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).

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Brief communication "Determination of urban growth in catchment areas in Cyprus using multi-temporal remotely sensed data: risk assessment study"

Natural Hazards and Earth System Science ◽

10.5194/nhess-10-2235-2010 ◽

2010 ◽

Vol 10 (11) ◽

pp. 2235-2240 ◽

Cited By ~ 15

Author(s):

D. G. Hadjimitsis

Keyword(s):

Risk Assessment ◽

Catchment Area ◽

Image Data ◽

Remotely Sensed ◽

Remotely Sensed Data ◽

Aerial Photos ◽

Area Of Interest ◽

Catchment Areas ◽

Multi Temporal

Abstract. The aim of this study is to quantify the actual urbanization activity near the catchment area in the urban area of interest located in the vicinity of the Agriokalamin River area of Kissonerga Village in Paphos District. Remotely sensed data such as aerial photos, Landsat-5/7 TM/ETM+ and Quickbird image data have been used to track the urbanization activity from 1963 to 2008. In-situ GPS measurements have been used to locate in-situ the boundaries of the catchment area. The results clearly illustrate that tremendous urban development has taken place ranging from 0.9 to 33% from 1963 to 2008, respectively. A flood risk assessment and hydraulic analysis were also performed.

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

Jurnal Teknologi ◽

10.11113/jt.v74.4838 ◽

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.

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assessment of the probable impacts of land use changes on water quality in shadeghan wetland using remotely sensed data

Journal of Spatial Analysis Environmental Hazarts ◽

10.29252/jsaeh.7.2.33 ◽

2020 ◽

Vol 7 (2) ◽

pp. 33-48

Author(s):

behzad raygani ◽

fargol goodarzi ◽

ahmad talebi ◽

mohammad talaeian araghi ◽

hadi hashemi ◽

...

Keyword(s):

Water Quality ◽

Land Use ◽

Land Use Changes ◽

Remotely Sensed ◽

Remotely Sensed Data

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Object-based approach to integrate remotely sensed data within a GIS context for land use changes detection at urban-rural fringe areas

10.1117/12.298161 ◽

1997 ◽

Author(s):

Ryan S. M. Wang ◽

Nicholas D. Efford ◽

Stuart A. Roberts

Keyword(s):

Land Use ◽

Land Use Changes ◽

Remotely Sensed ◽

Remotely Sensed Data ◽

Object Based ◽

Urban Rural ◽

Changes Detection

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Integrating Multi-Temporal Spectral and Structural Information from ALOS/AVNIR-2 Images to Map Heterogeneous Land Use/Cover: A Hybrid Approach

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.500.640 ◽

2012 ◽

Vol 500 ◽

pp. 640-645

Author(s):

Shu Yi Song ◽

Xi Jin ◽

Zhou Shi

Keyword(s):

Land Use ◽

Comprehensive Evaluation ◽

Structural Information ◽

Low Cost ◽

Hybrid Approach ◽

Remotely Sensed ◽

Kappa Statistics ◽

Remotely Sensed Data ◽

Supervised Classifiers ◽

Multi Temporal

Applicability of remotely sensed data in heterogeneous land use/cover mapping has been greatly be restricted, given the fragmented distribution and spectral confusion of land cover features. In addition, ALOS/AVNIR-2 data, which show great potential for land use/cover mapping considering the high-resolution but low-cost characteristics, have not received too much attention. A new hybrid methodology to address these issues is proposed. This approach combines traditional supervised classifiers and unsupervised classifiers and integrates multi-temporal spectral and structural information from ALOS/AVNIR-2 images. The multi-temporal spectral and structural information are then used as auxiliary data through a rule-based decision tree approach to generate a final product with enhanced land use classes and accuracy. A comprehensive evaluation of derived products of the northern part of Shangyu City in eastern coastal China is presented based on official land use/cover map (1:10000) as well as inter-classification consistency analyses. Overall accuracy of 86.5% and Kappa statistics of 0.84 have been achieved, which are significantly higher than those obtained from the Maximum Likelihood classifier and ISODATA classifier. The hybrid approach presented here is straightforward and flexible enough to be generalized so the approach can be applied to interpret similar fragmented land use/cover using various remotely sensed source data.

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Application of remote sensing and GIS technique to analyze the land-use change: the case of Phu Giao district, Binh Duong province

Journal of Vietnamese Environment ◽

10.13141/jve.vol8.no3.pp151-155 ◽

2017 ◽

Vol 8 (3) ◽

pp. 151-155

Author(s):

Trong Dieu Hien Le ◽

Gia Huan Pham ◽

Tien Dat Nguyen ◽

Xuan Truong Nguyen ◽

Van Tat Pham

Keyword(s):

Land Use ◽

Maximum Likelihood ◽

Water Body ◽

Land Use Changes ◽

Satellite Image ◽

Image Data ◽

Urban Land ◽

United States Geological Survey ◽

Perennial Plant ◽

Multi Temporal

Digital change detection is a helpful technique using multi-temporal satellite image for analyzing landscape exchange. The objective of this study is an attempt to assess the land-use changes in Phu Giao district, Binh Duong province, Vietnam in the period of fifteen years, from 2001 to 2015. Landsat Thematic Mapper (TM) image data files of years from 2001 to 2015 were collected on website of United States Geological Survey (USGS). Then, the images supervised were classified into five classes including perennial plant, annual plant, barren and urban land, and water body using Maximum Likelihood classification method in ENVI 4.7, and mapped using ArcGIS. The results show that during fifteen years, perennial land and urban land have been increased by 39.83% and 10.32%, while annual land and water body have been decreased by 1.37% and 5.35% accordingly, respectively. Phát hiện thay đổi số hóa là một kỹ thuật hiệu quả sử dụng hình ảnh vệ tinh đa thời gian cho phân tích thay đổi cảnh quan. Bài viết này là một sự cố gắng nhằm đánh giá sự thay đổi đất sử dụng ở huyện Phú Giáo, tỉnh Bình Dương, Việt Nam trong khoảng thời gian mười lăm năm từ năm 2001 đến năm 2015. Các file dữ liệu ảnh Landsat TM của các năm từ 2001 đến 2015 đã được thu thập trên trang web nghiên cứu Địa chất Hoa Kỳ (USGS). Sau đó, các hình ảnh giám sát được phân thành năm lớp bao gồm cả cây trồng lâu năm, cây trồng hàng năm, đất đô thị cằn cỗi và vùng nước sử dụng phương pháp phân loại Maximum Likelihood trong ENVI 4.7, và lập bản đồ bằng sử dụng ArcGIS. Kết quả cho thấy rằng trong suốt mười lăm năm, diện tích đất trồng cây lâu năm, đất đô thị đã được tăng tương ứng là 39,83% và 10,32%, trong khi đất đai hàng năm và vùng nước giảm 1,37% và 5,35%.

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