CLASSIFICATION METHODS FOR REMOTELY SENSED DATA: LAND USE AND LAND COVER CLASSIFICATION USING VARIOUS COMBINATIONS OF BANDS

2015 ◽  
Vol 74 (10) ◽  
Author(s):  
Nur Anis Mahmon ◽  
Norsuzila Ya’acob ◽  
Azita Laily Yusof ◽  
Jasmee Jaafar
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Images ◽  
Satellite Image ◽  
Remotely Sensed ◽  
Landsat 8 ◽  
Remotely Sensed Data ◽  
Ground Truth Data ◽  
Sensing Applications ◽  
Band Combinations

Land use and land cover (LU/LC) classification of remotely sensed data is an important field of research by which it is commonly used in remote sensing applications. In this study, the different types of classification techniques were compared using different RGB band combinations for classifying several satellite images of some parts of Selangor, Malaysia. For this objective, the classification was made using Landsat 8 satellite images and the Erdas Imagine software as the image processing package. From the classification output, the accuracy assessment and kappa statistic were evaluated to get the most accurate classifier. Optimal performance was identified by validating the classification results with ground truth data. From the results of the classified images, the Maximum Likelihood technique (overall accuracy 82.5%) was the highest and most applicable for satellite image classifications as compared with Mahalanobis Distance and Minimum Distance. Whereas for land use and land cover mapping, the RGB 4, 3, 2 band combinations were found to be more reliable. An accurate classification can produce a correct LU/LC map that can be used for various purposes.  

scholarly journals Forest Cover Changes and Land Use Dynamics in Oluwa Forest Reserve, Southwestern Nigeria

2014 ◽  
Vol 7 (2) ◽  
pp. 25-44 ◽  
Author(s):  
Oluwagbenga O. I. Orimoogunje
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Resource Use ◽  
Forest Cover ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Southwestern Nigeria ◽  
Forest Reserve ◽  
Ground Truth Data ◽  
Original Species

Abstract This study examined the extent of resource use and the level of degradation consequent upon land use. Three distinctive trends were observed in terms of forest and land cover dynamics. These are forest degradation, deforestation and regeneration. The paper integrated both, topographical map of 1969 and satellite imageries from Landsat MSS 1972, and Landsat TM 1991 and 2000 with ground truthing and socio-economic surveys to assess changes in forest resource use and land cover in South-western Nigeria. The satellite images were analysed using ILWIS software version 3.4. Based on ground truth data and remotely sensed data, the study area was classified into five categories using the supervised maximum likelihood classification technique. The accuracy assessment was carried out on the remotely sensed data. A total of 30 points for each dataset were selected for this operation and the overall accuracy of 90%, 86.7% and 85% respectively was obtained from the three image datasets. Results showed three dominant ecological communities in Oluwa Forest Reserve while two effects of changes on species were identified. The first was the replacement of what could be considered as the original species by other species tolerant to the ‘new’ ecosystem. The other was the reduction in the range of the original species that could be found. This was an indication that the area had been fragmented comparing to its original status. Results suggest that resource utilization and land cover change dynamically over time. The study also revealed that the creation of forest reserve to restrict local access and resource use would have been an effective tool for regulating encroachment and logging activities if there was an effective enforcement of regulation. It is therefore obvious that the main aim of environmental management should be the protection of the natural living space of humankind and integration of environmental scarcity in making decision on all economic issues and activities.

Making Of Classification Land Cover Through Result Of Visual Data Satellite Image Analysis Landsat 8 OLI : Case Study in Tapaktuan District, South Aceh District

2021 ◽  
Vol 6 (1) ◽  
pp. 59-65
Author(s):  
Safridatul Audah ◽  
Muharratul Mina Rizky ◽  
Lindawati
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Image Classification ◽  
Satellite Image ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Sensing Technology ◽  
Open Land ◽  
Cover Class ◽  
Satellite Image Analysis

