scholarly journals The Use of Spot-Hrv Data for the Mapping of the Land Cover (Applied upon East-Mono, Central Togo)

Afrika Focus ◽  
1991 ◽  
Vol 7 (1) ◽  
pp. 15-48
Author(s):  
Beata Maria De Vliegher
Keyword(s):  
Land Cover ◽  
Image Interpretation ◽  
Remote Sensing Data ◽  
Image Data ◽  
Visual Image ◽  
False Colour Composite ◽  
Data Set ◽  
Image Characteristics ◽  
Land Cover Map ◽  
Colour Composite

The mapping of the land use in a tropical wet and dry area (East-Mono, Central Togo) is made using remote sensing data, recorded by the satellite SPOT. The negative, multispectral image data set has been transferred into positives by photographical means and afterwards enhanced using the diazo technique. The combination of the different diazo coloured images resulted in a false colour composite, being the basic document for the visual image interpretation. The image analysis, based upon differences in colour and texture, resulted in a photomorphic unit map. The use of a decision tree including the various image characteristics allowed the conversion of the photomorphic unit map into a land cover map. For this, six main land cover types could be differentiated resulting in 16 different classes of the final map.

scholarly journals The Use of Spot-HRV Data for the Mapping of the Land Cover (applied upon East-Mono, Central Togo)

Afrika Focus ◽  
1991 ◽  
Vol 7 (1) ◽  
Author(s):  
Beata Maria De Vliegher
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Image Interpretation ◽  
Remote Sensing Data ◽  
Image Data ◽  
Visual Image ◽  
Data Set ◽  
Land Cover Map ◽  
Colour Composite

The mapping of the land use in a tropical wet and dry area (East-Mono, Central Togo) is made using remote sensing data, recorded by the satellite SPOT. The negative, multispectral image data set has been transferred into positives by photographical means and afterwards enhanced using the diazo technique. The combination of the different diazo coloured images resulted in a false colour composite, being the basic document for the visual image interpretation. The image analysis, based upon differences in colour and texture, resulted in a photomorphic unit map. The use of a decision tree including the various image characteristics allowed the conversion of the photomorphic unit map into a land cover map. For this, six main land cover types could be differentiated resulting in 16 different classes of the final map. KEY WORDS :Remote sensing, SPOT, Multispectral view, Visual image interpre- tation, Mapping, Vegetation, Land use, Togo. 

scholarly journals Agricultural Expansion in Mato Grosso from 1986–2000: A Bayesian Time Series Approach to Tracking Past Land Cover Change

2020 ◽  
Vol 12 (4) ◽  
pp. 688 ◽  
Author(s):  
Jacky Lee ◽  
Jeffrey A. Cardille ◽  
Michael T. Coe
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Spatial Resolution ◽  
Processing Speed ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Wide Audience ◽  
Data Set ◽  
Mato Grosso

Landsat 5 has produced imagery for decades that can now be viewed and manipulated in Google Earth Engine, but a general, automated way of producing a coherent time series from these images—particularly over cloudy areas in the distant past—is elusive. Here, we create a land use and land cover (LULC) time series for part of tropical Mato Grosso, Brazil, using the Bayesian Updating of Land Cover: Unsupervised (BULC-U) technique. The algorithm built backward in time from the GlobCover 2009 data set, a multi-category global LULC data set at 300 m resolution for the year 2009, combining it with Landsat time series imagery to create a land cover time series for the period 1986–2000. Despite the substantial LULC differences between the 1990s and 2009 in this area, much of the landscape remained the same: we asked whether we could harness those similarities and differences to recreate an accurate version of the earlier LULC. The GlobCover basis and the Landsat-5 images shared neither a common spatial resolution nor time frame, But BULC-U successfully combined the labels from the coarser classification with the spatial detail of Landsat. The result was an accurate fine-scale time series that quantified the expansion of deforestation in the study area, which more than doubled in size during this time. Earth Engine directly enabled the fusion of these different data sets held in its catalog: its flexible treatment of spatial resolution, rapid prototyping, and overall processing speed permitted the development and testing of this study. Many would-be users of remote sensing data are currently limited by the need to have highly specialized knowledge to create classifications of older data. The approach shown here presents fewer obstacles to participation and allows a wide audience to create their own time series of past decades. By leveraging both the varied data catalog and the processing speed of Earth Engine, this research can contribute to the rapid advances underway in multi-temporal image classification techniques. Given Earth Engine’s power and deep catalog, this research further opens up remote sensing to a rapidly growing community of researchers and managers who need to understand the long-term dynamics of terrestrial systems.

