation Indices ◽  
Pinus Elliottii ◽  
Growth Stages ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Infrared Band ◽  
Near Infrared Reflectance ◽  
Planning And Management ◽  
Forest Planning And Management

Abstract: The objective of this work was to evaluate the use of Landsat 8/OLI images to differentiate the age and estimate the total volume of Pinus elliottii, in order to determine the applicability of these data in the planning and management of forest activity. Fifty-three sampling units were installed, and dendrometric variables of 9-and-10-year-old P. elliottii commercial stands were measured. The digital numbers of the image were converted into surface reflectance and, subsequently, vegetation indices were determined. Red and near-infrared reflectance values were used to differentiate the ages of the stands. Regression analysis of the spectral variables was used to estimate the total volume. Increase in age caused an addition in reflectance in the near-infrared band and a decrease in the red band. The general equation for estimating the total volume for P.elliottii had an R2adj of 0.67 with a Syx of 31.46 m3 ha-1. Therefore, the spectral data with medium spatial resolution from the Landsat 8/OLI satellite can be used to distinguish the growth stages of the stands and can, thus, be used in the planning and proper management of forest activity on a spatial and temporal scale.

scholarly journals Estimating Rice Agronomic Traits Using Drone-Collected Multispectral Imagery

2019 ◽  
Vol 11 (5) ◽  
pp. 545 ◽  
Author(s):  
Dimitris Stavrakoudis ◽  
Dimitrios Katsantonis ◽  
Kalliopi Kadoglidou ◽  
Argyris Kalaitzidis ◽  
Ioannis Gitas
Keyword(s):  
Near Infrared ◽  
Agronomic Traits ◽  
Oryza Sativa L ◽  
N Uptake ◽  
Vegetation Indices ◽  
Growth Stages ◽  
Linear Regression Models ◽  
Remotely Sensed Data ◽  
Cost Efficient ◽  
N Management

The knowledge of rice nitrogen (N) requirements and uptake capacity are fundamental for the development of improved N management. This paper presents empirical models for predicting agronomic traits that are relevant to yield and N requirements of rice (Oryza sativa L.) through remotely sensed data. Multiple linear regression models were constructed at key growth stages (at tillering and at booting), using as input reflectance values and vegetation indices obtained from a compact multispectral sensor (green, red, red-edge, and near-infrared channels) onboard an unmanned aerial vehicle (UAV). The models were constructed using field data and images from two consecutive years in a number of experimental rice plots in Greece (Thessaloniki Regional Unit), by applying four different N treatments (C0: 0 N kg∙ha−1, C1: 80 N kg∙ha−1, C2: 160 N kg∙ha−1, and C4: 320 N kg∙ha−1). Models for estimating the current crop status (e.g., N uptake at the time of image acquisition) and predicting the future one (e.g., N uptake of grains at maturity) were developed and evaluated. At the tillering stage, high accuracies (R2 ≥ 0.8) were achieved for N uptake and biomass. At the booting stage, similarly high accuracies were achieved for yield, N concentration, N uptake, biomass, and plant height, using inputs from either two or three images. The results of the present study can be useful for providing N recommendations for the two top-dressing fertilizations in rice cultivation, through a cost-efficient workflow.

scholarly journals CCD CBERS and ASTER data in dasometric characterization of Pinus radiata D. Don (North-western Spain)

CERNE ◽  
2013 ◽  
Vol 19 (1) ◽  
pp. 103-110 ◽  
Author(s):  
Eva Sevillano-Marco ◽  
Alfonso Fernández-Manso ◽  
Carmen Quintano ◽  
Marcela Poulain
Keyword(s):  
Remote Sensing ◽  
Pinus Radiata ◽  
Near Infrared ◽  
Thermal Emission ◽  
Correlation Coefficients ◽  
Basal Area ◽  
Remote Sensing Data ◽  
Cost Effective ◽  
Infrared Band ◽  
Western Spain

A Chinese-Brazilian Earth Resources Satellite (CBERS) and an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scenes coupled with ancillary georeferenced data and field survey were employed to examine the potential of the remote sensing data in stand basal area, volume and aboveground biomass assessment over large areas of Pinus radiata D. Don plantations in Northwestern Spain. Statistical analysis proved that the near infrared band and the shade fraction image showed significant correlation coefficients with all stand variables considered. Predictive models were accordingly selected and utilized to undertake the spatial distribution of stand variables in radiata stands delimited by the National Forestry Map. The study reinforces the potentiality of remote sensing techniques in a cost-effective assessment of forest systems.

