scholarly journals BACKSCATTER ANALYSIS USING MULTI-TEMPORAL SENTINEL-1 SAR DATA FOR CROP GROWTH OF MAIZE IN KONYA BASIN, TURKEY

2018 ◽  
Vol XLII-3 ◽  
pp. 9-13 ◽  
Author(s):  
S. Abdikan ◽  
A. Sekertekin ◽  
M. Ustunern ◽  
F. Balik Sanli ◽  
R. Nasirzadehdizaji
Keyword(s):  
Land Use ◽  
Plant Height ◽  
Stem Elongation ◽  
Test Site ◽  
Central Anatolia ◽  
Crop Fields ◽  
Object Based ◽  
Multi Temporal ◽  
Sar Imagery ◽  
Konya Basin

Temporal monitoring of crop types is essential for the sustainable management of agricultural activities on both national and global levels. As a practical and efficient tool, remote sensing is widely used in such applications. In this study, Sentinel-1 Synthetic Aperture Radar (SAR) imagery was utilized to investigate the performance of the sensor backscatter image on crop monitoring. Multi-temporal C-band VV and VH polarized SAR images were acquired simultaneously by in-situ measurements which was conducted at Konya basin, central Anatolia Turkey. During the measurements, plant height of maize plant was collected and relationship between backscatter values and plant height was analysed. The maize growth development was described under Biologische Bundesanstalt, bundessortenamt und CHemische industrie (BBCH). Under BBCH stages, the test site was classified as leaf development, stem elongation, heading and flowering in general. The correlation coefficient values indicated high correlation for both polarimetry during the early stages of the plant, while late stages indicated lower values in both polarimetry. As a last step, multi-temporal coverage of crop fields was analysed to map seasonal land use. To this aim, object based image classification was applied following image segmentation. About 80 % accuracies of land use maps were created in this experiment. As preliminary results, it is concluded that Sentinel-1 data provides beneficial information about plant growth. Dual-polarized Sentinel-1 data has high potential for multi-temporal analyses for agriculture monitoring and reliable mapping.

scholarly journals A Meta-Methodology for Improving Land Cover and Land Use Classification with SAR Imagery

2020 ◽  
Vol 12 (6) ◽  
pp. 961 ◽  
Author(s):  
Marinalva Dias Soares ◽  
Luciano Vieira Dutra ◽  
Gilson Alexandre Ostwald Pedro da Costa ◽  
Raul Queiroz Feitosa ◽  
Rogério Galante Negri ◽  
...  
Keyword(s):  
Remote Sensing Data ◽  
Data Classification ◽  
Test Site ◽  
Radar Data ◽  
Input Image ◽  
Optical Data ◽  
Land Use Classification ◽  
Object Based ◽  
Sar Imagery ◽  
Small Change

Per-point classification is a traditional method for remote sensing data classification, and for radar data in particular. Compared with optical data, the discriminative power of radar data is quite limited, for most applications. A way of trying to overcome these difficulties is to use Region-Based Classification (RBC), also referred to as Geographical Object-Based Image Analysis (GEOBIA). RBC methods first aggregate pixels into homogeneous objects, or regions, using a segmentation procedure. Moreover, segmentation is known to be an ill-conditioned problem because it admits multiple solutions, and a small change in the input image, or segmentation parameters, may lead to significant changes in the image partitioning. In this context, this paper proposes and evaluates novel approaches for SAR data classification, which rely on specialized segmentations, and on the combination of partial maps produced by classification ensembles. Such approaches comprise a meta-methodology, in the sense that they are independent from segmentation and classification algorithms, and optimization procedures. Results are shown that improve the classification accuracy from Kappa = 0.4 (baseline method) to a Kappa = 0.77 with the presented method. Another test site presented an improvement from Kappa = 0.36 to a maximum of 0.66 also with radar data.

scholarly journals APPLICABILITY OF MULTI-SEASONAL X-BAND SAR IMAGERY FOR MULTIRESOLUTION SEGMENTATION: A CASE STUDY IN A RIPARIAN MIXED FOREST

2015 ◽  
Vol XL-1-W5 ◽  
pp. 123-128
Author(s):  
Z. Dabiri ◽  
D. Hölbling ◽  
S. Lang ◽  
A. Bartsch
Keyword(s):  
Scale Parameter ◽  
Spatial Scales ◽  
Mixed Forest ◽  
Scale Parameters ◽  
Image Objects ◽  
Object Based ◽  
Multi Temporal ◽  
Reference Map ◽  
Sar Imagery ◽  
Multiresolution Segmentation

