Use of remote sensing data obtained from UAVs to assess the biomass productivity of Silphium perfoliatum

Agromonitoring is one of the most important sources of obtaining up-to-date and timely information about the state of agricultural crops. It is possible to speed up and reduce the cost of its implementation process using remote sensing data (RSD) obtained with the help of unmanned aerial vehicles (UAVs). Possibility of using ultra-high-resolution remote sensing to determine productivity of Silphium perfoliatum biomass has been evaluated using Phantom-4ProV 2.0 UAV. The shooting was carried out in RGB mode, the shooting height was 50 m, the spatial resolution was 2.5 cm. Based on the results of the survey, a height map and orthomosaic were created, which were later used to assess productivity of plants. To obtain the plant height values, the difference between the vegetation cover heights obtained from the surface model raster and the minimum height determined within the raster has been calculated. The actual height of plants measured in the field was compared with the data obtained using the UAV, and after the biomass productivity calculated from the actual and predicted heights was determined. The determination coefficient for equation of paired linear regression between the actual and predicted values of productivity made 0.97, and the value of the average approximation error was 3.3 %. To verify the results obtained, 60 samples of biomass were taken in the field within the study area, with the length of the plants determined using a tape measure, and the sampling sites coordinated using GPS positioning. 13 vegetation indices have been determined using pixel-based calibrated orthomosaic and normalized RGB channels, four of which (ExG, VARI, WI, and EXGR) showed to be suitable for creating a predictive model of multiple linear regression, which allows estimating and predicting the productivity of Silphium perfoliatum biomass during stemming phase with an error not exceeding 2 %. The results of the study can be useful both in development of prediction methods and in the direct prediction of Silphium perfoliatum biomass and other forage crops productivity, in particular Helianthus annuus and Helianthus tuberosus.

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Modeling tropical montane forest biomass, productivity and canopy traits with multispectral remote sensing data

Remote Sensing of Environment ◽

10.1016/j.rse.2019.02.021 ◽

2019 ◽

Vol 225 ◽

pp. 77-92 ◽

Cited By ~ 7

Author(s):

Christine I.B. Wallis ◽

Jürgen Homeier ◽

Jaime Peña ◽

Roland Brandl ◽

Nina Farwig ◽

...

Keyword(s):

Remote Sensing ◽

Forest Biomass ◽

Remote Sensing Data ◽

Biomass Productivity ◽

Montane Forest ◽

Tropical Montane Forest ◽

Sensing Data ◽

Multispectral Remote Sensing

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A comparison of multi-resource remote sensing data for vegetation indices

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/17/1/012067 ◽

2014 ◽

Vol 17 ◽

pp. 012067 ◽

Cited By ~ 3

Author(s):

Liqin Cao ◽

Tingting Liu ◽

Lifei Wei

Keyword(s):

Remote Sensing ◽

Vegetation Indices ◽

Remote Sensing Data ◽

Sensing Data

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Evapotranspiration and soil moisture indexes derived from remote sensing data to identify and investigate the mechanisms of the spatio-temporal patterns of drought in the Ebro-Basin (NE Spain).

10.5194/egusphere-egu2020-5564 ◽

2020 ◽

Author(s):

Jaime Gaona ◽

Pere Quintana-Seguí ◽

Maria José Escorihuela

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Land Surface ◽

Land Surface Model ◽

Remote Sensing Data ◽

Temporal Patterns ◽

Surface Model ◽

Ebro Basin ◽

Sensing Data ◽

Spatio Temporal

The Mediterranean climate of the Iberian Peninsula defines high spatial and temporal variability of drought at multiple scales. These droughts impact human activities such as water management, agriculture or forestry, and may alter valuable natural ecosystems as well. An accurate understanding and monitoring of drought processes are crucial in this area. The HUMID project (CGL2017-85687-R) is studying how remote sensing data and models (Quintana-Segu&#237; et al., 2019; Barella-Ortiz and Quintana-Segu&#237;, 2019) can improve our current knowledge on Iberian droughts, in general, and in the Ebro basin, more specifically.The traditional ground-based monitoring of drought lacks the spatial resolution needed to identify the microclimatic mechanisms of drought at sub-basin scale, particularly when considering relevant variables for drought such as soil moisture and evapotranspiration. In situ data of these two variables is very scarce.The increasing availability of remote sensing products such as MODIS16 A2 ET and the high-resolution SMOS 1km facilitates the use of distributed observations for the analysis of drought patterns across scales. The data is used to generate standardized drought indexes: the soil moisture deficit index (SMDI) based on SMOS 1km data (2010-2019) and the evapotranspiration deficit index (ETDI) based on MODIS16 A2 ET 500m. The study aims to identify the spatio-temporal mechanisms of drought generation, propagation and mitigation within the Ebro River basin and sub-basins, located in NE Spain where dynamic Atlantic, Mediterranean and Continental climatic influences dynamically mix, causing a large heterogeneity in climates.Droughts in the 10-year period 2010-2019 of study exhibit spatio-temporal patterns at synoptic and mesoscale scales. Mesoscale spatio-temporal patterns prevail for the SMDI while the ETDI ones show primarily synoptic characteristics. The study compares the patterns of drought propagation identified with remote sensing data with the patterns estimated using the land surface model SURFEX-ISBA at 5km.&#160; The comparison provides further insights about the capabilities and limitations of both tools, while emphasizes the value of combining approaches to improve our understanding about the complexity of drought processes across scales.Additionally, the periods of quick change of drought indexes comprise valuable information about the response of evapotranspiration to water deficits as well as on the resilience of soil to evaporative stress. The lag analysis ranges from weeks to seasons. Results show lags between the ETDI and SMDI ranging from days to weeks depending on the precedent drought status and the season/month of drought&#8217;s generation or mitigation. The comparison of the lags observed on remote sensing data and land surface model data aims at evaluating the adequacy of the data sources and the indexes to represent the nonlinear interaction between soil moisture and evapotranspiration. This aspect is particularly relevant for developing drought monitoring aiming at managing the impact of drought in semi-arid environments and improving the adaptation to drought alterations under climate change.

