scholarly journals Evaluating land cover types from Landsat TM using SAGA GIS for vegetation mapping based on ISODATA and K-means clustering

2021 ◽  
Vol 26 (52) ◽  
pp. 159-165
Author(s):  
Polina Lemenkova
Keyword(s):  
Machine Learning ◽  
Land Cover ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Unsupervised Classification ◽  
Landsat Tm ◽  
Land Cover Types ◽  
Environmental Mapping ◽  
Landsat Tm Image ◽  
Tm Image

The paper presents the cartographic processing of the Landsat TM image by the two unsupervised classification methods of SAGA GIS: ISODATA and K-means clustering. The approaches were tested and compared for land cover type mapping. Vegetation areas were detected and separated from other land cover types in the study area of southwestern Iceland. The number of clusters was set to ten classes. The processing of the satellite image by SAGA GIS was achieved using Imagery Classification tools in the Geoprocessing menu of SAGA GIS. Unsupervised classification performed effectively in the unlabeled pixels for the land cover types using machine learning in GIS. Following an iterative approach of clustering, the pixels were grouped in each step of the algorithm and the clusters were reassigned as centroids. The paper contributes to the technical development of the application of machine learning in cartography by demonstrating the effectiveness of SAGA GIS in remote sensing data processing applied for vegetation and environmental mapping.

scholarly journals ISO CLUSTER CLASSIFIER BY ARCGIS FOR UNSUPERVISED CLASSIFICATION OF THE LANDSAT TM IMAGE OF REYKJAVÍK

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Polina Lemenkova
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Sustainable Development ◽  
Image Analysis ◽  
Data Processing ◽  
Unsupervised Classification ◽  
Landsat Tm ◽  
Environmental Mapping ◽  
Landsat Tm Image ◽  
Tm Image

The paper presents the use of the Landsat TM image processed by the ArcGIS Spatial Analyst Tool for environmental mapping of southwestern Iceland, region of Reykjavik.  Iceland is one of the most special Arctic regions with unique flora and landscapes. Its environment is presented by vulnerable ecosystems of highlands where vegetation is affected by climate, human or geologic factors: overgrazing, volcanism, annual temperature change. Therefore, mapping land cover types in Iceland contribute to the nature conservation, sustainable development and environmental monitoring purposes. This paper starts by review of the current trends in remote sensing, the importance of Landsat TM imagery for environmental mapping in general and Iceland in particular, and the requirements of GIS specifically for satellite image analysis. This is followed by the extended methodological workflow supported by illustrative print screens and technical description of data processing in ArcGIS. The data used in this research include Landsat TM image which was captured using GloVis and processed in ArcGIS. The methodology includes a workflow involving several technical steps of raster data processing in ArcGIS: 1) coordinate projecting, 2) panchromatic sharpening, 3) inspection of raster statistics, 4) spectral bands combination, 5) calculations, 6) unsupervised classification, 7) mapping. The classification was done by clustering technique using ISO Cluster algorithm and Maximum Likelihood Classification. This paper finally presents the results of the ISO Cluster application for Landsat TM image processing and concludes final remarks on the perspectives of environmental mapping based on Landsat TM image processing in ArcGIS.The results of the classification present landscapes divided into eight distinct land cover classes: 1) bare soils; 2) shrubs and smaller trees in the river valleys, urban areas including green spaces; 3) water areas; 4) forests including the Reykjanesfólkvangur National reserve; 5) ice-covered areas, glaciers and cloudy regions; 6) ravine valleys with a sparse type of the vegetation: rowan, alder, heathland, wetland; 7) rocks; 8) mixed areas. The final remarks include the discussion on the development of machine learning methods and opportunities of their technical applications in GIS-based analysis and Earth Observation data processing in ArcGIS, including image analysis and classification, mapping and visualization, machine learning and environmental applications for decision making in forestry and sustainable development.

scholarly journals Classificação de imagens multiespectrais LANDSAT TM e feições de textura: mapeamento da cobertura da terra

FLORESTA ◽  
1997 ◽  
Vol 27 (12) ◽  
Author(s):  
RUTH EMÍLIA NOGUEIRA LOCK ◽  
FLÁVIO FELIPE KIRCHNER
Keyword(s):  
Land Cover ◽  
Texture Analysis ◽  
Maximum Likelihood Method ◽  
Texture Features ◽  
Landsat Tm ◽  
Likelihood Method ◽  
Landsat Tm Image ◽  
Tm Image ◽  
Initial Results