The increasing availability of synthetic aperture radar (SAR) data from a range of different sensors necessitates efficient methods for semi-automated information extraction at multiple spatial scales for different fields of application. The focus of the presented study is two-fold: 1) to evaluate the applicability of multi-temporal TerraSAR-X imagery for multiresolution segmentation, and 2) to identify suitable Scale Parameters through different weighing of different homogeneity criteria, mainly colour variance. Multiresolution segmentation was used for segmentation of multi-temporal TerraSAR-X imagery, and the ESP (Estimation of Scale Parameter) tool was used to identify suitable Scale Parameters for image segmentation. The validation of the segmentation results was performed using very high resolution WorldView-2 imagery and a reference map, which was created by an ecological expert. The results of multiresolution segmentation revealed that in the context of object-based image analysis the TerraSAR-X images are applicable for generating optimal image objects. Furthermore, ESP tool can be used as an indicator for estimation of Scale Parameter for multiresolution segmentation of TerraSAR-X imagery. Additionally, for more reliable results, this study suggests that the homogeneity criterion of colour, in a variance based segmentation algorithm, needs to be set to high values. Setting the shape/colour criteria to 0.005/0.995 or 0.00/1 led to the best results and to the creation of adequate image objects.

scholarly journals REPEAT-PASS INTERFEROMETRIC AND BACKSCATTER ANALYSIS OF X-BAND PAZ SATELLITE – FIRST RESULTS

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 253-258
Author(s):  
S. Abdikan ◽  
C. Bayik ◽  
M. Ustuner ◽  
F. Balik Sanli
Keyword(s):  
High Resolution ◽  
Sand Dunes ◽  
Ground Surface ◽  
Central Anatolia ◽  
X Band ◽  
First Results ◽  
Multi Temporal ◽  
Sar Imagery ◽  
Single Polarization ◽  
New Generation

Abstract. In this paper we present the initial results of PAZ Synthetic Aperture Radar (SAR) imagery for the first time. In the study, the potential of repeat-pass high resolution PAZ images were investigated. To this aim, both linear backscatter and interferometric results were presented. We used multi-temporal X-band (3.1 cm wavelength) new generation single look complex (SLC) data from Spanish PAZ in single polarization data. PAZ is based on TerraSAR-X/TanDEM-X platform to establish a constellation with them to shorten the revisit time and increase data acquisition capacity. We applied two analysis on PAZ data to assess the performance of the satellite images. For the analysis a semi-arid and almost flat region of Central Anatolia was selected. The images are acquired in both ascending and descending orbits. Each pair has 33 days of temporal baselines. Firstly, backscatter analysis was conducted over the region for different land cover classes. Secondly interferometric analysis was applied to determine phase difference and coherence features. As the region has sand dunes, bareland and uncultivated agricultural fields the coherence analysis showed high values, while cultivated fields showed variations of coherence due to different growth of vegetation. Since the region is prone to sinkhole formation the high-resolution PAZ indicated its advantage as determining a sinkhole that has a circle shape. The displacement of ground surface is determined in line of sight direction.

scholarly journals COMBINATION OF SPECKLE DIVERGENCE AND NEIGHBORHOOD ANALYSIS TO CLASSIFY SETTLEMENT FROM TERASAR-X DATA

2012 ◽  
Vol 9 (1) ◽  
Author(s):  
Rokhis Komarudin ◽  
Agung Indrajit
Keyword(s):  
Specific Area ◽  
Temporal Analysis ◽  
Radar Data ◽  
Tropical Country ◽  
Neighborhood Analysis ◽  
Object Based ◽  
Radar Imagery ◽  
Multi Temporal ◽  
Sar Imagery ◽  
Spatial Indexes

Abstract.  The  objectives  of  this  research  were  to  develop  and  improve  methods  for determination  of  settlements  area  with  focus  on  synthetic  aperture  radar  (SAR)  data. Remote  sensing  settlement  classification  has  made  great  progress,  both  for  optical  and radar  data  as  well  for  their  fusion.  Yet,  in  radar  imagery,  settlement  classification  still contains  some  problems.  Several  studies  on  application  of  radar  imagery  have  been conducted  using  techniques  such  as  textural  analysis,  multi-temporal  analysis,  statistical model,  spatial  indexes,  and  object-based  classification.  Most  of  the  development  methods have several problems in the specific area especially in the tropical country. Several studies also  showed  that  settlement  classification  accuracies  were  just  below  60%.    This  was  not sufficient    enough  to  classify  settlement  areas  using  SAR  imagery.  Therefore,  in  this research, we proposed a new method i.e., the combination of the speckle divergence and the neighborhood  analysis.  The  proposed  method  was  applied  to  classify  settlement  area  in Cilacap  and  Padang  Districts  of  Indonesia.  The  results  showed  that  the  proposed  method produced a good accuracy i.e., 85.5% for Cilacap Districts and 78.1% for Padang Districts. 

