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

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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Monitoring military landscapes and detection of underground man-made critical infrastructures in Cyprus using Earth Observation

Advances in Geosciences ◽

10.5194/adgeo-45-335-2018 ◽

2018 ◽

Vol 45 ◽

pp. 335-342 ◽

Cited By ~ 3

Author(s):

George Melillos ◽

Athos Agapiou ◽

Silas Michaelides ◽

Diofantos G. Hadjimitsis

Keyword(s):

Remote Sensing ◽

Vegetation Indices ◽

Remote Sensing Data ◽

Earth Observation ◽

Underground Structures ◽

Spectral Signatures ◽

Sensing Data ◽

Phenological Cycle ◽

In Situ Observations

Abstract. This paper aims to explore the importance of monitoring military landscapes in Cyprus using Earth Observation. The rising availability of remote sensing data provides adequate opportunities for monitoring military landscapes and detecting underground military man-made structures. In order to study possible differences in the spectral signatures of vegetation so as to be used for the systematic monitoring of military landscapes that comprise underground military structures, field spectroscopy has been used. The detection of underground and ground military structures based on remote sensing data could make a significant contribution to defence and security science. In this paper, underground military structures over vegetated areas were monitored, using both ground and satellite remote sensing data. Several ground measurements have been carried out in military areas, throughout the phenological cycle of plant growth, during 2016–2017. The research was carried out using SVC-HR1024 ground spectroradiometers. Field spectroradiometric measurements were collected and analysed in an effort to identify underground military structures using the spectral profile of the vegetated surface overlying the underground target and the surrounding area, comprising the in situ observations. Multispectral vegetation indices were calculated in order to study their variations over the corresponding vegetation areas, in presence or absence of military underground structures. The results show that Vegetation Indices such as NDVI, SR, OSAVI, DVI and MSR are useful for determining areas where military underground structures are present.

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Use of Earth Remote Sensing data of monitoring the level of soil fertility

BULLETIN of the L N Gumilyov Eurasian National University Chemistry Geography Ecology Series ◽

10.32523/2616-6771-2020-131-2-78-84 ◽

2020 ◽

Vol 131 (2) ◽

pp. 78-84

Author(s):

K.S. Baktybekov ◽

◽

G.R. Kabzhanova ◽

А.А. Aimbetov ◽

M.T. Alibayeva ◽

...

Keyword(s):

Remote Sensing ◽

Soil Cover ◽

Vegetation Indices ◽

Remote Sensing Data ◽

Sensing Data ◽

Material Resources ◽

Northern Kazakhstan ◽

Spectral Ranges ◽

Ground Monitoring ◽

The Relationship

Ground monitoring of soil massifs takes a lot of time, labor and material resources, although it is the most accurate and detailed. When introducing complex methods of monitoring the soil cover, the inclusion of space technologies is mandatory.Remote sensing data carry objective information over large areas, obtained in various spectral ranges. The article discusses the possibility of using remote sensing data for mapping and monitoring changes in the soil cover of Northern Kazakhstan. On the basis of thematic processing of remote sensing data of native satellites, a spatial analysis of the content of main nutrients in the sowing layer of soils was carried out, the relationship was revealed between fertility indicators and the value of vegetation indices for test ranges of the territory of Northern Kazakhstan.

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Remote sensing data and field survey activities for monitoring the evolution of forest systems after coppicing and soil erosion: A case study in Sardinia (Italy)

10.5194/egusphere-egu2020-21354 ◽

2020 ◽

Author(s):

Filippo Giadrossich ◽

Antonio Ganga ◽

Sergio Campus ◽

Ilenia Murgia ◽

Irene Piredda ◽

...

Keyword(s):

Remote Sensing ◽

Soil Erosion ◽

Vegetation Indices ◽

Monitoring Network ◽

Remote Sensing Data ◽

Forest Stands ◽

Sensing Data ◽

Long Term Monitoring ◽

Term Monitoring

The practice of coppicing is debated in the literature for the risk factors associated with soil erosion. Although erosion experiments provide useful data for estimating the susceptibility to soil erosion, there are many open questions that cannot be solved in isolated experiments, but which can be assessed by activating a long-term monitoring process. In this way, it is possible to correctly frame the spatial and temporal scale of soil erosion in coppice forests.&#160;The aim of the work is to evaluate the effectiveness of the use of remote sensing data in combination with field data, for monitoring the evolution of forest stands interested by coppicing in relation to soil erosion.&#160;We have installed a long-term monitoring network for erosion estimation, while Sentinel-2C satellite data were used for the period 2016-2018. Starting from this dataset, a selection of vegetation indices was calculated and compared to the morphological and topographical parameters of the study area, as well as the above-ground data collected during field activities. Using the Canonical Correspondences Analysis (CCA) the relationships between the matrix of vegetation indices, topographic and vegetational parameters and the respective performances of this protocol have been explored in order to describe the evolution of the forest stands in the study area associated to soil losses.

