Spatial modeling of land use and land cover change in Sulaimani, Iraq, using multitemporal satellite data

Khwarahm, Nabaz R.

doi:10.1007/s10661-021-08959-6

Cited by 39 publications

(7 citation statements)

References 63 publications

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“…The validation findings indicate that the Kstandard (Kappa) value of this model's accuracy test is 0.906 or 90.6% (Figure 5), meaning that the agreement value between the prediction model and the actual land use in 2020 is relatively high, allowing this model to be utilized for predictions in 2030. Predictions based on prior research with a Kstandard value greater than 70% are trustworthy (Darmawan et al 2020;Khwarahm 2021;Munthali et al 2020).…”

Section: Results and Discussion Land Cover Changes In Banjarmasin Citymentioning

confidence: 99%

CA-Markov Chain Model-based Predictions of Land Cover: A Case Study of Banjarmasin City

Supriatna

Mukhtar²,

Wardani³

et al. 2022

IJG

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Land cover change is a prevalent thing in Indonesia. This phenomenon often causes deforestation rates to continue to increase every year, which can cause various natural disasters. This study will look at changes in land cover, make land cover prediction models, and see the relationship between land cover changes and the flood disaster that occurred in Banjarmasin City and its surroundings. Remote sensing is used to see changes in land cover from year to year with GlobeLand30 satellite imagery. Satellite imagery processing is carried out using the Cellular Automata – Markov Chain method to see the land cover prediction. The results show that the most significant land cover change from 2000 to 2020 is experienced by built-up land and forests, while in 2030, forests are predicted to experience deforestation of 356 km2 from 2020. The deforestation will cause catastrophic flooding in 2021, where flooding extends to areas that are not estimated to be high flood hazards, with 111 flood points located in the plantation area.

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Section: Results and Discussion Land Cover Changes In Banjarmasin Citymentioning

confidence: 99%

CA-Markov Chain Model-based Predictions of Land Cover: A Case Study of Banjarmasin City

Supriatna

Mukhtar²,

Wardani³

et al. 2022

IJG

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“…The literature describes the benefits of high-resolution satellites, quick delivery, and fine details [18]. After the appearance of high-resolution satellites was removed, this section presents an overview of the high-resolution expected.…”

Section: Development Of Land Observation Satellites (Los)mentioning

confidence: 99%

Artificial Intelligence Techniques for Observation of Earth’s Changes

Alshari¹,

Gawali²

2023

Satellite Altimetry - Theory, Applications and Recent Advances

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This chapter discusses the primary components that contribute to the observation of Earth’s changes, including Land Observation Satellites, land classification techniques and their stages of development, and Machine Learning Techniques. It will give a comprehensive summary of the development stages of high-resolution satellites. It also details land classification with artificial intelligence algorithms. It will also give knowledge of classification methodologies from various Fundamentals of Machine Learning Classifiers: Pixel-based (PB), Sub-pixel-based (SPB), Object-based (OB), Knowledge-based (KB), Rule-based (RB), Distance-based (DB), Neural-based (NB), Parameter Based (PB), object-based image analysis (OBIA). It includes several different classifiers for LULC Classification. This chapter will include two applications for land observation satellites: The first is land use and land cover change observation with a practical example (study land use and land cover classification for Sana’a of Yemen as a case study from 1980 to 2020). The second application is satellite altimetry monitoring changes in mean sea level. The most significant contributions of it are the integration of these components. This chapter will be crucial in helping future researchers comprehend this topic. It will aid them in selecting the most appropriate and effective satellites for monitoring Earth’s changes and the most efficient classifier for their research.

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“…They did not attempt to detect or predict a new oil field, rather they studied ground subsidence due to the extraction of groundwater and oil from the existing oil deposits 20 . Nabaz et al used remotely sensed Landsat satellite imagery, geographic information systems, and the hybrid cellular automata (Markov model) to study the region of Sulaimani Province in Kurdistan, Iraq 21 . Before them, Rahel et al did a similar work over the Halgurd-Sakran Core Zone of the National Park in the Kurdistan, Iraq 22 using Landsat-5 and Landsat-8 images in association with the Cellular Automata (Markov chain) model.…”

Section: Related Work and Gap In The Literaturementioning

confidence: 99%

A ubiquitous method for predicting underground petroleum deposits based on satellite data

Newaz

Tahmid

Al-Aboody

et al. 2023

Sci Rep

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The method of finding new petroleum deposits beneath the earth’s surface is always challenging for having low accuracy while simultaneously being highly expensive. As a remedy, this paper presents a novel way to predict the locations of petroleum deposits. Here, we focus on a region of the Middle East, Iraq to be specific, and conduct a detailed study on predicting locations of petroleum deposits there based on our proposed method. To do so, we develop a new method of predicting the location of a new petroleum deposit based on publicly available data sensed by an open satellite named Gravity Recovery and Climate Experiment (GRACE). Using GRACE data, we calculate the gravity gradient tensor of the earth over the region of Iraq and its surroundings. We use this calculated data to predict the locations of prospective petroleum deposits over the region of Iraq. In the process of our study for making the predictions, we leverage machine learning, graph-based analysis, and our newly-proposed OR-nAND method altogether. Our incremental improvement in the proposed methodologies enables us to predict 25 out of 26 existing petroleum deposits within the area under our study. Additionally, our method shows some prospective petroleum deposits that need to be explored physically in the future. It is worth mentioning that, as our study presents a generalized approach (demonstrated through investigating multiple datasets), we can apply it anywhere in the world beyond the area focused on in this study as an experimental case.

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Spatial modeling of land use and land cover change in Sulaimani, Iraq, using multitemporal satellite data

Cited by 39 publications

References 63 publications

CA-Markov Chain Model-based Predictions of Land Cover: A Case Study of Banjarmasin City

CA-Markov Chain Model-based Predictions of Land Cover: A Case Study of Banjarmasin City

Artificial Intelligence Techniques for Observation of Earth’s Changes

A ubiquitous method for predicting underground petroleum deposits based on satellite data

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