A study on landuse and landcover classification using  microwave data in Joida taluk of Uttara Kannada district,  Karnataka

The study has been conducted for land use and land cover classification by using SAR data. The study included examining of ALOS 2 PALSAR L- band quad pol (HH, HV, VH and VV) SAR data for LULC classification. The SAR data was pre-processed first which included multilook, radiometric calibration, geometric correction, speckle filtering, SAR Polarimetry and decomposition. For land use land cover classification of ALOS-2-PALSAR data sets, the supervised Random forest classifier was used. Training samples were selected with the help of ground truth data. The area was classified under 7 different classes such as dense forest, moderate dense forest, scrub/sparse forest, plantation, agriculture, water body, and settlements. Among them the highest area was covered by dense forest (108647ha) followed by horticulture plantation (57822 ha) and scrub/Sparse forest (49238 ha) and lowest area was covered by moderate dense forest (11589 ha). Accuracy assessment was performed after classification. The overall accuracy of SAR data was 80.36% and Kappa Coefficient was 0.76. Based on SAR backscatter reflectance such as single, double, and volumetric scattering mechanism different land use classes were identified.

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VEGETATION ANALYSIS AND LAND USE LAND COVER CLASSIFICATION OF FOREST IN UTTARA KANNADA DISTRICT INDIA THROUGH GEO-INFORMATICS APPROACH

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-1-w1-219-2017 ◽

2017 ◽

Vol XLII-1/W1 ◽

pp. 219-223 ◽

Cited By ~ 1

Author(s):

A. G. Koppad ◽

B. S. Janagoudar

Keyword(s):

Land Use ◽

Land Cover ◽

Anthropogenic Activities ◽

Ground Truth ◽

Vegetation Pattern ◽

Land Use Land Cover ◽

Forest Plantation ◽

Ground Truth Data ◽

Dense Forest ◽

Open Land

The study was conducted in Uttara Kannada districts during the year 2012&ndash;2014. The study area lies between 13.92&deg;&thinsp;N to 15.52&deg;&thinsp;N latitude and 74.08&deg;&thinsp;E to 75.09&deg;&thinsp;E longitude with an area of 10,215&thinsp;km<sup>2</sup>. The Indian satellite IRS P6 LISS-III imageries were used to classify the land use land cover classes with ground truth data collected with GPS through supervised classification in ERDAS software. The land use and land cover classes identified were dense forest, horticulture plantation, sparse forest, forest plantation, open land and agriculture land. The dense forest covered an area of 63.32&thinsp;% (6468.70&thinsp;sq&thinsp;km) followed by agriculture 12.88&thinsp;% (1315.31&thinsp;sq.&thinsp;km), sparse forest 10.59&thinsp;% (1081.37&thinsp;sq.&thinsp;km), open land 6.09&thinsp;% (622.37&thinsp;sq.&thinsp;km), horticulture plantation and least was forest plantation (1.07&thinsp;%). Settlement, stony land and water body together cover about 4.26 percent of the area. The study indicated that the aspect and altitude influenced the forest types and vegetation pattern. The NDVI map was prepared which indicated that healthy vegetation is represented by high NDVI values between 0.1 and 1. The non-vegetated features such as water bodies, settlement, and stony land indicated less than 0.1 values. The decrease in forest area in some places was due to anthropogenic activities. The thematic map of land use land cover classes was prepared using Arc GIS Software.

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VEGETATION ANALYSIS AND LAND USE LAND COVER CLASSIFICATION OF FOREST IN UTTARA KANNADA DISTRICT INDIA USING REMOTE SENSIGN AND GIS TECHNIQUES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w5-121-2017 ◽

2017 ◽

Vol XLII-4/W5 ◽

pp. 121-125 ◽

Cited By ~ 2

Author(s):

A. G. Koppad ◽

B. S. Janagoudar

Keyword(s):

Land Use ◽

Land Cover ◽

Anthropogenic Activities ◽

Ground Truth ◽

Vegetation Pattern ◽

Land Use Land Cover ◽

Forest Plantation ◽

Ground Truth Data ◽

Dense Forest ◽

Open Land

The study was conducted in Uttara Kannada districts during the year 2012&ndash;2014. The study area lies between 13.92&deg;&thinsp;N to 15.52&deg;&thinsp;N latitude and 74.08&deg;&thinsp;E to 75.09&deg;&thinsp;E longitude with an area of 10,215&thinsp;km<sup>2</sup>. The Indian satellite IRS P6 LISS-III imageries were used to classify the land use land cover classes with ground truth data collected with GPS through supervised classification in ERDAS software. The land use and land cover classes identified were dense forest, horticulture plantation, sparse forest, forest plantation, open land and agriculture land. The dense forest covered an area of 63.32&thinsp;% (6468.70&thinsp;sq&thinsp;km) followed by agriculture 12.88&thinsp;% (1315.31&thinsp;sq.&thinsp;km), sparse forest 10.59&thinsp;% (1081.37&thinsp;sq.&thinsp;km), open land 6.09&thinsp;% (622.37&thinsp;sq.&thinsp;km), horticulture plantation and least was forest plantation (1.07&thinsp;%). Settlement, stony land and water body together cover about 4.26 percent of the area. The study indicated that the aspect and altitude influenced the forest types and vegetation pattern. The NDVI map was prepared which indicated that healthy vegetation is represented by high NDVI values between 0.1 and 1. The non- vegetated features such as water bodies, settlement, and stony land indicated less than 0.1 values. The decrease in forest area in some places was due to anthropogenic activities. The thematic map of land use land cover classes was prepared using Arc GIS Software.

