Improving Object-Based Land Use/Cover Classification from Medium Resolution Imagery by Markov Chain Geostatistical Post-Classification

Many small islands are located in the southwestern Indian Ocean. These islands have their own environmental specificities and very fragmented landscapes. Generic land use products developed from low and medium resolution satellite images are not suitable for studying these small territories. This is why we have developed a land use/land cover product, called Homisland-IO, based on remote sensing processing on high spatial resolution satellite images acquired by SPOT 5 satellite between December 2012 and July 2014. This product has been produced using an object-based classification process. The overall accuracy of the product is 86%. Homisland-IO is freely accessible through a web portal and is thus available for future use.

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Detailed Urban Land Use Land Cover Classification at the Metropolitan Scale Using a Three-Layer Classification Scheme

Sensors ◽

10.3390/s19143120 ◽

2019 ◽

Vol 19 (14) ◽

pp. 3120 ◽

Cited By ~ 7

Author(s):

Guoyin Cai ◽

Huiqun Ren ◽

Liuzhong Yang ◽

Ning Zhang ◽

Mingyi Du ◽

...

Keyword(s):

Remote Sensing ◽

Land Use ◽

High Resolution ◽

Classification Scheme ◽

Urban Land Use ◽

Land Use Land Cover ◽

Large Urban Area ◽

Object Based ◽

Medium Resolution ◽

Very High

Urban Land Use/Land Cover (LULC) information is essential for urban and environmental management. It is, however, very difficult to automatically extract detailed urban LULC information from remote sensing imagery, especially for a large urban area. Medium resolution imagery, such as Landsat Thematic Mapper (TM) data, cannot uncover detailed LULC information. Further, very high resolution (VHR) satellite imagery, such as IKONOS and QuickBird data, can only be applied to a small area, largely due to the data unavailability and high computation cost. As a result, little research has been conducted to extract detailed urban LULC information for a large urban area. This study, therefore, developed a three-layer classification scheme for deriving detailedurban LULC information by integrating newly launched Chinese GF-1 (medium resolution) and GF-2 (very high resolution) satellite imagery and synthetically incorporating geometry, texture, and spectral information through multi-resolution image segmentation and object-based image classification (OBIA). Homogeneous urban LULC types such as water bodies or large areas of vegetation could be derived from GF-1 imagery with 16 m and 8 m spatial resolutions, while heterogeneous urban LULC types such as industrial buildings, residential buildings, and roads could be extracted from GF-2 imagery with 3.2 m and 0.8 m spatial resolutions. The multi-resolution segmentation method and a random forest algorithm were employed to perform image segmentation and object-based image classification, respectively. An analysis of the results suggests an overall accuracy of 0.89 and 0.87 were achieved for the second and third level urban LULC classification maps, respectively. Therefore, the three-layer classification scheme has the potential to derive high accuracy urban LULC information through integrating medium and high-resolution remote sensing imagery.

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Landscape pattern and economic factors’ effect on prediction accuracy of cellular automata-Markov chain model on county scale

Open Geosciences ◽

10.1515/geo-2020-0162 ◽

2020 ◽

Vol 12 (1) ◽

pp. 626-636

Author(s):

Wang Song ◽

Zhao Yunlin ◽

Xu Zhenggang ◽

Yang Guiyan ◽

Huang Tian ◽

...

Keyword(s):

Land Use ◽

Markov Chain ◽

Cellular Automata ◽

Markov Model ◽

Prediction Accuracy ◽

Driving Mechanism ◽

Economic Factors ◽

Chain Model ◽

The Impact ◽

County Scale

AbstractUnderstanding and modeling of land use change is of great significance to environmental protection and land use planning. The cellular automata-Markov chain (CA-Markov) model is a powerful tool to predict the change of land use, and the prediction accuracy is limited by many factors. To explore the impact of land use and socio-economic factors on the prediction of CA-Markov model on county scale, this paper uses the CA-Markov model to simulate the land use of Anren County in 2016, based on the land use of 1996 and 2006. Then, the correlation between the land use, socio-economic data and the prediction accuracy was analyzed. The results show that Shannon’s evenness index and population density having an important impact on the accuracy of model predictions, negatively correlate with kappa coefficient. The research not only provides a reference for correct use of the model but also helps us to understand the driving mechanism of landscape changes.

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Comparison of object-based and pixel-based methods for urban land-use classification from WorldView-2 imagery

2014 Third International Workshop on Earth Observation and Remote Sensing Applications (EORSA) ◽

10.1109/eorsa.2014.6927896 ◽

2014 ◽

Author(s):

Yanchen Wu ◽

Yinghai Ke ◽

Huili Gong ◽

Beibei Chen ◽

Lin Zhu

Keyword(s):

Land Use ◽

Urban Land ◽

Urban Land Use ◽

Land Use Classification ◽

Object Based

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Predicting Land Use/Land Cover Changes Using a CA-Markov Model under Two Different Scenarios

Sustainability ◽

10.3390/su10103421 ◽

2018 ◽

Vol 10 (10) ◽

pp. 3421 ◽

Cited By ~ 43

Author(s):

Rahel Hamad ◽

Heiko Balzter ◽

Kamal Kolo

Keyword(s):