How to improve 3-m resolution land cover mapping from imperfect 10-m resolution land cover mapping product?

2020 ◽  
Author(s):  
Runmin Dong ◽  
Haohuan Fu
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Histogram Equalization ◽  
Land Cover Mapping ◽  
Data Set ◽  
Chesapeake Bay Watershed ◽  
Land Cover Map ◽  
Previous State ◽  
Human Interpretation ◽  
Large Scale Land

<p>Land cover mapping has made drastic progress with the improvement of the resolution of remote sensing images in recent research. However, with various limitations of public land cover datasets, human efforts on interpreting and labelling images still account for a significant part of the total cost. For example, it took 10 months and $1.3 million to label about 160,000 square kilometers in the Chesapeake Bay watershed in the northeastern United States. Therefore, it is significant to consider the human interpreting cost of the large-scale land cover mapping.</p><p> </p><p>In this work, we explore a possible solution to achieve 3-m resolution land cover mapping without any human interpretation. This is made possible thanks to a 10-m resolution global land cover map developed for the year of 2017. We propose a complete workflow and a novel deep learning based network to transform the imperfect 10-m resolution land cover map to a preferable 3-m resolution land cover map, which is beneficial to reduce the research thresholds in this community and give similar studies as an example. As we use the imperfect training label, a well-designed and robust approach is strongly needed. We integrate a deep high-resolution network with instance normalization, adaptive histogram equalization, and a pruning process for large-scale land cover mapping.</p><p> </p><p>Our proposed approach achieves the overall accuracy (OA) of 86.83% on the test data set for China, improving the previous state-of-the-art accuracies of 10-m resolution land cover mapping product by 5.35% in OA. Moreover, we present detailed results obtained over three mega cities in China as example and demonstrate the effectiveness of our proposed approach for 3-m resolution large-scale land cover mapping.</p>

scholarly journals DINAMIKA PERUBAHAN GARIS PANTAI DI PESISIR DESA SURODADI KECAMATAN SAYUNG DENGAN MENGGUNAKAN CITRA SATELIT (Dynamics of Coastal Line Changes in the Surodadi Village of Sayung Sub District by Using Satellite Imagery)

2018 ◽  
Vol 6 (4) ◽  
pp. 433-441
Author(s):  
Aulia Huda Riyanti ◽  
Agung Suryanto ◽  
Churun Ain
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Sea Water ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Image Data ◽  
Rate Of Change ◽  
Research Station ◽  
Shoreline Changes ◽  
Coastline Change

