Tag Archives: vegetation indices

1185-1202 A.O. Markosyan, M.H. Zadayan, G. Azgaldyan, S.K. Baghdasaryan, S.Z. Kroyan and S.A. Markosyan
Assessment of the spatiotemporal changes of saline-alkaline soils using GIS and, geospatial technologies methods: a community case study
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Assessment of the spatiotemporal changes of saline-alkaline soils using GIS and, geospatial technologies methods: a community case study

A.O. Markosyan¹, M.H. Zadayan²*, G. Azgaldyan³, S.K. Baghdasaryan¹, S.Z. Kroyan⁴ and S.A. Markosyan⁵

¹Armenian National Agrarian University, Scientific Center of Soil Science, Agrochemistry and Melioration named after H. Petrosyan, Department of Soil Science, Agrochemistry and Geography of Soils, 24 Admiral Isakov Ave, AM0004 Yerevan, Armenia
²Center for Agricultural Research and Certification, State Non-Commercial Organization of the Ministry of Economy of the Republic of Armenia, Yerevanyan highway 2nd deadlock, building 4, Armavir Marz, v. Merdzavan, AM1139, Armenia
³Hydrometeorology and Monitoring Center of the Ministry of Environment of the Republic of Armenia, State Non-Commercial Organization, GIS and Remote Sensing Service, 46 Charenc, AM0025 Yerevan, Armenia
⁴National University of Architecture and Construction of Armenia, Department of Engineering Geodesy, Teryan St. 105, AM0009, Yerevan, Armenia
⁵Yerevan State University, Faculty of Biology, Department of Biology, 1 Alex Manoogian, AM0025 Yerevan, Armenia
*Correspondence: mhzadayan@gmail.com

Abstract:

Currently, in many countries, soil salinization is recognized as one of the primary land degradation processes, particularly in arid regions, where it significantly limits soil fertility and worsens ecological conditions.

The widespread occurrence of solonetzic soils, including soda-type saline-alkaline soils, along with the intensification of salinization under changing climatic conditions and anthropogenic pressure, highlights the urgent need to update data on their distribution and expansion trends.

This study, conducted between 2020 and 2022, presents the results of a survey of 600 hectares of saline soils in the Mrgashat settlement, Armavir Region, Republic of Armenia (center coordinates: 44° 5′ 14.36″ E, 40° 7′ 14.57″ N). A quantitative and qualitative assessment of the current state was carried out using GIS and remote sensing data, alongside soil sampling from six designated points.

Newly salinized areas over the past 10 years were mapped, and the dynamics and direction of salinization were analyzed. The validity of the findings was corroborated by field survey data and relevant statistical indicators.

The results indicate a clear trend of spatial and temporal expansion of salinized soils. Over the last two decades, the total salinized area has increased by 54 hectares, representing a 10.1% growth.

Key words:

Remote sensing, salinity index, salinity mapping, satellite imagery, soil salinity, spectral analysis, vegetation indices

151-163 A.N.H. Moreira, A.H. Ciappina, R.R. Andrade, D. Casaroli, G. Rossi, L. Conti and G. Bambi
Temporal analysis of pasture vegetation cover incentral-western Brazil using remote sensing
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Temporal analysis of pasture vegetation cover incentral-western Brazil using remote sensing

A.N.H. Moreira¹, A.H. Ciappina², R.R. Andrade¹*, D. Casaroli¹, G. Rossi³, L. Conti³ and G. Bambi³*

¹Federal University of Goiás, Department of Biosystems Engineering, College of Agronomy, BR74690-900 Goiânia, Goiás, Brazil
²Centro Universitário UniAraguaia, Department of Agronomic Engineering,
BR74223-060 Goiânia, Goiás, Brazil
³University of Florence, Department of Agriculture, Food, Environment and Forestry, Via San Bonaventura 13, IT50145 Firenze, Italy
*Correspondence: rafaella.andrade@ufg.br, gianluca.bambi@unifi.it

Abstract:

Brazil is the world’s leading exporter of beef, consolidating beef cattle farming as an important branch of national livestock farming. The expansion of livestock farming and agriculture in recent decades has resulted in a notable increase in pasture areas in Brazil. However, the country faces the growing challenge of pasture degradation, a problem that threatens sustainability and food production. On the other hand, livestock farming in Brazil’s Central-West region, the country’s largest cattle-producing area, particularly in the state of Goiás, can cause environmental damage when sustainable practices are disregarded. Thus, the objective of this article was to evaluate pasture degradation, at different levels, in the Ribeirão Serra Negra Watershed, in the municipality of Piracanjuba, Goiás, Brazil. Using images from the Sentinel-2A orbital sensor, the NDVI (Normalized Difference Vegetation Index) vegetation index and the vegetation cover classes of pastures were obtained between 2017 and 2021. During this period, the results showed that more than 98% of the areas had some level of degradation, with an average coverage of 6,586.1 ha. There was an upward evolution in the levels of vegetation cover between 2017 and 2019, with the best pasture conditions predominating in 2019. These assessments help identify areas that require greater attention and often necessitate conservation practices and management plans. In this context, monitoring degraded areas is a practice that facilitates the improvement of existing pastures, promotes the rational management of inputs, conserves natural resources, and aligns with development programs focused on sustainability.

Key words:

livestock grazing, pasture quality degradation, Remote sensing, sustainable, vegetation indices

2049-2059 Z. Jelínek, K. Starý, J. Kumhálová, J. Lukáš and J. Mašek
Winter wheat, winter rape and poppy crop growth evaluation with the help of remote and proximal sensing measurements
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Winter wheat, winter rape and poppy crop growth evaluation with the help of remote and proximal sensing measurements

Z. Jelínek¹*, K. Starý¹, J. Kumhálová¹, J. Lukáš² and J. Mašek³

¹Czech University of Life Sciences Prague, Faculty of Engineering, Department of Machinery Utilization, Kamýcká 129, CZ165 00 Prague, Czech Republic
²Crop Research Institute, v.v.i., Drnovská 507, CZ161 00 Prague, Czech Republic
³Czech University of Life Sciences, Faculty of Engineering, Department of Agricultural Machines, Kamýcká 129, CZ165 00 Prague, Czech Republic
*Correspondence: jelinekzdenek@tf.czu.cz

Abstract:

Monitoring of agricultural crops with the help of remote and proximal sensors during the growing season plays important role for site-specific management decisions. Winter wheat, winter rape and poppy are representatives of typical agricultural crops from the family Poacea, Brassicaceae and Papaveraceae, growing in relative dry area of Rakovník district in the Czech Republic. Ten Sentinel 2 satellite images acquired during vegetation season of the crops were downloaded and processed. Crops were monitored with the help of unmanned aerial vehicles (UAV) equipped with consumer grade Red Green Blue (RGB) camera and multispectral (MS) MicaSense RedEdge MX camera. In-field variability was assessed by computing RGB-based vegetation indices Triangular Greenness Index (TGI), Green Leaf Index (GLI) and Visible Atmospherically Resistant Index (VARI) and commonly used vegetation indices as Normalised Difference Vegetation Index (NDVI) and Green NDVI (GNDVI). The results derived from satellite and UAV images were supported with in-situ measurements of hand-held GreenSeeker and Chlorophyll Meter Content sensors. The study showed the usability of individual vegetation indices, especially the TGI index for chlorophyll content estimation, and VARI index for green vegetation fraction detection and leaf area index estimation, in comparison with selected hand-held devices. The results showed as well that leaf properties and canopy structure of typical characteristics of selected families can significantly influence the spectral response of the crops detected in different phenological stages.

Key words:

poppy, satellite images, unmanned aerial vehicles, vegetation indices, winter rape, winter wheat

2592-2601 K. Starý, Z. Jelínek, J. Kumhálová, J. Chyba and K. Balážová
Comparing RGB – based vegetation indices from UAV imageries to estimate hops canopy area
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Comparing RGB – based vegetation indices from UAV imageries to estimate hops canopy area

K. Starý¹*, Z. Jelínek¹, J. Kumhálová¹, J. Chyba² and K. Balážová²

¹Czech University of Life Sciences Prague, Faculty of Engineering, Department of Machinery Utilization, Kamýcká 129, CZ165 00 Prague, Czech Republic
²Czech University of Life Sciences, Faculty of Engineering, Department of Agricultural Machines, Kamýcká 129, CZ165 00 Prague, Czech Republic
*Correspondence: staryk@tf.czu.cz

Abstract:

Remote estimation of hops plants in hop gardens is imperative in field of precision agriculture, because of precise imaging of hop garden structure. Monitoring of hop plant volume and area can help to predict the condition and yield of hops. In this study, two unmanned aerial vehicles (UAV) – eBee X senseFly UAV equipped with Red Green Blue (RGB) S.O.D.A. camera and Vertical Take-Off Landing (VTOL) UAV FireFly6 Pro by BirdsEyeView Aerobotics equipped with MicaSense RedEdge MX camera were used to acquire images of hop garden at phenology stage maturity of cones (24 th July) before harvest. Seven commonly used RGB vegetation indices (VI) were derived from these RGB and multispectral (MS) images after photogrammetric pre-processing and orthophoto mosaic extraction using Pix4Dmapper software. Vegetation Indices as the Green Percentage Index (G%), Excess of Green Index (ExGreen), Green Leaf Index (GLI), Visible Atmospherically Resistant Index (VARI), Red Green Blue Vegetation Index (RGBVI), Normalised Green Red Difference Index (NGRDI) and Triangular Greenness Index (TGI) were derived from both data sets. Binary model from each of VI was derived and threshold value for green vegetation was set. The results showed significant differences in hop plant area based on the specifications of cameras, especially wavelengths centres, and design and flight parameters of both UAV types. The comparison of various indices showed, that ExG and TGI indices has the highest congruity of estimated vegetation indices in hop garden canopy area for both used cameras. Further processing by Fuzzy Overlay tool proved high accuracy in green canopy area estimation for ExG and TGI vegetation indices.

Key words:

canopy area, hop garden, unmanned aerial vehicles, vegetation indices