scholarly journals Monitoring 10-m LST from the Combination MODIS/Sentinel-2, Validation in a High Contrast Semi-Arid Agroecosystem

2020 ◽  
Vol 12 (9) ◽  
pp. 1453
Author(s):  
Juan M. Sánchez ◽  
Joan M. Galve ◽  
José González-Piqueras ◽  
Ramón López-Urrea ◽  
Raquel Niclòs ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Land Surface ◽  
Near Infrared ◽  
Estimation Error ◽  
Vegetation Indices ◽  
Model Performance ◽  
Test Site ◽  
Spatio Temporal ◽  
Average Estimation Error ◽  
Sentinel 2

Downscaling techniques offer a solution to the lack of high-resolution satellite Thermal InfraRed (TIR) data and can bridge the gap until operational TIR missions accomplishing spatio-temporal requirements are available. These techniques are generally based on the Visible Near InfraRed (VNIR)-TIR variable relations at a coarse spatial resolution, and the assumption that the relationship between spectral bands is independent of the spatial resolution. In this work, we adopted a previous downscaling method and introduced some adjustments to the original formulation to improve the model performance. Maps of Land Surface Temperature (LST) with 10-m spatial resolution were obtained as output from the combination of MODIS/Sentinel-2 images. An experiment was conducted in an agricultural area located in the Barrax test site, Spain (39°03′35″ N, 2°06′ W), for the summer of 2018. Ground measurements of LST transects collocated with the MODIS overpasses were used for a robust local validation of the downscaling approach. Data from 6 different dates were available, covering a variety of croplands and surface conditions, with LST values ranging 300–325 K. Differences within ±4.0 K were observed between measured and modeled temperatures, with an average estimation error of ±2.2 K and a systematic deviation of 0.2 K for the full ground dataset. A further cross-validation of the disaggregated 10-m LST products was conducted using an additional set of Landsat-7/ETM+ images. A similar uncertainty of ±2.0 K was obtained as an average. These results are encouraging for the adaptation of this methodology to the tandem Sentinel-3/Sentinel-2, and are promising since the 10-m pixel size, together with the 3–5 days revisit frequency of Sentinel-2 satellites can fulfill the LST input requirements of the surface energy balance methods for a variety of hydrological, climatological or agricultural applications. However, certain limitations to capture the variability of extreme LST, or in recently sprinkler irrigated fields, claim the necessity to explore the implementation of soil moisture or vegetation indices sensitive to soil water content as inputs in the downscaling approach. The ground LST dataset introduced in this paper will be of great value for further refinements and assessments.

scholarly journals Assessing the near surface sensitivity of SCIAMACHY atmospheric CO<sub>2</sub> retrieved using (FSI) WFM-DOAS

2007 ◽  
Vol 7 (13) ◽  
pp. 3597-3619 ◽  
Author(s):  
M. P. Barkley ◽  
P. S. Monks ◽  
A. J. Hewitt ◽  
T. Machida ◽  
A. Desai ◽  
...  
Keyword(s):  
Near Infrared ◽  
Vegetation Indices ◽  
Correlation Coefficients ◽  
The United States ◽  
Atmospheric Co2 ◽  
Carbon Surface ◽  
Near Surface ◽  
Surface Sensitivity ◽  
Vegetation Activity