scholarly journals Object-Based Land Use and Land Cover Change Detection in Multi Temporal Remote-Sensing Images

2021 ◽  
Author(s):  
Dan Li ◽  
Runjie Jin ◽  
Jiali Gu ◽  
Runqiu Huang ◽  
Jiaping Wu
Keyword(s):  
Decision Making ◽  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Eastern China ◽  
Management Decision ◽  
Lulc Changes ◽  
Object Based ◽  
Management Decision Making ◽  
Multi Temporal

The changing of land use and land cover (LULC) are both affected by climate and human activity and affect climate, biological diversity, and human well-being. Accurate and timely information about the LULC pattern and change is crucial for land management decision-making, ecosystem monitoring, and urban planning, especially in developing economies undergoing industrialization, urbanization, and globalization. Biodiversity degradation and urban expansion in eastern China are research hot-spots. However, the influence of LULC changes on the region remains largely unexplored. Here, an object-based and multi-temporal image analysis approach was developed to detect how LULC changes during 1985-2015 in the Tiaoxi watershed (Zhejiang province, eastern China) using Landsat TM and OLI data. The main objective of this study is to improve the accuracy of unsupervised change detection from object-based and multi-temporal images. To this end, a total of seven LULC maps are generated with multi-temporal images. A random stratified sample design was used for assessing change detection accuracy. The proposed method achieved an overall accuracy of 91.86%, 92.14%, 92.00%, and 93.86% for 2000, 2005, 2010, and 2015, respectively. Nevertheless, the proposed method, in conjunction with object-oriented and multi-temporal satellite images, offers a robust and flexible approach to LULC changes mapping that helps with emergency response and government management. Urbanization and agriculture efficiency are the main reasons for LULC changes in the region. We anticipate that this freely available data will improve the modeling for surface forcing, provide evidence of changes in LULC, and inform water-management decision-making.

scholarly journals From Land Cover Map to Land Use Map: A Combined Pixel-Based and Object-Based Approach Using Multi-Temporal Landsat Data, a Random Forest Classifier, and Decision Rules

2021 ◽  
Vol 13 (9) ◽  
pp. 1700
Author(s):  
Dang Hung Bui ◽  
László Mucsi
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Land Cover ◽  
Decision Rules ◽  
Random Forest Classifier ◽  
Landsat Images ◽  
Object Based ◽  
Multi Temporal ◽  
Land Cover Map ◽  
Land Cover Maps

It is essential to produce land cover maps and land use maps separately for different purposes. This study was conducted to generate such maps in Binh Duong province, Vietnam, using a novel combination of pixel-based and object-based classification techniques and geographic information system (GIS) analysis on multi-temporal Landsat images. Firstly, the connection between land cover and land use was identified; thereafter, the land cover map and land use function regions were extracted with a random forest classifier. Finally, a land use map was generated by combining the land cover map and the land use function regions in a set of decision rules. The results showed that land cover and land use were linked by spectral, spatial, and temporal characteristics, and this helped effectively convert the land cover map into a land use map. The final land cover map attained an overall accuracy (OA) = 93.86%, with producer’s accuracy (PA) and user’s accuracy (UA) of its classes ranging from 73.91% to 100%. Meanwhile, the final land use map achieved OA = 93.45%, and the UA and PA ranged from 84% to 100%. The study demonstrated that it is possible to create high-accuracy maps based entirely on free multi-temporal satellite imagery that promote the reproducibility and proactivity of the research as well as cost-efficiency and time savings.