Tapaktuan is the capital and administrative center of South Aceh Regency, which is a sub-district level city area known as Naga City. Tapaktuan is designated as a sub-district to be used for the expansion of the capital's land. Consideration of land suitability is needed so that the development of settlements in Tapaktuan District is directed. The purpose of this study is to determine the level of land use change from 2014 to 2018 by using remote sensing technology in the form of Landsat-8 OLI satellite data through image classification methods by determining the training area of the image which then automatically categorizes all pixels in the image into land cover class. The results obtained are the results of the two image classification tests stating the accuracy of the interpretation of more than 80% and the results of the classification of land cover divided into seven forms of land use, namely plantations, forests, settlements, open land, and clouds. From these classes, the area of land cover change in Tapaktuan is increasing in size from year to year.

scholarly journals Urban Bare Land Classification Using NDBaI Index Based on Combination of Sentinel 2 MSI and Landsat 8 Multiresolution Images

2020 ◽  
Vol 36 (2) ◽  
Author(s):  
Trinh Le Hung
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Image ◽  
Urban Land ◽  
Landsat 8 ◽  
Land Classification ◽  
Infrared Band ◽  
Urban Land Cover ◽  
Bare Land ◽  
Sentinel 2

The classification of urban land cover/land use is a difficult task due to the complexity in the structure of the urban surface. This paper presents the method of combining of Sentinel 2 MSI and Landsat 8 multi-resolution satellite image data for urban bare land classification based on NDBaI index. Two images of Sentinel 2 and Landsat 8 acquired closely together, were used to calculate the NDBaI index, in which sortware infrared band (band 11) of Sentinel 2 MSI image and thermal infrared band (band 10) of Landsat 8 image were used to improve the spatial resolution of NDBaI index. The results obtained from two experimental areas showed that, the total accuracy of classifying bare land from the NDBaI index which calculated by the proposed method increased by about 6% compared to the method using the NDBaI index, which is calculated using only Landsat 8 data. The results obtained in this study contribute to improving the efficiency of using free remote sensing data in urban land cover/land use classification.

Land Use and Land Cover Changes in Northern Kordofan State of Sudan: A Remotely Sensed Data Analysis

2014 ◽  
pp. 269-283 ◽  
Author(s):  
Mohamed S. Dafalla ◽  
Elfatih M. Abdel-Rahman ◽  
Khalid H. A. Siddig ◽  
Ibrahim S. Ibrahim ◽  
Elmar Csaplovics
Keyword(s):  
Land Use ◽  
Data Analysis ◽  
Land Cover ◽  
Remotely Sensed ◽  
Land Cover Changes ◽  
Remotely Sensed Data

Prediction Changes for Nonstationary Multi-Temporal Satellite Images using HMM

2017 ◽  
pp. 387-406
Author(s):  
Ali Ben Abbes ◽  
Imed Riadh Farah
Keyword(s):  
Land Use ◽  
Land Use Changes ◽  
Satellite Image ◽  
Remotely Sensed ◽  
High Temporal Resolution ◽  
Remotely Sensed Data ◽  
Vegetation Monitoring ◽  
Area Of Interest ◽  
Multi Temporal ◽  
The Difference

Due to the growing advances in their temporal, spatial, and spectral resolutions, remotely sensed data continues to provide tools for a wide variety of environmental applications. This chapter presents the benefits and difficulties of Multi-Temporal Satellite Image (MTSI) for land use. Predicting land use changes using remote sensing is an area of interest that has been attracting increasing attention. Land use analysis from high temporal resolution remotely sensed images is important to promote better decisions for sustainable management land cover. The purpose of this book chapter is to review the background of using Hidden Markov Model (HMM) in land use change prediction, to discuss the difference on modeling using stationary as well as non-stationary data and to provide examples of both case studies (e.g. vegetation monitoring, urban growth).