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Relationship between remote sensing data and field-observed interril erosion

Pesquisa Agropecuária Brasileira ◽

10.1590/s0100-204x2018000300008 ◽

2018 ◽

Vol 53 (3) ◽

pp. 332-341 ◽

Cited By ~ 1

Author(s):

André Geraldo de Lima Moraes ◽

Daniel Fonseca de Carvalho ◽

Mauro Antonio Homem Antunes ◽

Marcos Bacis Ceddia

Keyword(s):

Remote Sensing ◽

Prediction Models ◽

Vegetation Indices ◽

Remote Sensing Data ◽

Exponential Model ◽

Landsat 8 ◽

Sensing Data ◽

Linear Spectral Mixture Analysis ◽

Spectral Mixture ◽

The Relationship

Abstract: The objective of this work was to evaluate the relationship between different remote sensing data, derived from satellite images, and interrill soil losses obtained in the field by using a portable rainfall simulator. The study was carried out in an area of a hydrographic basin, located in Médio Paraíba do Sul, in the state of Rio de Janeiro - one of the regions most affected by water erosion in Brazil. Evaluations were performed for different vegetation indices (NDVI, Savi, EVI, and EVI2) and fraction images (FI), derived from linear spectral mixture analysis (LSMA), obtained from RapidEye, Sentinel2A, and Landsat 8 OLI images. Vegetation indices are more adequate to predict soil loss than FI, highlighting EVI2, whose exponential model showed R2 of 0.74. The best prediction models are generated from the RapidEye image, which shows the highest spatial resolution among the sensors evaluated.

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The Use of Deep Machine Learning for the Automated Selection of Remote Sensing Data for the Determination of Areas of Arable Land Degradation Processes Distribution

Remote Sensing ◽

10.3390/rs13010155 ◽

2021 ◽

Vol 13 (1) ◽

pp. 155

Author(s):

Dmitry I. Rukhovich ◽

Polina V. Koroleva ◽

Danila D. Rukhovich ◽

Natalia V. Kalinina

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Soil Degradation ◽

Arable Land ◽

Vegetation Indices ◽

Remote Sensing Data ◽

Agricultural Field ◽

Sensing Data ◽

Selection Of

Soil degradation processes are widespread on agricultural land. Ground-based methods for detecting degradation require a lot of labor and time. Remote methods based on the analysis of vegetation indices can significantly reduce the volume of ground surveys. Currently, machine learning methods are increasingly being used to analyze remote sensing data. In this paper, the task is set to apply deep machine learning methods and methods of vegetation indices calculation to automate the detection of areas of soil degradation development on arable land. In the course of the work, a method was developed for determining the location of degraded areas of soil cover on arable fields. The method is based on the use of multi-temporal remote sensing data. The selection of suitable remote sensing data scenes is based on deep machine learning. Deep machine learning was based on an analysis of 1028 scenes of Landsats 4, 5, 7 and 8 on 530 agricultural fields. Landsat data from 1984 to 2019 was analyzed. Dataset was created manually for each pair of “Landsat scene”/“agricultural field number”(for each agricultural field, the suitability of each Landsat scene was assessed). Areas of soil degradation were calculated based on the frequency of occurrence of low NDVI values over 35 years. Low NDVI values were calculated separately for each suitable fragment of the satellite image within the boundaries of each agricultural field. NDVI values of one-third of the field area and lower than the other two-thirds were considered low. During testing, the method gave 12.5% of type I errors (false positive) and 3.8% of type II errors (false negative). Independent verification of the method was carried out on six agricultural fields on an area of 713.3 hectares. Humus content and thickness of the humus horizon were determined in 42 ground-based points. In arable land degradation areas identified by the proposed method, the probability of detecting soil degradation by field methods was 87.5%. The probability of detecting soil degradation by ground-based methods outside the predicted regions was 3.8%. The results indicate that deep machine learning is feasible for remote sensing data selection based on a binary dataset. This eliminates the need for intermediate filtering systems in the selection of satellite imagery (determination of clouds, shadows from clouds, open soil surface, etc.). Direct selection of Landsat scenes suitable for calculations has been made. It allows automating the process of constructing soil degradation maps.

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