MANAJEMEN DATA SPASIAL: PENGGUNAAN TANAH WILAYAH PEDESAAN DI SUMATERA BARAT

2019 ◽  
Vol 5 (2) ◽  
pp. 48-53
Author(s):  
Afrital Rezki, S.Pd., M.Si ◽  
Erna Juita ◽  
Dasrizal Dasrizal ◽  
Arie Zella Putra Ulni
Keyword(s):  
Land Use ◽  
Paddy Field ◽  
Secondary Forest ◽  
Water Bodies ◽  
Primary Forest ◽  
Land Uses ◽  
Forest Land ◽  
Land Use Patterns ◽  
Crop Fields ◽  
Mixed Plantations

Perkembangan penggunaan tanah bergerak horisontal secara spasial ke arah wilayah yang mudah diusahakan. Penggunaan tanah juga bergerak secara vertikal dalam rangka menaikkan mutunya. Penelitian ini bertujuan untuk menganalisis pola penggunaan lahan, bagaimana manajemen penggunaan lahan di satu wilayah berdasarkan batas Nagari. Metode yang digunakan adalah analsisis spasial dengan interpretasi citra penginderaan jauh, survey lapangan, dan analisis deskriptif. Pertumbuhan pemukiman Nagari Sungai Sariak Kecamatan VII Koto Kabupaten Padang Pariaman mengakibatkan pemanfaatan ruang menjadi tumpang tindih. Diperlukan cara-cara pengelolaan dan managemen penggunaan tanah dalam rangka pembangunan berkelanjutan yang menaikkan taraf hidup masyarakat dan tidak menimbulkan kerugian lingkungan.Terdapat 9 jenis penggunaan lahan yang ada di Nagari Sungai Sariak. Penggunaan lahan tersebut adalah Primary Forest, Secondary Forest, Paddy Field, Settlement, Mixed Plantations, Crop Fields, Water Bodies, Bushes, dan Plantations. Penggunaan lahan yang paling luas di Nagari Sungai Sariak adalah jenis penggunaan lahan Primary Forest, sebesar 48% dari total luas wilayah Nagari Sungai Sariak. Pada tahun 2011 sampai tahun 2016, penggunaan lahan paling luas terjadi pada penggunaan lahan jenis Primary Forest yang kemudian menjadi Mixed Plantations. Land use Changes moved horizontally spatially towards areas that are easily cultivated. The land use also moves vertically in order to increase its quality. This study aims to analyze land use patterns, how land use management in one area is based on Nagari boundaries. The method used is spatial analysis with interpretation of remote sensing images, field surveys, and descriptive analysis. The growth of Nagari Sungai Sariak in Kecamatan VII Koto, Kabupaten Padang Pariaman resulted in overlapping use of space. Management methods are needed and management of land use in the framework of sustainable development that raises the standard of living of the community and does not cause environmental losses. There are 9 types of land use in the Nagari Sungai Sariak. The land uses are Primary Forest, Secondary Forest, Paddy Field, Settlement, Mixed Plantations, Crop Fields, Water Bodies, Bushes, and Plantations. The most extensive land use in Nagari Sungai Sariak is the type of Primary Forest land use, amounting to 48% of the total area of the Nagari Sungai Sariak. From 2011 to 2016, the most extensive land use occurred in Primary Forest land uses which later became Mixed Plantations.

scholarly journals Cropland Abandonment in Slovakia: Analysis and Comparison of Different Data Sources

Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 334
Author(s):  
Juraj Lieskovský ◽  
Dana Lieskovská
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Spatial Data ◽  
Data Sources ◽  
Communist Regime ◽  
Corine Land Cover ◽  
Cropland Area ◽  
Cropland Abandonment ◽  
Multi Temporal

This study compares different nationwide multi-temporal spatial data sources and analyzes the cropland area, cropland abandonment rates and transformation of cropland to other land cover/land use categories in Slovakia. Four multi-temporal land cover/land use data sources were used: The Historic Land Dynamics Assessment (HILDA), the Carpathian Historical Land Use Dataset (CHLUD), CORINE Land Cover (CLC) data and Landsat images classification. We hypothesized that because of the different spatial, temporal and thematic resolution of the datasets, there would be differences in the resulting cropland abandonment rates. We validated the datasets, compared the differences, interpreted the results and combined the information from the different datasets to form an overall picture of long-term cropland abandonment in Slovakia. The cropland area increased until the Second World War, but then decreased after transition to the communist regime and sharply declined following the 1989 transition to an open market economy. A total of 49% of cropland area has been transformed to grassland, 34% to forest and 15% to urban areas. The Historical Carpathian dataset is the more reliable long-term dataset, and it records 19.65 km2/year average cropland abandonment for 1836–1937, 154.44 km2/year for 1938–1955 and 140.21 km2/year for 1956–2012. In comparison, the Landsat, as a recent data source, records 142.02 km2/year abandonment for 1985–2000 and 89.42 km2/year for 2000–2010. These rates, however, would be higher if the dataset contained urbanisation data and more precise information on afforestation. The CORINE Land Cover reflects changes larger than 5 ha, and therefore the reported cropland abandonment rates are lower.

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