Fast Urban Land Cover Mapping Exploiting Sentinel-1 and Sentinel-2 Data

Petrushevsky, Naomi; Manzoni, Marco; Guarnieri, Andrea Monti

doi:10.3390/rs14010036

Cited by 11 publications

(3 citation statements)

References 44 publications

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“…Meantime, the pixel‐based (PB) approach suffers from the “salt‐pepper” effect on higher spatial resolutions. An OO LULC classification approach was used to group pixels into meaningful objects based on their spectral, spatial, and temporal characteristics (Petrushevsky et al, 2022).…”

Section: Methodsmentioning

confidence: 99%

A non‐grain production on cropland spatiotemporal change detection method based on Landsat time‐series data

He,

Jiang,

Xiao

et al. 2024

Land Degrad Dev

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Global food security is being threatened by the reduction of high‐quality cropland, extreme weather events, and the uncertainty of food supply chains. The globalization of agricultural trade has elevated the diversification of non‐grain production (NGP) on cultivated land to a prominent strategy for poverty alleviation in numerous developing nations. Its rapid expansion has engendered a multitude of deleterious consequences on both food security and ecological stability. NGP in China is becoming very common in the process of rapid urbanization, threatening national food security. To better understand the causal mechanisms and enable governments to balance food security and rural development, it is crucial to have a clear understanding of the spatiotemporal dynamics of NGP using remote sensing. Yet knowledge gaps remain concerning how to use remote sensing to track human‐dominated or ‐induced long‐term cultivated land changes. Our study proposed a method for detecting the spatiotemporal evolution of NGP based on Landsat time‐series data under the Google Earth Engine platform. This approach was proposed by (1) obtaining the union of cultivated lands from multiple landcover products to minimize the cultivated land omission, (2) constructing multi‐index dynamic trend rules for 3 representative types of NGP and obtaining results at the pixel level, while adopting the continuous change detection and classification algorithm to Landsat time series (1986–2022) to determine when the most recent change occurred, (3) minimizing the noise by object‐oriented land use–land cover classification and mode filter approaches, and (4) mapping the spatiotemporal distribution of NGP. The proposed methodology was tested in Jiashan, located in Zhejiang Province (eastern China), where NGP is widespread. We achieved a high overall accuracy of 95.67% for NGP type detection and an overall accuracy of 85.26% for change detection of time. The results indicated a continued increasing pattern of NGP in Jiashan from 1986 to 2022, with the cumulative percentage of NGP increasing from 0.02% to 20.69%. This study highlights the utilization of time‐series data to document essential NGP information for evaluating food security in China and the method is well‐suited for large‐scale mapping due to its automatic manner.

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Section: Methodsmentioning

confidence: 99%

A non‐grain production on cropland spatiotemporal change detection method based on Landsat time‐series data

He,

Jiang,

Xiao

et al. 2024

Land Degrad Dev

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“…A further analysis of the previous method was proposed by the same authors in 2022 [57]. Here, the Sentinel-2 image was used to identify clusters of similar pixels.…”

Section: Type C-land Consumption Mapping Using Data Fusion or Data In...mentioning

confidence: 99%

Land Consumption Classification Using Sentinel 1 Data: A Systematic Review

Mastrorosa

Crespi

Congedo

et al. 2023

Land

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The development of remote sensing technology has redefined the approaches to the Earth’s surface monitoring. The Copernicus Programme promoted by the European Space Agency (ESA) and the European Union (EU), through the launch of the Synthetic Aperture Radar (SAR) Sentinel-1 and the multispectral Sentinel-2 satellites, has provided a valuable contribution to monitoring the Earth’s surface. There are several review articles on the land use/land cover (LULC) matter using Sentinel images, but it lacks a methodical and extensive review in the specific field of land consumption monitoring, concerning the application of SAR images, in particular Sentinel-1 images. In this paper, we explored the potential of Sentinel-1 images to estimate land consumption using mathematical modeling, focusing on innovative approaches. Therefore, this research was structured into three principal steps: (1) searching for appropriate studies, (2) collecting information required from each paper, and (3) discussing and comparing the accuracy of the existing methods to evaluate land consumption and their applied conditions using Sentinel-1 Images. Current research has demonstrated that Sentinel-1 data has the potential for land consumption monitoring around the world, as shown by most of the studies reviewed: the most promising approaches are presented and analyzed.

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“…Other studies have successfully mapped AIS using Sentinel-1 backscatter [49,50] or interferometric coherence [51,52]. Lately, studies have leveraged the joint use of ALOS PALSAR, Sentinel-1 SAR and/or Sentinel-2 multispectral imagery and applying unsupervised clustering, supervised classification, or deep learning [53][54][55][56][57].…”

Section: Introductionmentioning

confidence: 99%

Automated Method for Artificial Impervious Surface Area Mapping in Temperate, Tropical, and Arid Environments Using Hyperlocal Training Data With Sentinel-2 Imagery

Lasko,

O'Neill

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This study presents an automated methodology to generate training data to map update-to-date Artificial Impervious Surface (AIS) extent maps using two dates (winter and non-winter) of a Sentinel-2 granule across six international sites (Egypt, India, Qatar, UK, Eastern USA, and Western USA). It uses a series of spectral, textural, and distance decision functions combined with an outdated AIS layer to create nontarget and target binary masks from which to generate a balanced set of training data applied to a random forest classifier. Two outdated global AIS layers (GMIS-2010 and GISA-2016) were evaluated within the framework to create AIS maps from more recent years (e.g. 2020). For the decision functions, stepwise threshold adjustments to NDVI and Euclidean distance layers were evaluated on the binary masks (low density AIS, high density AIS, and non-target land covers) with 729 permutations and 115 permutations for GISA and GMIS respectively. The optimal thresholds were determined globally (all six scenes), individually (scene) and grouped by climate for adaptive thresholds. The accuracy assessment found both GMIS-output and GISA-output with global thresholds can accurately map current AIS with 86.9% (±1.7%) (GISA) and 82.7% (±2.3%) (GMIS) accuracy. Adaptive climate thresholds yielded slightly higher accuracies for temperate, tropics, and arid scenes. A novel beach bare ground sampling mask and annual NDVI standard deviation were also evaluated for performance and improved the accuracy in 5/6 sites. Lastly, the global GISA output was compared with a manually-labeled deep learning model (Esri), which outperformed it only slightly (86.9% vs 88.6% overall accuracy).

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Fast Urban Land Cover Mapping Exploiting Sentinel-1 and Sentinel-2 Data

Cited by 11 publications

References 44 publications

A non‐grain production on cropland spatiotemporal change detection method based on Landsat time‐series data

A non‐grain production on cropland spatiotemporal change detection method based on Landsat time‐series data

Land Consumption Classification Using Sentinel 1 Data: A Systematic Review

Automated Method for Artificial Impervious Surface Area Mapping in Temperate, Tropical, and Arid Environments Using Hyperlocal Training Data With Sentinel-2 Imagery

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