Spatiotemporal dynamics of urbanization and cropland in the Nile Delta of Egypt using machine learning and satellite big data: implications for sustainable development

Badreldin, Nasem; Hatab, Assem Abu; Lagerkvist, Carl Johan

doi:10.1007/s10661-019-7934-x

Cited by 18 publications

(10 citation statements)

References 57 publications

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“…To further improve the accuracy of land‐use classification, all images are preprocessed one by one in ENVI Software, including radiometric calibration, atmospheric correction, image registration, and cropping. And then, the classification is implemented in ERDAS Software, and the producer's accuracy (PA, %), which represents how well reference pixels of the ground cover type are classified, and the user's accuracy (UA, %), which represents the probability of a classified pixel into a given category that represents that category on the ground (Patel & Kaushal, 2010; Rasool et al, 2021), are obtained through confusion matrix calculation (which provides a summary in a cross‐tabulation format between the classified classes and the ground‐truthing data) (Badreldin et al, 2019, 2021). Finally, overall accuracy (OA, which is the total accurate prediction among all classified classes) and kappa value are obtained.…”

Section: Methodsmentioning

confidence: 99%

Dynamic simulation of debris flow waste‐shoal land use based on an integrated system dynamics–geographic information systems model

Wang

Zhao

et al. 2022

Land Degrad Dev

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Given the increased conflict between human development and land use in fragile mountainous areas, debris flow waste-shoal land (DFWSL) use has shown beneficial effects and drawn public concern in recent years. However, temporal-spatial future change trends of DFWSL are not yet clear, meanwhile widely applied singular methods, including the system dynamics (SD) model and geographic information systems (GIS), are unsatisfactory. To better assess the sustainability and challenges of DFWSL use and provide reference for future development, we propose a hybrid SD-GIS model for characterizing complex relationships and performing dynamic simulations and spatial spreading. A causality flowchart was plotted and response equation defining the changes was established to predict macro land-use demands in the SD model component. Additionally, the spatial distribution of land-use types was determined using the GIS model component. The Awang DFWSL system in the Yunnan Province, China was analyzed using the coupled method. The results revealed a steadily declining trend in cultivated land and water areas accompanied by a substantial increase in construction land. Future projections indicate that from 2018 to 2025, the total area of cultivated land will decrease by nearly half compared with that in 2018 and decrease to one-quarter of the level in 2018 by 2030. These predictions consider the reciprocal influences of social, economic, and ecological indicators, and risk factors, such as debris flow scale and frequency. This study provides a new approach for estimating future trends of DFWSL use and increases the awareness of the spatiotemporal changes encountered when using marginal land. K E Y W O R D S debris flow waste-shoal land, dynamic land-use simulation, ecologically fragile area, SD-GIS model, spatiotemporal changes 1 | INTRODUCTION Despite global climate change, population growth, and accelerated urbanization, debris flow waste-shoal land (DFWSL) has been utilized in low-and medium-risk mountainous areas to harmonize mountain region development with environmental regulations. It is an important initiative for sustainable mountain development (He et al., 2018, 2019). DFWSL provides adequate space for industrialization and urbanization while simultaneously furnishing a buffer space for ecological conservation. It could help solve the problem of cultivated land

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Section: Methodsmentioning

confidence: 99%

Dynamic simulation of debris flow waste‐shoal land use based on an integrated system dynamics–geographic information systems model

Wang

Zhao

et al. 2022

Land Degrad Dev

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“…De'Ath and Fabricius (2000) concluded that a decision-based tree classification is a powerful tool for ecological research because of the following reasons: (1) flexibility to import a diverse data type; (2) ability to test the importance of many variables; (3) capability to assess modeling progress and strength; and (4) feasibility to generate reliable outputs in environmental research. The RF classifier is based on the generated decision trees from the aggregated bootstrapped training samples from ground-truthing [18,44], which was computed based on [45] steps, as follow:…”

Section: Random Forest (Rf) Classificationmentioning

confidence: 99%

“…The first assessment is the confusion matrix, which provides a summary in a cross-tabulation format between the classified classes and the ground-truthing data (Foody, 2002(Foody, , 2010. This assessment included information on the overall accuracy (the total accurate prediction among all classified classes), the user's accuracy (%), which represents the probability of a classified pixel into a given category that actually represents that category on the ground, and the producer's accuracy (%) which represents how well reference pixels of the ground cover type are classified [18]. According to [47], Kappa indices failed to reflect and offer useful information on the classification accuracy because of the following two reasons: (1) it compares the accuracy to the baseline of randomness; and (2) it cannot deliver useful and fundamental information on the classification accuracy.…”

