Sustainability of Agriculture Territories in South Kazakhstan: Remote Sensing and Geodata for Design of Landscape and Soil Maps

Sustainability of Agriculture Territories in South Kazakhstan: Remote Sensing and Geodata for Design of Landscape and Soil Maps

Aizhan Assylbekova (Al-Farabi Kazakh National University, Kazakhstan) and Natalya Tsychuyeva (Al-Farabi Kazakh National University, Kazakhstan)
DOI: 10.4018/978-1-7998-2551-7.ch005

Abstract

The increasing anthropogenic impact on the soil and vegetation cover, insufficient effective land management, and climatic changes, the degradation process of soils and agrolandscapes is accelerated, and as a result, lands have low productivity, and agrolandscapes have poor environmental sustainability. In this regard, on the basis of modern digital technologies of remote sensing and geoinformation systems (GIS), an initial study in Karasai district of Almaty region in Kazakhstan was conducted, which is aimed at the timely identification areas of degradation agrolandscapes for the adoption of preventive measures. Based on spatial analysis of remote sensing data and field data, a soil-geomorphological map and landscape map of the region was compiled on a scale of 1:100000, which covers several taxonomic units: classes, subclasses, and types of landscapes. The territory of the Karasai region is a complex biogeosystem, as the analyzed territory consists of 52 types of landscape. This data allows a modern analysis of the agrolandscapes of the region.
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Introduction

The objectives of rational nature management are one of the main aspects of sustainable development and environmental stability of Central Asian states and entire Eurasia. At the same time, agriculture remains one of the main sectors of the national economies of the countries of the continent. New environmental challenges of our time forced to use adaptive systems of organization and farming. One of these methods, based on the transition from zonal farming systems to landscape farming, is the adaptive-landscape farming method (Kiryushin, 1996). This approach takes into account both the needs of society in agricultural products and the agroecological requirements of crops.

The mentioned method of adaptive landscape farming systems requires a preliminary study of the territory, the creation of a landscape and soil maps of the area, and recommendations on the use of the territory. In this regard, for any territory on which this method of farming will be applied, a detailed landscape and soil maps is needed. The creation of such maps using digital technologies of remote sensing and GIS is demonstrated in this chapter.

Landscape maps are used to describe types of geographic pattern and their distribution and the geo-complexes and their spatial structure in an integrative way. A detailed landscape map in a scale of 1:10000 and larger includes landscape topological landscape units on the generalized large-scale and medium scale (scale 1:10000-1:1000000) maps landscape mezo units and areas on small-scale (smaller than 1:1000000) mainly landscapes, although in some cases can be shown (in a highly generalized form) landscape geotopes and landscape meso units (Knizhnikov, 2003).

For purposes of adaptive landscape farming systems landscape maps focused on large-and medium-scale. Such maps should be based on field survey and on the interpretation of high-resolution remote sensing data (Räsänen et al., 2019, Forkuor et al., 2017). Depending on the purpose of landscape maps the legends varying in detail. They from a brief reference to the main components of indicators of geographical complexes (relief, vegetation) to a detailed differentiation of indicators (e.g. by including elements of climate, moisture conditions, soil, etc.) (Isachenko, 1991).

A landscape science possesses the basic research by the development of specific recommendations and proposals for the implementation and the improvement of the production systems in the agricultural sector. Recently a significant decrease of the yields per ha have been observed here (Kenenbaev, 2009). If there are a high natural resource potential on a site for arable production with fertile land and a high content of humus in the soil the average yields of the main food crop wheat can be more than three times higher (Geldyeva, 1992) than the average yield in Kazakhstan. A comparable success in several countries of the world when using farming systems for the site differentiation with regard to the agro-ecological conditions and by the awareness of the obvious need for adaptation of agricultural technologies can not be observed in Central Asia at moment. Such site adaptive agriculture should also include a site specific intensification of the production, the economic structures and the markets for agricultural products.

Key Terms in this Chapter

Space Images: The collective name of data obtained using spacecraft (SC) in various ranges of the electromagnetic range, visualized by a specific algorithm.

Landscape: The visible features of an area of land, its landforms, and how they integrate with natural or man-made features. A landscape includes the physical elements of geophysical defined landforms such as (ice-capped) mountains, hills, water bodies such as rivers, lakes, ponds and the sea, living elements of land cover including indigenous vegetation, human elements including different forms of land use, buildings, and structures, and transitory elements such as lighting and weather conditions. Combining both their physical origins and the cultural overlay of human presence, often created over millennia, landscapes reflect a living synthesis of people and place that is vital to local and national identity.

Image Classification: The process of extracting classes from multichannel bitmap information. The resulting raster from image classification can be used to create thematic maps. Depending on the interaction between the analyst and the computer during classification, there are two types of classification: supervised and unsupervised.

Geographic Information System (GIS): A system designed to capture, store, manipulate, analyze, manage, and present spatial or geographic data.

Decryption, Interpretation: The process of studying the territories, water areas and the atmosphere based on the relationship between the properties of the decrypted objects and the nature of their reproduction in images from aerial and space images. The content and task of Decryption is to obtain a certain amount of quantitative and qualitative information on remote sensing data on the state, composition, structure, sizes, relationships and dynamics of processes, phenomena and objects using decryption features.

Normalized Difference Vegetation Index (NDVI): A standardized index allowing you to generate an image displaying greenness (relative biomass). This index takes advantage of the contrast of the characteristics of two bands from a multispectral raster dataset—the chlorophyll pigment absorptions in the red band and the high reflectivity of plant materials in the near-infrared (NIR) band.

Remote Sensing: The process of detecting and monitoring the physical characteristics of an area by measuring its reflected and emitted radiation at a distance (typically from satellite or aircraft). Special cameras collect remotely sensed images, which help researchers “sense” things about the Earth.

Map: A mathematically defined, reduced, generalized image of the surface of the Earth, another celestial body, or outer space, showing the objects located or projected onto them in the adopted system of conventional signs. The map is considered as a figurative and symbolic model with high information content, spatiotemporal similarity with respect to the original, metricity, special visibility and visualization, which makes it the most important means of knowledge in Earth sciences and socio-economic sciences.

Adaptive-Landscape System Of Agriculture (ALSA): A system of land use, which makes use of agri-environmental groups focused on the production and the economic and natural due to the quantity and quality in accordance with the public (market) needs, natural and industrial and natural resource, providing sustainable agrolandscape and soil fertility.

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