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

Identification of wet areas in forest using remote sensing data

J. Ivanovs* and A. Lupikis

Latvian State Forest Research Institute “Silava”, Rigas street 111, LV-2169 Salaspils, Latvia
*Correspondence: janis.ivanovs@silava.lv

Abstract:

Aim of this study is to evaluate different remote sensing indices to detect spatial distribution of wet soils using GIS based algorithms. Area of this study represents different soil types on various quaternary deposits as well as different forest types. We analyzed 25 sites with the area of 1 km² each in central and western part of Latvia. Data about soil characteristics like thickness of peat layer and presence of reductimorphic colors in soil was collected during field surveys in 228 random points within study sites. ANOVA test for comparing means of different soil wetness classes and binary logistic regression analysis for evaluating the accuracy of different remote sensing indices to model spatial distribution of wet areas are used for analysis. Main conclusion of this study is that for different quaternary deposits and soil texture classes different algorithms for soil wetness prediction should be used. Data layers for predicting soil wetness in this study are various modifications and resolutions of digital elevation model like depressions, slope and SAGA wetness index as well as Sentinel-2 multispectral satellite imagery. Accuracy of soil wetness classification of soils on moraine, fluvial and eolian sediments exceeds 94%, whereas on the clayey sediments it is close to 80%.

Key words: