scholarly journals ABANDONED AGRICULTURAL LAND IDENTIFICATION USING OBJECT-BASED APPROACH AND SENTINEL DATA IN THE DANUBIAN LOWLAND, SLOVAKIA

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1539-1545 ◽  
Author(s):  
T. Goga ◽  
D. Szatmári ◽  
J. Feranec ◽  
J. Papčo
Keyword(s):  
Agricultural Land ◽  
Farmland Abandonment ◽  
Land Reforms ◽  
Spatial And Temporal Scales ◽  
Object Based Image Analysis ◽  
The Earth ◽  
Object Based ◽  
Widespread Phenomenon ◽  
Sentinel 2

Abstract. Farmland abandonment is a widespread phenomenon in different parts of the Earth especially in the countries of Central and Eastern Europe where large areas of agricultural land were left uncultivated, state-support and markets for agriculture disappeared and land reforms resulted in massive land ownership transfers following the collapse of socialism. Remote sensing and geographic information system provide powerful tools for identification and analysis of abandoned agricultural land (AAL) at various spatial and temporal scales. Here we present an approach to AAL extraction from Sentinel-1 and Sentinel-2 images, provided in the frame of the European Copernicus program. This study aims to investigate and map the spatial distribution of AAL on the foothill of Little Carpathians and in the Danubian Lowland, Slovakia. The presented case study showed the possibility of the use of Sentinel images and the object-based image analysis in the process of AAL identification that may improve the transfer of scientific knowledge to the local agri-environmental monitoring and management.

scholarly journals The Mapping of Land Use Using Object-Based Image Analysis (OBIA) in Klaten Regency

2020 ◽  
Vol 202 ◽  
pp. 06036
Author(s):  
Nurhadi Bashit ◽  
Novia Sari Ristianti ◽  
Yudi Eko Windarto ◽  
Desyta Ulfiana
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Image Analysis ◽  
Satellite Imagery ◽  
Agricultural Land ◽  
Scale Parameter ◽  
Land Use Classification ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Sentinel 2

Klaten Regency is one of the regencies in Central Java Province that has an increasing population every year. This can cause an increase in built-up land for human activities. The built-up land needs to be monitored so that the construction is in accordance with the regional development plan so that it does not cause problems such as the occurrence of critical land. Therefore, it is necessary to monitor land use regularly. One method for monitoring land use is the remote sensing method. The remote sensing method is much more efficient in mapping land use because without having to survey the field. The remote sensing method utilizes satellite imagery data that can be processed for land use classification. This study uses the sentinel 2 satellite image data with the Object-Based Image Analysis (OBIA) algorithm to obtain land use classification. Sentinel 2 satellite imagery is a medium resolution image category with a spatial resolution of 10 meters. The land use classification can be used to see the distribution of built-up land in Klaten Regency without having to conduct a field survey. The results of the study obtained a segmentation scale parameter value of 60 and a merge scale parameter value of 85. The classification results obtained by 5 types of land use with OBIA. Agricultural land use dominates with an area of 50% of the total area.

scholarly journals EXPLORING THE FACTORS OF FARMLAND ABANDONMENT – A CASE STUDY OF THE CHOSEN POLISH METROPOLITAN AREAS

2017 ◽  
Vol XIX (4) ◽  
pp. 187-194 ◽  
Author(s):  
Wojciech Sroka ◽  
Aleksandra Płonka ◽  
Piotr Krzyk
Keyword(s):  
Population Density ◽  
Agricultural Land ◽  
Metropolitan Areas ◽  
Small Farms ◽  
Farmland Abandonment ◽  
Secondary Research ◽  
Academic Literature ◽  
Net Migration ◽  
Research Material

The main aim of this paper was to assess the factors of farmland abandonment in selected metropolitan areas in Poland. The research used secondary research material, including data from the Main Statistical Office (Polish GUS) and academic literature. Analyses were conducted by means of the method of regression trees, among other things. The research found out that nearly 16% of farmland in Polish metropolitan areas had been abandoned. The factor that most affected set-aside was the share of small farms with an area of less than 5 ha of agricultural land. In communes with the majority of small farms, almost 30% of agricultural land was set aside. Entrepreneurship indicator, population density and net migration were also significant in explaining the phenomenon discussed in the paper. High values of these measures correlated with more advanced processes of farmland abandonment.

scholarly journals A Framework for Geographic Object-Based Image Analysis (GEOBIA) based on geographic ontology

2015 ◽  
Vol XL-7/W4 ◽  
pp. 27-33 ◽  
Author(s):  
H. Y. Gu ◽  
H. T. Li ◽  
L. Yan ◽  
X. J. Lu
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Systematic Approach ◽  
Semantic Network ◽  
Image Objects ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Geographic Object ◽  
Objective Model

GEOBIA (Geographic Object-Based Image Analysis) is not only a hot topic of current remote sensing and geographical research. It is believed to be a paradigm in remote sensing and GIScience. The lack of a systematic approach designed to conceptualize and formalize the class definitions makes GEOBIA a highly subjective and difficult method to reproduce. This paper aims to put forward a framework for GEOBIA based on geographic ontology theory, which could implement "Geographic entities - Image objects - Geographic objects" true reappearance. It consists of three steps, first, geographical entities are described by geographic ontology, second, semantic network model is built based on OWL(ontology web language), at last, geographical objects are classified with decision rule or other classifiers. A case study of farmland ontology was conducted for describing the framework. The strength of this framework is that it provides interpretation strategies and global framework for GEOBIA with the property of objective, overall, universal, universality, etc., which avoids inconsistencies caused by different experts’ experience and provides an objective model for mage analysis.

scholarly journals Fast Urban Land Cover Mapping Exploiting Sentinel-1 and Sentinel-2 Data

2021 ◽  
Vol 14 (1) ◽  
pp. 36
Author(s):  
Naomi Petrushevsky ◽  
Marco Manzoni ◽  
Andrea Monti-Guarnieri
Keyword(s):  
Land Cover ◽  
State Of The Art ◽  
Rapid Change ◽  
Object Based Image Analysis ◽  
Urban Land Cover ◽  
Monitoring Tools ◽  
Object Based ◽  
Urban Extent ◽  
Manual Classification ◽  
Sentinel 2

The rapid change and expansion of human settlements raise the need for precise remote-sensing monitoring tools. While some Land Cover (LC) maps are publicly available, the knowledge of the up-to-date urban extent for a specific instance in time is often missing. The lack of a relevant urban mask, especially in developing countries, increases the burden on Earth Observation (EO) data users or requires them to rely on time-consuming manual classification. This paper explores fast and effective exploitation of Sentinel-1 (S1) and Sentinel-2 (S2) data for the generation of urban LC, which can be frequently updated. The method is based on an Object-Based Image Analysis (OBIA), where one Multi-Spectral (MS) image is used to define clusters of similar pixels through super-pixel segmentation. A short stack (<2 months) of Synthetic Aperture Radar (SAR) data is then employed to classify the clusters, exploiting the unique characteristics of the radio backscatter from human-made targets. The repeated illumination and acquisition geometry allows defining robust features based on amplitude, coherence, and polarimetry. Data from ascending and descending orbits are combined to overcome distortions and decrease sensitivity to the orientation of structures. Finally, an unsupervised Machine Learning (ML) model is used to separate the signature of urban targets in a mixed environment. The method was validated in two sites in Portugal, with diverse types of LC and complex topography. Comparative analysis was performed with two state-of-the-art high-resolution solutions, which require long sensing periods, indicating significant agreement between the methods (averaged accuracy of around 90%).

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