Land Use ◽

Markov Chain ◽

Land Cover ◽

Land Use Changes ◽

Landsat 8 ◽

Chain Model ◽

Land Use Land Cover ◽

Kurdistan Region ◽

The Past ◽

Past Trend

Multi-temporal Landsat images from Landsat 5 Thematic Mapper (TM) acquired in 1993, 1998, 2003 and 2008 and Landsat 8 Operational Land Imager (OLI) from 2017, are used for analysing and predicting the spatio-temporal distributions of land use/land cover (LULC) categories in the Halgurd-Sakran Core Zone (HSCZ) of the National Park in the Kurdistan region of Iraq. The aim of this article was to explore the LULC dynamics in the HSCZ to assess where LULC changes are expected to occur under two different business-as-usual (BAU) assumptions. Two scenarios have been assumed in the present study. The first scenario, addresses the BAU assumption to show what would happen if the past trend in 1993–1998–2003 has continued until 2023 under continuing the United Nations (UN) sanctions against Iraq and particularly Kurdistan region, which extended from 1990 to 2003. Whereas, the second scenario represents the BAU assumption to show what would happen if the past trend in 2003–2008–2017 has to continue until 2023, viz. after the end of UN sanctions. Future land use changes are simulated to the year 2023 using a Cellular Automata (CA)-Markov chain model under two different scenarios (Iraq under siege and Iraq after siege). Four LULC classes were classified from Landsat using Random Forest (RF). Their accuracy was evaluated using κ and overall accuracy. The CA-Markov chain method in TerrSet is applied based on the past trends of the land use changes from 1993 to 1998 for the first scenario and from 2003 to 2008 for the second scenario. Based on this model, predicted land use maps for the 2023 are generated. Changes between two BAU scenarios under two different conditions have been quantitatively as well as spatially analysed. Overall, the results suggest a trend towards stable and homogeneous areas in the next 6 years as shown in the second scenario. This situation will have positive implication on the park.

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A comparison of pixel-based and object-based approaches for land use land cover classification in semi-arid areas, Sudan

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/37/1/012061 ◽

2016 ◽

Vol 37 ◽

pp. 012061 ◽

Cited By ~ 6

Author(s):

H E Adam ◽

E Csaplovics ◽

M E Elhaja

Keyword(s):

Land Use ◽

Land Cover ◽

Land Cover Classification ◽

Arid Areas ◽

Land Use Land Cover ◽

Object Based ◽

Semi Arid

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ANALISIS PERUBAHAN PENGGUNAAN LAHAN MENJADI LAHAN TERBANGUN PASCA PERPINDAHAN IBUKOTA KABUPATEN SUKABUMI DI WILAYAH TELUK PALABUHANRATU

Jurnal Tataloka ◽

10.14710/tataloka.21.3.407-420 ◽

2019 ◽

Vol 21 (3) ◽

pp. 407

Author(s):

Yuda Pringgo Bayusukmara ◽

Baba Barus ◽

Akhmad Fauzi

Keyword(s):

Land Use ◽

Markov Chain ◽

Land Use Change ◽

Landsat Imagery ◽

Beach Sand ◽

Bay Area ◽

Future Situation ◽

Type Of Land Use ◽

Key Variables ◽

Spatial Plan

The determination of the Capital of Sukabumi Regency had implications on Palabuhanratu Bay area in terms of the physical area marked by the change of land use. This research was begun by analyzing land use change using Landsat imagery. Markov Chain and CA-Markov Chain method were used to predict land use change. Prospective Structural Analysis assume that the future is different from the past and is not imposed, but can be built. MICMAC method were used to determine key variables in influencing the change of land use into built-area. The results showed that in the period of post-relocation, the built-up area had a significant increase than the period of pre-relocation. The prediction results of 2030 indicate the type of land use which had a significant decrease from 2016-2030 were beach sand and waterbodies. The type of land use which had higher increase was built-up area and shrub. The key variables that influence the change of land use into built-up area in Palabuhanratu Bay area in the present situation are distance to the city center, Regional Spatial Plan policy, and slope. In future situation, variables such as distance to cities, Regional Spatial Plan policy, and the proportion of paddy field would be the key variables in influencing the change of land use into built-up area.

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The Mapping of Land Use Using Object-Based Image Analysis (OBIA) in Klaten Regency

E3S Web of Conferences ◽

10.1051/e3sconf/202020206036 ◽

2020 ◽

Vol 202 ◽

pp. 06036

Author(s):

Nurhadi Bashit ◽

Novia Sari Ristianti ◽

Yudi Eko Windarto ◽

Desyta Ulfiana

Keyword(s):

Remote Sensing ◽

Land Use ◽

Image Analysis ◽

Satellite Imagery ◽

Agricultural Land ◽

Scale Parameter ◽

Land Use Classification ◽

Object Based Image Analysis ◽

Object Based ◽

Sentinel 2

Klaten Regency is one of the regencies in Central Java Province that has an increasing population every year. This can cause an increase in built-up land for human activities. The built-up land needs to be monitored so that the construction is in accordance with the regional development plan so that it does not cause problems such as the occurrence of critical land. Therefore, it is necessary to monitor land use regularly. One method for monitoring land use is the remote sensing method. The remote sensing method is much more efficient in mapping land use because without having to survey the field. The remote sensing method utilizes satellite imagery data that can be processed for land use classification. This study uses the sentinel 2 satellite image data with the Object-Based Image Analysis (OBIA) algorithm to obtain land use classification. Sentinel 2 satellite imagery is a medium resolution image category with a spatial resolution of 10 meters. The land use classification can be used to see the distribution of built-up land in Klaten Regency without having to conduct a field survey. The results of the study obtained a segmentation scale parameter value of 60 and a merge scale parameter value of 85. The classification results obtained by 5 types of land use with OBIA. Agricultural land use dominates with an area of 50% of the total area.

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