Rapid and uncontrolled population growth along with economic and industrial development, especially in developing countries during the late twentieth and early twenty-first centuries, have increased the rate of land-use/land-cover (LULC) change many times. Since quantitative assessment of changes in LULC is one of the most efficient means to understand and manage the land transformation, there is a need to examine the accuracy of different algorithms for LULC mapping in order to identify the best classifier for further applications of earth observations. In this article, six machine-learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), fuzzy adaptive resonance theory-supervised predictive mapping (Fuzzy ARTMAP), spectral angle mapper (SAM) and Mahalanobis distance (MD) were examined. Accuracy assessment was performed by using Kappa coefficient, receiver operational curve (RoC), index-based validation and root mean square error (RMSE). Results of Kappa coefficient show that all the classifiers have a similar accuracy level with minor variation, but the RF algorithm has the highest accuracy of 0.89 and the MD algorithm (parametric classifier) has the least accuracy of 0.82. In addition, the index-based LULC and visual cross-validation show that the RF algorithm (correlations between RF and normalised differentiation water index, normalised differentiation vegetation index and normalised differentiation built-up index are 0.96, 0.99 and 1, respectively, at 0.05 level of significance) has the highest accuracy level in comparison to the other classifiers adopted. Findings from the literature also proved that ANN and RF algorithms are the best LULC classifiers, although a non-parametric classifier like SAM (Kappa coefficient 0.84; area under curve (AUC) 0.85) has a better and consistent accuracy level than the other machine-learning algorithms. Finally, this review concludes that the RF algorithm is the best machine-learning LULC classifier, among the six examined algorithms although it is necessary to further test the RF algorithm in different morphoclimatic conditions in the future.

Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review

Commuting by bicycle has advantages over other modes of transport,both for the commuter and for society.Although cycling is an option for many commuters,a considerable number of them choose to use other forms of trans-port.In order to underpin policies that promote commuting by bicy-cle,this paper investigates the de-terminants for commuting to work.As many bicycle commuters do notcycle every day,we also examine people’s daily choices,in terms of frequency.We conducted a survey of the current literature in order to identify the determinants for com-muting by bicycle.We found many determinants,not all of which are addressed by conventional mode choice studies and models.This suggests that predicting and influ-encing bicycle use needs to be grounded in other kinds of knowl-edge than those currently available for motorized forms of transport.

Commuting by Bicycle:An Overview of the Literature

Recent applications of Landsat 8 Operational Land Imager (L8/OLI) and Sentinel-2 MultiSpectral Instrument (S2/MSI) data for acquiring information about land use and land cover (LULC) provide a new perspective in remote sensing data analysis. Jointly, these sources permit researchers to improve operational classification and change detection, guiding better reasoning about landscape and intrinsic processes, as deforestation and agricultural expansion. However, the results of their applications have not yet been synthesized in order to provide coherent guidance on the effect of their applications in different classification processes, as well as to identify promising approaches and issues which affect classification performance. In this systematic review, we present trends, potentialities, challenges, actual gaps, and future possibilities for the use of L8/OLI and S2/MSI for LULC mapping and change detection. In particular, we highlight the possibility of using medium-resolution (Landsat-like, 10–30 m) time series and multispectral optical data provided by the harmonization between these sensors and data cube architectures for analysis-ready data that are permeated by publicizations, open data policies, and open science principles. We also reinforce the potential for exploring more spectral bands combinations, especially by using the three Red-edge and the two Near Infrared and Shortwave Infrared bands of S2/MSI, to calculate vegetation indices more sensitive to phenological variations that were less frequently applied for a long time, but have turned on since the S2/MSI mission. Summarizing peer-reviewed papers can guide the scientific community to the use of L8/OLI and S2/MSI data, which enable detailed knowledge on LULC mapping and change detection in different landscapes, especially in agricultural and natural vegetation scenarios.

Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review

Deep learning (DL) algorithms are considered as a methodology of choice for remote-sensing image analysis over the past few years. Due to its effective applications, deep learning has also been introduced for automatic change detection and achieved great success. The present study attempts to provide a comprehensive review and a meta-analysis of the recent progress in this subfield. Specifically, we first introduce the fundamentals of deep learning methods which arefrequently adopted for change detection. Secondly, we present the details of the meta-analysis conducted to examine the status of change detection DL studies. Then, we focus on deep learning-based change detection methodologies for remote sensing images by giving a general overview of the existing methods. Specifically, these deep learning-based methods were classified into three groups; fully supervised learning-based methods, fully unsupervised learning-based methods and transfer learning-based techniques. As a result of these investigations, promising new directions were identified for future research. This study will contribute in several ways to our understanding of deep learning for change detection and will provide a basis for further research.

Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis

https://repositorio.ul.pt/bitstream/10451/50072/1/Viana_Freire_Abrantes_Rocha_Pereira_2021.pdf

Agricultural land systems importance for supporting food security and sustainable development goals: A systematic review.

The increasing availability and volume of remote sensing data, such as Landsat satellite images, have allowed the multidimensional analysis of land use/land cover (LULC) changes. However, the performance of image classification is highly dependent on the quality and quantity of the training set and its temporal continuity, which may affect the accuracy of the classification and bias the analysis of the LULC changes. In this study, we intended to apply a long-term LULC analysis in a rural region based on a Landsat time series of 21 years (1995 to 2015). Here, we investigated the use of open LULC source data to provide training samples and the application of the K-means clustering technique to refine the broad range of spectral signatures for each LULC class. Experiments were conducted on a predominantly rural region characterized by a mixed agro-silvo-pastoral environment. The open source data of the official Portuguese LULC map (Carta de Uso e Ocupacao do Solo, COS) from 1995, 2007, 2010, and 2015 were integrated to generate the training samples for the entire period of analysis. The time series was computed from Landsat data based on the normalized difference vegetation index and normalized difference water index, using 221 Landsat images. The Time-Weighted Dynamic Time Warping (TWDTW) classifier was used, since it accounts for LULC-type seasonality and has already achieved promising overall accuracy values for classifications based on time series. The results revealed that the proposed method was efficient in classifying a long-term satellite time-series with an overall accuracy of 76%, providing insights into the main LULC changes that occurred over 21 years.

/pdf/long-term-satellite-image-time-series-for-land-use-land-1vvszj1wsn.pdf

Long-Term Satellite Image Time-Series for Land Use/Land Cover Change Detection Using Refined Open Source Data in a Rural Region

The extended node-place model at the local scale: Evaluating the integration of land use and transport for Lisbon's subway network

This study presents a proposed application of the time-weighted dynamic time warping method for urban sprawl analysis. Four spectral indices were computed from a long time-series of Landsat satellite imagery, corresponding to 48 scenes acquired between 2006 and 2018. The spectral indices were the normalized difference vegetation index, the normalized difference built-up index, the normalized difference water index, and the normalized difference bareness index, which when processed resulted in 192 different images. The R package dtwSat was used for image processing, since it represents one of the few open-source software programs available for processing large time-series datasets. The method was tested applied in the Alentejo Region of southern Portugal; traditionally a rural region, where urban sprawl presents risks for the preservation of agricultural systems and for ecosystem sustainability. The sprawl analysis was integrated in a geographic information system, in which we computed an expansion index to quantitatively assess the three main urban land expansion types: infill, extension, and leapfrog. The results show that, between 2007 and 2012, the main changes are due to extension (50 ha), but with a significant amount of infill (36 ha) and leapfrog growth (4 ha), with the latter being the worst-case scenario. However, in the subsequent period, 2012–17, urban growth decreased to about 10 ha, comprising both infill and extension, but notably leapfrog expansion disappeared. Our methodology proved to be flexible for managing irregular sampling and an out-of-phase time-series. The procedure offers a quantitative means of assessing urban sprawl dynamics and represents a potential strategy for defining sustainable urban development.

Land use/land cover change detection and urban sprawl analysis

As a result of mounting concerns over the adverse ecological and socio-economic effects of mobility systems dominated by individual motorized transport, metropolitan areas worldwide have expressed a renewed interest in the role of public transport. Like many other metropolitan areas, the Brussels Capital Region (BCR) faces the problem of an increasingly congested transport system. Against this backdrop, the Regional Express Railway (RER) network is intended as a rapid-transit railway system serving an area of 30 km around the region, with the objective of improving the capacity and frequency of the railway services between the BCR and its periphery. In order to inform policy prescription, this paper reports on a systematic empirical assessment of all RER railway stations in terms of transport and land use characteristics, by drawing on the node-place modeling and transit oriented development literature. The proposed accessibility instrument considers different catchment area sizes in order to increase its empirical basis. Based on this systematic railway station inventory, cluster analysis was conducted revealing seven comparative accessibility profiles, of which some prove highly robust over the different precinct sizes. When combining the quantitative analyses reported in this paper with the more intuitive expertise of practitioners and stakeholders involved in the planning process, the accessibility instrument may effectively assist the identification of differentiated development opportunities for the RER stations.

Cláudia M. Viana

Papers

Agricultural land systems importance for supporting food security and sustainable development goals: A systematic review.

Long-Term Satellite Image Time-Series for Land Use/Land Cover Change Detection Using Refined Open Source Data in a Rural Region

The extended node-place model at the local scale: Evaluating the integration of land use and transport for Lisbon's subway network

Land use/land cover change detection and urban sprawl analysis

Measuring the Accessibility of Railway Stations in the Brussels Regional Express Network: a Node-Place Modeling Approach