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Modelling sub-grid wetland in the ORCHIDEE global land surface model: evaluation against river discharges and remotely sensed data

Geoscientific Model Development ◽

10.5194/gmd-5-941-2012 ◽

2012 ◽

Vol 5 (4) ◽

pp. 941-962 ◽

Cited By ~ 41

Author(s):

B. Ringeval ◽

B. Decharme ◽

S. L. Piao ◽

P. Ciais ◽

F. Papa ◽

...

Keyword(s):

Remote Sensing ◽

Land Surface ◽

River Flow ◽

Spatial Scales ◽

Remote Sensing Data ◽

Surface Model ◽

Global Evaluation ◽

Good Opportunity ◽

Sensing Data ◽

Basin Scale

Abstract. The quality of the global hydrological simulations performed by land surface models (LSMs) strongly depends on processes that occur at unresolved spatial scales. Approaches such as TOPMODEL have been developed, which allow soil moisture redistribution within each grid-cell, based upon sub-grid scale topography. Moreover, the coupling between TOPMODEL and a LSM appears as a potential way to simulate wetland extent dynamic and its sensitivity to climate, a recently identified research problem for biogeochemical modelling, including methane emissions. Global evaluation of the coupling between TOPMODEL and an LSM is difficult, and prior attempts have been indirect, based on the evaluation of the simulated river flow. This study presents a new way to evaluate this coupling, within the ORCHIDEE LSM, using remote sensing data of inundated areas. Because of differences in nature between the satellite derived information – inundation extent – and the variable diagnosed by TOPMODEL/ORCHIDEE – area at maximum soil water content, the evaluation focuses on the spatial distribution of these two quantities as well as on their temporal variation. Despite some difficulties in exactly matching observed localized inundated events, we obtain a rather good agreement in the distribution of these two quantities at a global scale. Floodplains are not accounted for in the model, and this is a major limitation. The difficulty of reproducing the year-to-year variability of the observed inundated area (for instance, the decreasing trend by the end of 90s) is also underlined. Classical indirect evaluation based on comparison between simulated and observed river flow is also performed and underlines difficulties to simulate river flow after coupling with TOPMODEL. The relationship between inundation and river flow at the basin scale in the model is analyzed, using both methods (evaluation against remote sensing data and river flow). Finally, we discuss the potential of the TOPMODEL/LSM coupling to simulate wetland areas. A major limitation of the coupling for this purpose is linked to its ability to simulate a global wetland coverage consistent with the commonly used datasets. However, it seems to be a good opportunity to account for the wetland areas sensitivity to the climate and thus to simulate its temporal variability.

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A STUDY OF USING FULLY CONVOLUTIONAL NETWORK FOR TREETOP DETECTION ON REMOTE SENSING DATA

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-1-163-2018 ◽

2018 ◽

Vol IV-1 ◽

pp. 163-169 ◽

Cited By ~ 1

Author(s):

C. Xiao ◽

R. Qin ◽

X. Huang ◽

J. Li

Keyword(s):

Remote Sensing ◽

Inventory Management ◽

Remote Sensing Data ◽

Training Data ◽

Surface Model ◽

Detection Accuracy ◽

Convolutional Network ◽

Individual Tree ◽

Fully Convolutional Network ◽

Sensing Data

Abstract. Individual tree detection and counting are critical for the forest inventory management. In almost all of these methods that based on remote sensing data, the treetop detection is the most important and essential part. However, due to the diversities of the tree attributes, such as crown size and branch distribution, it is hard to find a universal treetop detector and most of the current detectors need to be carefully designed based on the heuristic or prior knowledge. Hence, to find an efficient and versatile detector, we apply deep neural network to extract and learn the high-level semantic treetop features. In contrast to using manually labelled training data, we innovatively train the network with the pseudo ones that come from the result of the conventional non-supervised treetop detectors which may be not robust in different scenarios. In this study, we use multi-view high-resolution satellite imagery derived DSM (Digital Surface Model) and multispectral orthophoto as data and apply the top-hat by reconstruction (THR) operation to find treetops as the pseudo labels. The FCN (fully convolutional network) is adopted as a pixel-level classification network to segment the input image into treetops and non-treetops pixels. Our experiments show that the FCN based treetop detector is able to achieve a detection accuracy of 99.7% at the prairie area and 66.3% at the complicated town area which shows better performance than THR in the various scenarios. This study demonstrates that without manual labels, the FCN treetop detector can be trained by the pseudo labels that generated using the non-supervised detector and achieve better and robust results in different scenarios.

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