Measuring and monitoring land cover, land use, and vegetation characteristics

2010 ◽  
Author(s):  
Ned Horning ◽  
Julie A. Robinson ◽  
Eleanor J. Sterling ◽  
Woody Turner ◽  
Sacha Spector
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Deciduous Forest ◽  
Land Cover Classification ◽  
Remotely Sensed ◽  
Land Cover Mapping ◽  
Remotely Sensed Data ◽  
Soil P ◽  
Vegetation Characteristics ◽  
Classification Project

In terrestrial biomes, ecologists and conservation biologists commonly need to understand vegetation characteristics such as structure, primary productivity, and spatial distribution and extent. Fortunately, there are a number of airborne and satellite sensors capable of providing data from which you can derive this information. We will begin this chapter with a discussion on mapping land cover and land use. This is followed by text on monitoring changes in land cover and concludes with a section on vegetation characteristics and how we can measure these using remotely sensed data. We provide a detailed example to illustrate the process of creating a land cover map from remotely sensed data to make management decisions for a protected area. This section provides an overview of land cover classification using remotely sensed data. We will describe different options for conducting land cover classification, including types of imagery, methods and algorithms, and classification schemes. Land cover mapping is not as difficult as it may appear, but you will need to make several decisions, choices, and compromises regarding image selection and analysis methods. Although it is beyond the scope of this chapter to provide details for all situations, after reading it you will be able to better assess your own needs and requirements. You will also learn the steps to carry out a land cover classification project while gaining an appreciation for the image classification process. That said, if you lack experience with land cover mapping, it always wise to seek appropriate training and, if possible, collaborate with someone who has land cover mapping experience (Section 2.3). Although the terms “land cover” and “land use” are sometimes used interchangeably they are different in important ways. Simply put, land cover is what covers the surface of the Earth and land use describes how people use the land (or water). Examples of land cover classes are: water, snow, grassland, deciduous forest, or bare soil.

Historical spatiotemporal analysis of land-use/land-cover changes and carbon budget in a temperate peatland (Turkey) using remotely sensed data

2011 ◽  
Vol 31 (3) ◽  
pp. 1166-1172 ◽  
Author(s):  
Fatih Evrendilek ◽  
Suha Berberoglu ◽  
Nusret Karakaya ◽  
Ahmet Cilek ◽  
Guler Aslan ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Carbon Budget ◽  
Spatiotemporal Analysis ◽  
Remotely Sensed ◽  
Land Cover Changes ◽  
Land Use Land Cover ◽  
Remotely Sensed Data

scholarly journals Estimating Land Use and Land Cover Change in North Central Georgia: Can Remote Sensing Observations Augment Traditional Forest Inventory Data?

Forests ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 856 ◽  
Author(s):  
Gretchen G. Moisen ◽  
Kelly S. McConville ◽  
Todd A. Schroeder ◽  
Sean P. Healey ◽  
Mark V. Finco ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Forest Inventory ◽  
Remotely Sensed ◽  
Data Sets ◽  
Inventory Data ◽  
Remotely Sensed Data ◽  
North Central ◽  
Forest Inventory Data ◽  
Long Time

Throughout the last three decades, north central Georgia has experienced significant loss in forest land and tree cover. This study revealed the temporal patterns and thematic transitions associated with this loss by augmenting traditional forest inventory data with remotely sensed observations. In the US, there is a network of field plots measured consistently through time from the USDA Forest Service’s Forest Inventory and Analysis (FIA) Program, serial photo-based observations collected through image-based change estimation (ICE) methodology, and historical Landsat-based observations collected through TimeSync. The objective here was to evaluate how these three data sources could be used to best estimate land use and land cover (LULC) change. Using data collected in north central Georgia, we compared agreement between the three data sets, assessed the ability of each to yield adequately precise and temporally coherent estimates of land class status as well as detect net and transitional change, and we evaluated the effectiveness of using remotely sensed data in an auxiliary capacity to improve detection of statistically significant changes. With the exception of land cover from FIA plots, agreement between paired data sets for land use and cover was nearly 85%, and estimates of land class proportion were not significantly different for overlapping time intervals. Only the long time series of TimeSync data revealed significant change when conducting analyses over five-year intervals and aggregated land categories. Using ICE and TimeSync data through a two-phase estimator improved precision in estimates but did not achieve temporal coherence. We also show analytically that using auxiliary remotely sensed data for post-stratification for binary responses must be based on maps that are extremely accurate in order to see gains in precision. We conclude that, in order to report LULC trends in north central Georgia with adequate precision and temporal coherence, we need data collected on all the FIA plots each year over a long time series and broadly collapsed LULC classes.

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