4.7 Article

Spatio-temporal classification and prediction of land use and land cover change for the Vembanad Lake system, Kerala: a machine learning approach

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Meteorology & Atmospheric Sciences

Bias Correction and Trend Analysis of Temperature Data by a High-Resolution CMIP6 Model over a Tropical River Basin

Dinu Maria Jose et al.

Summary: This study employed six bias correction methods to correct climate variables in the Netravati basin in India, with most methods showing a considerable reduction in bias. Historical and future temperature data reveal a trend of increasing temperatures.

ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES (2022)

Article Environmental Sciences

Artificial neural network and sensitivity analysis in the landslide susceptibility mapping of Idukki district, India

Jesudasan Jacinth Jennifer et al.

Summary: The study developed two ANN models to generate landslide susceptibility maps, showing similar trends with the observed landslide locations in the region.

GEOCARTO INTERNATIONAL (2022)

Article Environmental Sciences

Detailed and automated classification of land use/land cover using machine learning algorithms in Google Earth Engine

Xia Pan et al.

Summary: A novel method for detailed and automated LULC classification based on RF and CART classifiers was proposed in this study to address issues of insufficient training samples and time-consuming processes. Results showed that the RF classifier had higher validation overall accuracy compared to CART, making it more suitable for automated LULC classification in Australia and the USA.

GEOCARTO INTERNATIONAL (2022)

Article Environmental Sciences

Assessment of land use and land cover change detection and prediction using remote sensing and CA Markov in the northern coastal districts of Tamil Nadu, India

Devanantham Abijith et al.

Summary: The study utilized GEE, TerrSet, and GIS tools to analyze LULC changes on the Northern TN coast between 2009-2019 and 2019-2030, revealing trends of decreased water bodies, increased built-up areas, and conversion of barren land and vegetation into built-up areas. The overall accuracy was above 89%, providing valuable insights for urban development planning and coastal flooding prevention.

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2022)

Article Engineering, Geological

Physical model studies on damage and stability analysis of breakwaters armoured with geotextile sand containers

Tom Elias et al.

Summary: Research conducted a scaled physical experiment to test the feasibility of using geotextile sand containers as breakwater armour units, finding that increasing sand fill ratio and armour unit size can enhance the stability of the structure.

GEOTEXTILES AND GEOMEMBRANES (2021)

Article Computer Science, Interdisciplinary Applications

O-LCMapping: a Google Earth Engine-based web toolkit for supporting online land cover classification

Huaqiao Xing et al.

Summary: This paper introduces a web-based toolkit called O-LCMapping, which supports online land cover classification. By providing a complete classification process through user interfaces, the toolkit is suitable for users with basic remote sensing knowledge but limited programming skills. Experimental cases demonstrate that the toolkit can be easily applied to various applications of land cover classification in different fields.

EARTH SCIENCE INFORMATICS (2021)

Article Environmental Sciences

Assessing the Effect of Training Sampling Design on the Performance of Machine Learning Classifiers for Land Cover Mapping Using Multi-Temporal Remote Sensing Data and Google Earth Engine

Shobitha Shetty et al.

Summary: Machine learning classifiers, particularly Random Forest, were evaluated for their performance in Land Use and Land Cover (LULC) mapping using multi-temporal satellite remote sensing data. Different sampling methods were found to have varying impacts on the classification results, with Stratified Proportional Random Sampling (SRS(Prop)) favoring major classes, Stratified Equal Random Sampling (SRS(Eq)) providing good accuracies for minority classes, and Stratified Systematic Sampling (SSS) performing well for areas with large intra-class variability. Random Forest outperformed other machine learning classifiers with a confidence level of over 95%, while Support Vector Machine and Classification and Regression Trees showed similar performance. Relevance Vector Machine achieved good results with limited training samples.

REMOTE SENSING (2021)

Article Green & Sustainable Science & Technology

Modeling and Prediction of Land Use Land Cover Change Dynamics Based on Land Change Modeler (LCM) in Nashe Watershed, Upper Blue Nile Basin, Ethiopia

Megersa Kebede Leta et al.

Summary: This study utilized Landsat images and LCM to analyze the temporal and spatial dynamics of land use land cover in the Nashe watershed of Ethiopia, predicting a further decline in forest cover and expansion of agricultural land in the future. The main trend of future LULC change will be the conversion of forest areas, range land, and grass land into agricultural land.

SUSTAINABILITY (2021)

Article Computer Science, Interdisciplinary Applications

Development of SLEUTH-Density for the simulation of built-up land density

Ankita Saxena et al.

Summary: Urban growth is a complex phenomenon involving both horizontal and vertical built-up activities. Simulating built-up land density can help planning authorities make better decisions, and a new version of the SLEUTH model has been used to simulate built-up land density in Ajmer city, India, with results validating the accuracy of the simulation.

COMPUTERS ENVIRONMENT AND URBAN SYSTEMS (2021)

Article Biodiversity Conservation

Modeling land cover change based on an artificial neural network for a semiarid river basin in northeastern Brazil

Leonardo Pereira e Silva et al.