Garis pantai Desa Surodadi mengalami perubahan dari tahun ke tahun. Perubahan yang serius ini perlu untuk dilakukan pemantauan terus menerus. Penelitian ini dilakukan untuk memperoleh informasi tentang perubahan garis pantai dan kaitannya dengan tutupan lahan di pesisir Desa Surodadi Kecamatan Sayung Kabupaten Demak pada tahun 2015 dan 2016. Penelitian ini dilaksanakan pada bulan Mei sampai dengan Juni 2017. Stasiun penelitian dibagi menjadi lima stasiun berdasarkan lokasi abrasi dan akresi yang telah terjadi. Dengan proses overlay kedua data citra satelit melalui sistem informasi geografis merupakan cara cepat untuk mengetahui perubahan garis pantai yang terjadi pada pesisir Desa Surodadi. Metode penelitian ini dengan menggunakan metode deskriptif studi kasus dengan menggunakan teknologi penginderaan jauh pada pengolahan data citra SPOT 6 tahun 2015 dan tahun 2016 yang diperoleh dari Pusat Teknologi dan Data Penginderaan Jauh LAPAN Jakarta serta dilakukan survei lapangan sehingga diperoleh laju perubahan garis pantai serta tutupan lahan yang terdapat pada lokasi penelitian. Garis pantai yang terjadi dari tahun 2015 sampai tahun 2016 lebih banyak mengalami proses abrasi jika dibandingkan proses akresi. Berdasarkan hasil penelitian dapat diketahui laju perubahan panjang garis pantai sebesar 103,58 m, perubahan garis pantai yang terjadi berupa abrasi sebesar 1,197 ha dan perubahan yang berupa akresi sebesar 0,490 ha. Keterkaitan antara perubahan garis pantai dengan tutupan lahan di Desa Surodadi adalah tutupan mangrove yang ada cukup luas dan relatif rapat sehingga dapat mencegah intrusi air laut yang dapat menyebabkan perubahan garis pantai. Surodadi village coastline changes from year to year. This serious change is necessary for ongoing monitoring. This research was conducted to obtain information about coastline change and its relation to land cover in coastal village of Surodadi Sub-District of Sayung Regency of Demak in 2015 until 2016. This research was conducted from May to June 2017. The research station is divided into five stations based on the location of abrasion and Accretion that has occurred. With the second overlay process satellite image data through geographic information system is a quick way to find out the shoreline changes that occur in the coastal village of Surodadi. This research method is done by using descriptive method of case study by using remote sensing technology on SPOT image data processing of 6 year 2015 and year 2016 which obtained from Center of Technology and Remote Sensing Data of LAPAN Jakarta and conducted field survey so that obtained rate of change of coastline happened also Land cover located at the research location. Coastlines that occur from 2015 to 2016 more experienced abrasion process when compared to the accretion process. Based on the research results can be seen the rate of change of coastline length of 103.58 m, shoreline changes that occur in the form of abrasion of 1.197 ha and changes in the form of accretion of 0.490 ha. The link between coastline change and land cover in Surodadi Village is that the mangrove cover is wide enough and relatively close so it can prevent the intrusion of sea water which can cause coastline changes.

Establishment of land cover map using object-oriented classification method for VNREDSat-1 data

2020 ◽  
Vol 61 (2) ◽  
pp. 134-144 ◽  
Author(s):  
Lan Thi Pham ◽  
Son Phi Nguyen ◽  
Nghia Viet Nguyen ◽  
Huong Van Dao ◽  
Long Duc Doan ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Spectral Characteristics ◽  
Object Oriented ◽  
Remote Sensing Data ◽  
Shape Index ◽  
Classification Method ◽  
Land Use Classification ◽  
Land Cover Map ◽  
Level 3

Land cover/land use classification using high resolution remote sensing data has the biggest challenge is how to distinguish object classes from different spectral values, structures, shapes, and spatial elements. This paper reveals the object-oriented classification method to establish the land cover map using VNREDSat-1 data, with a spatial resolution of 10 m. Land cover/land use system is classified according to CORINE with level 3 with 14 types of land cover/land use. Extraction of 14 types of land cover/land use using object-oriented classification method based on reflectance spectral characteristics, shape index, location of objects, brightness, NDVI plant index, and density objects. The overall accuracy of classification result is about 0.71%.

scholarly journals ASSESSING THE ACCURACY OF LAND USE LAND COVER (LULC) MAPS USING CLASS PROPORTIONS IN THE REFERENCE DATA

2020 ◽  
Vol V-3-2020 ◽  
pp. 669-674 ◽  
Author(s):  
C. C. Fonte ◽  
L. See ◽  
J. C. Laso-Bayas ◽  
M. Lesiv ◽  
S. Fritz
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Reference Data ◽  
Accuracy Assessment ◽  
Reference Database ◽  
Data Sets ◽  
Land Use Land Cover ◽  
Data Set ◽  
Mixed Pixels ◽  
Land Cover Map