Section: Accuracy Assessmentmentioning

confidence: 99%

“…The big geospatial dataset is a relatively new concept and not clearly defined [17] Li et al (2016), but it facilitates new opportunities to discover and assess landcover types at multi-dimensions [18]. Laney (2001) [19] defines big data as volume, velocity, and variety (3Vs), and Suthaharan (2014) [20] defines big data to cardinality, continuity, and complexity (3Cs).…”

Section: Introductionmentioning

confidence: 99%

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Mapping Grasslands in Mixed Grassland Ecoregion of Saskatchewan Using Big Remote Sensing Data and Machine Learning

2021

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Accurate spatial distribution information of native, mixed, and tame grasslands is essential for maintaining ecosystem health in the Prairie. This research aimed to use the latest monitoring technology to assess the remaining grasslands in Saskatchewan’s mixed grassland ecoregion (MGE). The classification approach was based on 78 raster-based variables derived from big remote sensing data of multispectral optical space-borne sensors such as MODIS and Sentinel-2, and synthetic aperture radar (SAR) space-borne sensors such as Sentinel-1. Principal component analysis (PCA) was used as a data dimensionality reduction technique to mitigate big data load and improve processing time. Random Forest (RF) was used in the classification process and incorporated the selected variables from 78 satellite-based layers and 2385 reference training points. Within the MGE, the overall accuracy of the classification was 90.2%. Native grassland had 98.20% of user’s accuracy and 88.40% producer’s accuracy, tame grassland had 81.4% user’s accuracy and 93.8% producer’s accuracy, whereas mixed grassland class had very low user’s accuracy (45.8%) and producer’s accuracy 82.83%. Approximately 3.46 million hectares (40.2%) of the MGE area are grasslands (33.9% native, 4% mixed, and 2.3% tame). This study establishes a novel analytical framework for reliable grassland mapping using big data, identifies future challenges, and provides valuable information for Saskatchewan and North America decision-makers.

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“…Nearly 90% of global urban expansion is predicted to take place in developing countries, especially in Asia and Africa, where the largest numbers of the world's poor and undernourished people are concentrated [2]. Therefore, urbanization is increasingly recognized as a major driver of land use/land cover changes (LULC) changes that progressively affect the socioeconomic and environmental landscape in developing countries [3]. For instance, the 2030 Agenda for Sustainable Development and the New Urban Agenda recognize urbanization and demographic changes as key components of resilient and sustainable development [4].…”

Section: Introductionmentioning

confidence: 99%

Trade-Offs between Economic Benefits and Ecosystem Services Value under Three Cropland Protection Scenarios for Wuhan City in China

Wang

Hatab

2020

Land

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Over the past few decades urbanization and population growth have been the main trend all over the world, which brings the increase of economic benefits (EB) and the decrease of cropland. Cropland protection policies play an important role in the urbanization progress. In this study, we assess the trade-offs between EB and ecosystem services value (ESV) under three cropland protection policy scenarios using the LAND System Cellular Automata for Potential Effects (LANDSCAPE) model. The empirical results reveal that trade-offs between EB and ESV in urbanizing areas are dynamic, and that they considerably vary under different cropland protection policy scenarios. Especially, the results identify certain “turning points” for each policy scenario at which a small to moderate growth in EB would result in greater ESV losses. Among the three scenarios, we found that the cropland protection policy has the most adverse effect on trade-offs between EB and ESV and the results in the business as usual scenario have the least effect on the trade-offs. Furthermore, the results show that a strict balance between requisition and compensation of cropland is an inappropriate policy option in areas where built-up areas are increasing rapidly from the perspective of mitigating conflict between EB and ESV and the numbers of cropland protection that restrained by land use planning policy of Wuhan is a better choice.

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Spatiotemporal dynamics of urbanization and cropland in the Nile Delta of Egypt using machine learning and satellite big data: implications for sustainable development

Cited by 18 publications

References 57 publications

Dynamic simulation of debris flow waste‐shoal land use based on an integrated system dynamics–geographic information systems model

Dynamic simulation of debris flow waste‐shoal land use based on an integrated system dynamics–geographic information systems model

Mapping Grasslands in Mixed Grassland Ecoregion of Saskatchewan Using Big Remote Sensing Data and Machine Learning

Trade-Offs between Economic Benefits and Ecosystem Services Value under Three Cropland Protection Scenarios for Wuhan City in China

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