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Stratified even sampling method for accuracy assessment of land use/land cover classification: a case study of Beijing, China

International Journal of Remote Sensing ◽

10.1080/01431161.2020.1739349 ◽

2020 ◽

Vol 41 (16) ◽

pp. 6427-6443

Author(s):

Shiwei Dong ◽

Ziyue Chen ◽

Bingbo Gao ◽

Hui Guo ◽

Danfeng Sun ◽

...

Keyword(s):

Land Use ◽

Land Cover ◽

Sampling Method ◽

Accuracy Assessment ◽

Land Cover Classification ◽

Land Use Land Cover ◽

Beijing China

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Classification of various land features using RISAT-1 dual polarimetric data

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-8-833-2014 ◽

2014 ◽

Vol XL-8 ◽

pp. 833-837 ◽

Cited By ~ 10

Author(s):

V. N. Mishra ◽

P. Kumar ◽

D. K. Gupta ◽

R. Prasad

Keyword(s):

Land Use ◽

Land Cover ◽

Agricultural Land ◽

Uttar Pradesh ◽

Land Cover Classification ◽

Support Vector ◽

Land Use Land Cover ◽

Remotely Sensed Data ◽

Sar Data ◽

Land Cover Maps

Land use land cover classification is one of the widely used applications in the field of remote sensing. Accurate land use land cover maps derived from remotely sensed data is a requirement for analyzing many socio-ecological concerns. The present study investigates the capabilities of dual polarimetric C-band SAR data for land use land cover classification. The MRS mode level 1 product of RISAT-1 with dual polarization (HH & HV) covering a part of Varanasi district, Uttar Pradesh, India is analyzed for classifying various land features. In order to increase the amount of information in dual-polarized SAR data, a band HH + HV is introduced to make use of the original two polarizations. Transformed Divergence (TD) procedure for class separability analysis is performed to evaluate the quality of the statistics prior to image classification. For most of the class pairs the TD values are greater than 1.9 which indicates that the classes have good separability. Non-parametric classifier Support Vector Machine (SVM) is used to classify RISAT-1 data with optimized polarization combination into five land use land cover classes like urban land, agricultural land, fallow land, vegetation and water bodies. The overall classification accuracy achieved by SVM is 95.23 % with Kappa coefficient 0.9350.

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A STUDY ON PRODUCING HIGHLY RELIABILE REFERENCE DATA SETS FOR GLOBAL LAND COVER VALIDATION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b8-1207-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 1207-1211

Author(s):

N. Soyama ◽

K. Muramatsu ◽

M. Daigo ◽

F. Ochiai ◽

N. Fujiwara

Keyword(s):

Land Cover ◽

Reference Data ◽

Ground Truth ◽

Plant Functional Type ◽

Data Sets ◽

Functional Type ◽

Reference Dataset ◽

Ground Truth Data ◽

Global Land Cover ◽

Global Land

Validating the accuracy of land cover products using a reliable reference dataset is an important task. A reliable reference dataset is produced with information derived from ground truth data. Recently, the amount of ground truth data derived from information collected by volunteers has been increasing globally. The acquisition of volunteer-based reference data demonstrates great potential. However information given by volunteers is limited useful vegetation information to produce a complete reference dataset based on the plant functional type (PFT) with five specialized forest classes. In this study, we examined the availability and applicability of FLUXNET information to produce reference data with higher levels of reliability. FLUXNET information was useful especially for forest classes for interpretation in comparison with the reference dataset using information given by volunteers.

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Adaptive Feature Spaces for Land Cover Classification with Limited Ground Truth Data

Multiple Classifier Systems - Lecture Notes in Computer Science ◽

10.1007/3-540-45428-4_19 ◽

2002 ◽

pp. 189-200 ◽

Cited By ~ 5

Author(s):

Joseph T. Morgan ◽

Alex Henneguelle ◽

Melba M. Crawford ◽

Joydeep Ghosh ◽

Amy Neuenschwander

Keyword(s):

Land Cover ◽

Ground Truth ◽

Land Cover Classification ◽

Ground Truth Data ◽

Feature Spaces

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POI Mining for Land Use Classification: A Case Study

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9090493 ◽

2020 ◽

Vol 9 (9) ◽

pp. 493 ◽

Cited By ~ 1

Author(s):

Renato Andrade ◽

Ana Alves ◽

Carlos Bento

Keyword(s):

Land Use ◽

Land Cover ◽

Urban Areas ◽

Smart Cities ◽

Ground Truth ◽

Land Use Classification ◽

Data Types ◽

Ground Truth Data ◽

Functional Roles ◽

Artificial Neural Network Ann

The modern planning and management of urban spaces is an essential topic for smart cities and depends on up-to-date and reliable information on land use and the functional roles of the places that integrate urban areas. In the last few years, driven by the increased availability of geo-referenced data from social media, embedded sensors, and remote sensing images, various techniques have become popular for land use analysis. In this paper, we first highlight and discuss the different data types and methods usually adopted in this context, as well as their purposes. Then, based on a systematic state-of-the-art study, we focused on exploring the potential of points of interest (POIs) for land use classification, as one of the most common categories of crowdsourced data. We developed an application to automatically collect POIs for the study area, creating a dataset that was used to generate a large number of features. We used a ranking technique to select, among them, the most suitable features for classifying land use. As ground truth data, we used CORINE Land Cover (CLC), which is a solid and reliable dataset available for the whole European territory. It was used an artificial neural network (ANN) in different scenarios and our results reveal values of more than 90% for the accuracy and F-score in one experiment performed. Our analysis suggests that POI data have promising potential to characterize geographic spaces. The work described here aims to provide an alternative to the current methodologies for land use and land cover (LULC) classification, which are usually time-consuming and depend on expensive data types.

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