Este trabalho mostra os resultados iniciais de uma pesquisa sobre classificação de imagens multiespectrais considerando feições de textura, aplicada ao mapeamento da cobertura da terra, com ênfase na separação das classes de cobertura vegetal. Para tanto foi efetuado um levantamento bibliográfico e estudo sobre o assunto, que está resumido na parte inicial. Na seqüência relata-se a parte prática, onde foi feita a classificação multiespectral da imagem LANDSAT-5 TM da Ilha de São Francisco do Sul-SC, utilizando o algoritmo de classificação Máxima verossimilhança. Para testar as potencialidades das feições de textura foram efetuadas quatro classificações distintas para obter as mesmas informações agrupadas em dez classes. Na primeira etapa foi efetuada somente a classificação multiespectral, nas outras foram consideradas feições de textura e classificação espectral. Classification of LANDSAT TM’s multiespectral images and texture features: land cover mapping Abstract This paper shows the initial results of a research regarding multiespectral image classification using texture analysis for land cover maping. A bibliographic review was conducted wich is disposed in the first part of this work. Following this, a classification of the LANDSAT TM image of São Francisco island, SC, was performed using the Maximum Likelihood Method. To test the texture analysis potentialities, four distinct classifications were performed to obtain the same informations grouped into ten classes. In the first one only a multiespectral classification was performed, and in the other three the texture analysis was considered.

Using k-nn and discriminant analyses to classify rain forest types in a Landsat TM image over northern Costa Rica

2008 ◽  
Vol 112 (5) ◽  
pp. 2485-2494 ◽  
Author(s):  
Sirpa Thessler ◽  
Steven Sesnie ◽  
Zayra S. Ramos Bendaña ◽  
Kalle Ruokolainen ◽  
Erkki Tomppo ◽  
...  
Keyword(s):  
Costa Rica ◽  
Rain Forest ◽  
Landsat Tm ◽  
Forest Types ◽  
Landsat Tm Image ◽  
Tm Image ◽  
Discriminant Analyses

Remote sensing and GIS forest landscape assessment for sustainable forest management

2021 ◽  
Vol 17 (1) ◽  
pp. 12-26
Author(s):  
A.F. Chukwuka ◽  
A. Alo ◽  
O.J. Aigbokhan
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Forest Cover ◽  
Sustainable Forest Management ◽  
Markov Chain Model ◽  
Remote Sensing Data ◽  
Chain Model ◽  
Spatial Metrics ◽  
Forest Reserve ◽  
Land Cover Types

This study set out to assess the dynamic characteristics of the Ikere forest reserve landscape between 1985 and 2017 using remote sensing data and spatial metrics. Landscape of the study area maintained complex patterns of spatial heterogeneity over the years. Forest cover loss to other land cover types results in new large non-forest area at increasing rate. As at the year 2017, the changes in land cover types were not yet at equilibrium, thus the need to determine the future forest cover extent using a three-way markov Chain model. The decrease in number of patches of forest land (NumP) with increase in its mean patch size (MPS) shows that the forest is becoming a single unit probably due to clearing of existing patches of forest trees. The decrease in class diversity and evenness (SDI and SEI) of the general landscape over the years strengthens this assertion. The findings of this study would be very helpful to government and other stakeholders responsible for ensuring sustainable forest and general environment. Keyword: Landscape, Spatial metrics, sustainable forest and Environment

scholarly journals MODEL FOR LAND COVER ESTIMATION USING UNSUPERVISED MACHINE LEARNING ON GOOGLE MAPS COLOR IMAGES

2018 ◽  
Vol XLII-5 ◽  
pp. 147-154
Author(s):  
R. S. Bhowmick ◽  
A. Kumar ◽  
G. D. Singh ◽  
S. Kumar
Keyword(s):  
Machine Learning ◽  
Land Cover ◽  
Remote Sensing Data ◽  
Pixel Classification ◽  
Unsupervised Machine Learning ◽  
Landscape Characteristics ◽  
Area Of Interest ◽  
Proposed Model ◽  
Agricultural Areas ◽  
Hyper Text Transfer Protocol

<p><strong>Abstract.</strong> Remote sensing data and satellite images are broadly used for land cover information. There are so many challenges to classify pixels on the basis of features and characteristics. Generally it is pixel classification that required the count of pixels for certain area of interest. In the proposed model, we are applying unsupervised machine learning to classify the content of the input images on the basis of pixels intensity. The study aims to compare classification accuracy of different landscape characteristics like water, forest, urban, agricultural areas, transport network and other classes adapted from CORINE (Coordination of information on the environment) nomenclature. To fulfil the aim of the model, accessing data from Google map using Google static API service which creates a map based on URL parameters sent through a standard HTTP (Hyper Text Transfer Protocol) request and returns the map as an image which can be display on any graphical user interface platform. The Google Static Maps API returns an image either in GIF, PNG or JPEG format in response to an HTTP request. To identify different land cover/use classes using k-means clustering. The model is dynamic in nature that describes the clustering as well formulate the area of the concerned class or clustered fields.</p>

Estimation of land surface temperature from atmospherically corrected LANDSAT TM image using 6S and NCEP global reanalysis product

2014 ◽  
Vol 72 (12) ◽  
pp. 5183-5196 ◽  
Author(s):  
Prashant K. Srivastava ◽  
Dawei Han ◽  
Miguel A. Rico-Ramirez ◽  
Michaela Bray ◽  
Tanvir Islam ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Landsat Tm ◽  
Reanalysis Product ◽  
Landsat Tm Image ◽  
Tm Image ◽  
Global Reanalysis
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