Abstract. Satellite observations of atmospheric CO2 offer the potential to identify regional carbon surface sources and sinks and to investigate carbon cycle processes. The extent to which satellite measurements are useful however, depends on the near surface sensitivity of the chosen sensor. In this paper, the capability of the SCIAMACHY instrument on board ENVISAT, to observe lower tropospheric and surface CO2 variability is examined. To achieve this, atmospheric CO2 retrieved from SCIAMACHY near infrared (NIR) spectral measurements, using the Full Spectral Initiation (FSI) WFM-DOAS algorithm, is compared to in-situ aircraft observations over Siberia and additionally to tower and surface CO2 data over Mongolia, Europe and North America. Preliminary validation of daily averaged SCIAMACHY/FSI CO2 against ground based Fourier Transform Spectrometer (FTS) column measurements made at Park Falls, reveal a negative bias of about −2.0% for collocated measurements within ±1.0° of the site. However, at this spatial threshold SCIAMACHY can only capture the variability of the FTS observations at monthly timescales. To observe day to day variability of the FTS observations, the collocation limits must be increased. Furthermore, comparisons to in-situ CO2 observations demonstrate that SCIAMACHY is capable of observing a seasonal signal that is representative of lower tropospheric variability on (at least) monthly timescales. Out of seventeen time series comparisons, eleven have correlation coefficients of 0.7 or more, and have similar seasonal cycle amplitudes. Additional evidence of the near surface sensitivity of SCIAMACHY, is provided through the significant correlation of FSI derived CO2 with MODIS vegetation indices at over twenty selected locations in the United States. The SCIAMACHY/MODIS comparison reveals that at many of the sites, the amount of CO2 variability is coincident with the amount of vegetation activity. The presented analysis suggests that SCIAMACHY has the potential to detect CO2 variability within the lowermost troposphere arising from the activity of the terrestrial biosphere.

scholarly journals Assessing the near surface sensitivity of SCIAMACHY atmospheric CO<sub>2</sub> retrieved using (FSI) WFM-DOAS

2007 ◽  
Vol 7 (1) ◽  
pp. 2477-2530 ◽  
Author(s):  
M. P. Barkley ◽  
P. S. Monks ◽  
A. J. Hewitt ◽  
T. Machida ◽  
A. Desai ◽  
...  
Keyword(s):  
Near Infrared ◽  
Vegetation Indices ◽  
Correlation Coefficients ◽  
The United States ◽  
Atmospheric Co2 ◽  
Carbon Surface ◽  
Near Surface ◽  
Surface Sensitivity ◽  
Vegetation Activity

Abstract. Satellite observations of atmospheric CO2 offer the potential to identify regional carbon surface sources and sinks and to investigate carbon cycle processes. The extent to which satellite measurements are useful however, depends on the near surface sensitivity of the chosen sensor. In this paper, the capability of the SCIAMACHY instrument on board ENVISAT, to observe lower tropospheric and surface CO2 variability is examined. To achieve this, atmospheric CO2 retrieved from SCIAMACHY near infrared (NIR) spectral measurements, using the Full Spectral Initiation (FSI) WFM-DOAS algorithm, is compared to in situ aircraft observations over Siberia and additionally to tower and surface CO2 data over Mongolia, Europe and North America. Preliminary validation of daily averaged SCIAMACHY/FSI CO2 against ground based Fourier Transform Spectrometer (FTS) column measurements made at Park Falls, reveal a negative bias of about −2.0% for collocated measurements within ±1.0\\degree of the site. However, at this spatial threshold SCIAMACHY can only capture the variability of the FTS observations at monthly timescales. To observe day to day variability of the FTS observations, the collocation limits must be increased. Furthermore, comparisons to in-situ CO2 observations demonstrate that SCIAMACHY is capable of observing lower tropospheric variability on (at least) monthly timescales. Out of seventeen time series comparisons, eleven have correlation coefficients of 0.7 or more, and have similar seasonal cycle amplitudes. Additional evidence of the near surface sensitivity of SCIAMACHY, is provided through the significant correlation of FSI derived CO2 with MODIS vegetation indices at over twenty selected locations in the United States. The SCIAMACHY/MODIS comparison reveals that at many of the sites, the amount of CO2 variability is coincident with the amount of vegetation activity. It is evident, from this analysis, that SCIAMACHY therefore has the potential to detect CO2 variability within the lowermost troposphere arising from the activity of the terrestrial biosphere.

scholarly journals Uso de imagens tomadas por aronaves remotamente pilotadas para detecção da cultura do milho infestadas por Spodoptera frugiperda

2020 ◽  
Vol 13 (1) ◽  
pp. 156
Author(s):  
Denner Borges Rezende ◽  
Carlos Alberto Matias de Abreu Junior ◽  
George Deroco Martins ◽  
Odair José Marques ◽  
Laura Cristina Moura Xavier
Keyword(s):  
Spodoptera Frugiperda ◽  
Regression Models ◽  
Near Infrared ◽  
Field Research ◽  
Attack Detection ◽  
Linear Regression Models ◽  
Infrared Band ◽  
Maize Crop ◽  
Area Index ◽  
Remotely Piloted Aircraft