GLOBAL ECOLOGY AND CONSERVATION (2020)

Review Environmental Sciences

An Overview of Platforms for Big Earth Observation Data Management and Analysis

Vitor C. F. Gomes et al.

REMOTE SENSING (2020)

Article Green & Sustainable Science & Technology

Spatiotemporal Analysis of Land Cover Changes in the Chemoga Basin, Ethiopia, Using Landsat and Google Earth Images

Wubeshet Damtea et al.

SUSTAINABILITY (2020)

Review Geography, Physical

Google Earth Engine for geo-big data applications: A meta-analysis and systematic review

Haifa Tamiminia et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2020)

Review Geosciences, Multidisciplinary

Uncertainties in predicting impacts of climate change on hydrology in basin scale: a review

Dinu Maria Jose et al.

ARABIAN JOURNAL OF GEOSCIENCES (2020)

Article Environmental Sciences

Land use and land cover change detection using geospatial techniques in the Sikkim Himalaya, India

Prabuddh Kumar Mishra et al.

EGYPTIAN JOURNAL OF REMOTE SENSING AND SPACE SCIENCES (2020)

Article Computer Science, Interdisciplinary Applications

Land suitability and urban growth modeling: Development of SLEUTH-Suitability

Ankita Saxena et al.

COMPUTERS ENVIRONMENT AND URBAN SYSTEMS (2020)

Article Agriculture, Multidisciplinary

Classification of Indian cities using Google Earth Engine

Shivani Agarwal et al.

JOURNAL OF LAND USE SCIENCE (2019)

Article Environmental Sciences

Prediction of spatial land use changes based on LCM in a GIS environment for Desert Wetlands - A case study: Meighan Wetland, Iran

Amir Ansari et al.

INTERNATIONAL SOIL AND WATER CONSERVATION RESEARCH (2019)

Article Environmental Sciences

Remote Sensing Data and SLEUTH Urban Growth Model: As Decision Support Tools for Urban Planning

Inoka Sandamali Serasinghe Pathiranage et al.

CHINESE GEOGRAPHICAL SCIENCE (2018)

Article Environmental Sciences

Deriving suitability factors for CA-Markov land use simulation model based on local historical data

Xin Fu et al.

JOURNAL OF ENVIRONMENTAL MANAGEMENT (2018)

Article Remote Sensing

Using Google Earth Engine to detect land cover change: Singapore as a use case

Nanki Sidhu et al.

EUROPEAN JOURNAL OF REMOTE SENSING (2018)

Article Green & Sustainable Science & Technology

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

Rahel Hamad et al.

SUSTAINABILITY (2018)

Review Remote Sensing

Remote sensing of forest insect disturbances: Current state and future directions

Cornelius Senf et al.

INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION (2017)

Article Environmental Sciences

Google Earth Engine: Planetary-scale geospatial analysis for everyone

Noel Gorelick et al.

REMOTE SENSING OF ENVIRONMENT (2017)

Article Computer Science, Interdisciplinary Applications

Modification of the random forest algorithm to avoid statistical dependence problems when classifying remote sensing imagery

Fulgencio Canovas-Garcia et al.

COMPUTERS & GEOSCIENCES (2017)

Review Geography, Physical

Random forest in remote sensing: A review of applications and future directions

Mariana Belgiu et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2016)

Article Computer Science, Interdisciplinary Applications

Supporting SLEUTH - Enhancing a cellular automaton with support vector machines for urban growth modeling

Andreas Rienow et al.

COMPUTERS ENVIRONMENT AND URBAN SYSTEMS (2015)

Review Computer Science, Artificial Intelligence

Support vector machine applications in the field of hydrology: A review

Sujay N. Raghavendra et al.

APPLIED SOFT COMPUTING (2014)

Article Remote Sensing

Modeling multiple land use changes using ANN, CART and MARS: Comparing tradeoffs in goodness of fit and explanatory power of data mining tools

Amin Tayyebi et al.

INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION (2014)

Article Geography, Physical

Evaluating the impact of red-edge band from Rapideye image for classifying insect defoliation levels

Samuel Adelabu et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2014)

Article Green & Sustainable Science & Technology

Simulating Urban Growth Using the SLEUTH Model in a Coastal Peri-Urban District in China

Lizhong Hua et al.

SUSTAINABILITY (2014)

Review Geography, Physical

Parameterizing Support Vector Machines for Land Cover Classification

Xiaojun Yang

PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING (2011)

Article Remote Sensing

A kernel functions analysis for support vector machines for land cover classification

T. Kavzoglu et al.

INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION (2009)

Article Environmental Sciences

Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors

Gyanesh Chander et al.

REMOTE SENSING OF ENVIRONMENT (2009)

Article Computer Science, Artificial Intelligence

Random Forests for land cover classification

PO Gislason et al.

PATTERN RECOGNITION LETTERS (2006)

Article Remote Sensing

An assessment of support vector machines for land cover classification

C Huang et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2002)

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)