Abstract. Traditionally the accuracy assessment of a hard raster-based land use land cover (LULC) map uses a reference data set that contains one LULC class per pixel, which is the class that has the largest area in each pixel. However, when mixed pixels exist in the reference data, this is a simplification of reality that has implications for both the accuracy assessment and subsequent applications of LULC maps, such as area estimation. This paper demonstrates how the use of class proportions in the reference data set can be used easily within regular accuracy assessment procedures and how the use of class proportions can affect the final accuracy assessment. Using the CORINE land cover map (CLC) and the more detailed Urban Atlas (UA), two accuracy assessments of the raster version of CLC were undertaken using UA as the reference and considering for each pixel: (i) the class proportions retained from the UA; and (ii) the class with the majority area. The results show that for the study area and the classes considered here, all accuracy indices decrease when the class proportions are considered in the reference database, achieving a maximum difference of 16% between the two approaches. This demonstrates that if the UA is considered as representing reality, then the true accuracy of CLC is lower than the value obtained when using the reference data set that assigns only one class to each pixel. Arguments for and against using class proportions in reference data sets are then provided and discussed.

scholarly journals The evaluation and application of an urban land cover map with image data fusion and laboratory measurements

2017 ◽  
Vol 66 (2) ◽  
pp. 145-156 ◽  
Author(s):  
László Mucsi ◽  
Csilla Mariann Liska ◽  
László Henits ◽  
Zalán Tobak ◽  
Bálint Csendes ◽  
...  
Keyword(s):  
Land Cover ◽  
Data Fusion ◽  
Image Data ◽  
Urban Land ◽  
Laboratory Measurements ◽  
Urban Land Cover ◽  
Land Cover Map

Land Cover Classification based on Deep Convolutional Neural Network with Feature-based Data Augmentation

2020 ◽  
Author(s):  
Bo Wang ◽  
Chengeng Huang ◽  
Yuhua Guo ◽  
Jiahui Tao
Keyword(s):  
Land Cover ◽  
Data Augmentation ◽  
Remote Sensing Data ◽  
Image Data ◽  
Texture Features ◽  
Land Cover Classification ◽  
Deep Convolutional Neural Networks ◽  
Image Information ◽  
Feature Based ◽  
The Rich

Radiation information is essential to land cover classification, but general deep convolutional neural networks (DCNNs) hardly use this to advantage. Additionally, the limited amount of available remote sensing data restricts the efficiency of DCNN models though this can be overcome by data augmentation. However, normal data augmentation methods, which only involve operations such as rotation and translation, have little effect on radiation information. These methods ignore the rich information contained in the image data. In this article, the authors propose a feasible feature-based data augmentation method, which extracts spectral features that can reflect radiation information as well as geometric and texture features that can reflect image information prior to augmentation. Through feature extraction, this method indirectly enhances radiation information and increases the utilization of image information. Classification accuracies show an improvement from 80.20% to 89.20%, which further verifies the effectiveness of this method.

scholarly journals COMPARISON OF PIXEL-BASED AND OBJECT-BASED CLASSIFICATION USING PARAMETERS AND NON-PARAMETERS APPROACH FOR THE PATTERN CONSISTENCY OF MULTI SCALE LANDCOVER

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 765-771 ◽  
Author(s):  
E. Juniati ◽  
E. N. Arrofiqoh
Keyword(s):  
Land Cover ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Topographic Map ◽  
Visual Interpretation ◽  
Object Based ◽  
Study Analyse ◽  
Land Cover Map ◽  
Map Scale ◽  
Neural Network Classifiers

Information extraction from remote sensing data especially land cover can be obtained by digital classification. In practical some people are more comfortable using visual interpretation to retrieve land cover information. However, it is highly influenced by subjectivity and knowledge of interpreter, also takes time in the process. Digital classification can be done in several ways, depend on the defined mapping approach and assumptions on data distribution. The study compared several classifiers method for some data type at the same location. The data used Landsat 8 satellite imagery, SPOT 6 and Orthophotos. In practical, the data used to produce land cover map in 1:50,000 map scale for Landsat, 1:25,000 map scale for SPOT and 1:5,000 map scale for Orthophotos, but using visual interpretation to retrieve information. Maximum likelihood Classifiers (MLC) which use pixel-based and parameters approach applied to such data, and also Artificial Neural Network classifiers which use pixel-based and non-parameters approach applied too. Moreover, this study applied object-based classifiers to the data. The classification system implemented is land cover classification on Indonesia topographic map. The classification applied to data source, which is expected to recognize the pattern and to assess consistency of the land cover map produced by each data. Furthermore, the study analyse benefits and limitations the use of methods.

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