A Spodoptera frugiperda (Smith) (lagarta-do-cartucho) é a principal praga do milho, com a intensificação da agricultura, os cultivos sucessivos possibilitam maior infestação pela praga. Isso levou ao surgimento de populações resistentes a inseticidas e culturas transgênicas. Pesquisas de campo para iniciar tratamentos com inseticidas são demoradas e exaustivas. Pensando em agilidade e qualidade, neste trabalho foi utilizado uma aeronave remotamente pilotada (ARP) equipado com uma câmera RGB e uma câmera MAPPIR 3 para capturar imagens de uma lavoura de milho, com o objetivo de estimar o índice de área foliar (IAF) de um talhão infestado por S. frugiperda. Durante o ciclo da cultura do milho foram realizadas várias avaliações: determinação do índice de área foliar (IAF), severidade do ataque da praga e, voos para aquisição das imagens. Modelos radiométricos para estimativa do IAF foram obtidos a partir de modelos de regressão linear compostos pelas bandas que melhor correlacionaram com os parâmetros medidos. Os resultados obtidos demonstraram eficiência e maior precisão na estimativa do IAF para o modelo radiométrico composto pela a banda do infravermelho próximo na câmera MAPPIR 3. Nesta ocasião, o RMSE calculado foi de 885,0714 cm². Use of images for attack detection of Spodoptera frugiperda in corn under function of loss of foliar area A B S T R A C TSpodoptera frugiperda (Smith) (cartridge-caterpillar) is the main pest of maize, with the intensification of agriculture, the successive crops allow greater infestation by the pest. This has led to the emergence of populations resistant to insecticides and transgenic crops. Field research to begin treatments with insecticides is time consuming and exhaustive. Thus, thinking of agility and quality, in this work was used a remotely piloted aircraft (ARP) equipped with an RGB camera and a MAPPIR 3 camera to capture images of a maize crop with the objective of estimating the leaf area index (LAI) of a field infested by S. frugiperda. The radiometric models for the estimation of LAI were obtained from linear regression models composed by the bands that best correlated with the measured parameters. The obtained results demonstrated efficiency and greater accuracy in the estimation of the LAI for the radiometric model composed by the near infrared band (IVP) in the MAPPIR 3 camera. On this occasion, the RMSE calculated was 885.0714 cm².Keywords: Corn; Cartridge Caterpillar; Remotely Piloted Aircraft. 

scholarly journals Estimation of biometric, physiological, and nutritional variables in lettuce seedlings using multispectral images

2021 ◽  
Vol 25 (10) ◽  
pp. 689-695
Author(s):  
George D. Martins ◽  
Onésio F. da Silva Neto ◽  
Glecia J. dos S. Carmo ◽  
Renata Castoldi ◽  
Ludymilla C. S. Santos ◽  
...  
Keyword(s):  
Near Infrared ◽  
Vegetation Indices ◽  
Parametric Models ◽  
Multispectral Images ◽  
Infrared Band ◽  
Physiological Variable ◽  
Minas Gerais State ◽  
Increase Productivity ◽  
Estimation Models ◽  
Multispectral Camera

ABSTRACT The formation of seedlings is one of the most important phases of lettuce cultivation. Therefore, any strategy that aims to obtain high-quality seedlings can increase productivity. One of these strategies is the prediction of morphophysiological attributes based on optical properties. The objective of this study was to quantitatively estimate the biometric variables of lettuce from parametric and non-parametric models based on the response of multispectral camera images. The experiment was conducted in a greenhouse in the municipality of Uberaba, Minas Gerais State, Brazil. Twenty days after sowing, multispectral images of the plants were captured using a MAPIR Survey 3 camera. To compose the estimation models, along with the original bands of the camera, the multispectral vegetation indices were calculated using the calibrated original camera bands. Bands B550, B660, and B850 and the near-infrared indices contributed significantly to estimating the physiological variable models, with B850 contributing the most to the biometric and nutritional variables. From the near-infrared band (B850) and derived indices, it was possible to estimate all the agronomic variables from the models generated by the M5 algorithm, with an accuracy of up to 1.6% for the maximum quantum yield. Thus, it is possible to quantify the biometric, physiological, and nutritional variables of lettuce using a multispectral camera. Among the Mapir camera bands, B660 exhibited the greatest variability, showing that the red range was the most sensitive.

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