Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Browser

Remote Sensing of Land Use and Land Change with Google Earth Engine

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Related Special Issue
Published Papers

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 68702

Share This Special Issue

Special Issue Editors

Dr. Wu Xiao

E-Mail Website
Guest Editor

Department of Land Management, Zhejiang University, Hangzhou 310058, China
Interests: ecological restoration; geographic information system; unmanned aerial vehicles; land reclamation; remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Qiusheng Wu

E-Mail Website1 Website2
Guest Editor

Department of Geography, University of Tennessee, Knoxville, TN 37996, USA
Interests: remote sensing; GIS; wetland hydrology; LiDAR; google earth engine
Special Issues, Collections and Topics in MDPI journals

Dr. Xuecao Li

E-Mail Website
Guest Editor

College of Land Science and Technology, China Agricultural University, 17 Qinghua E Rd, Beijing 100083, China
Interests: urban remote sensing; vegetation phenology; urban heat island; urban growth modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Information on Land Use and Land Cover Change (LULCC) is critical for modeling human–earth systems, and remote sensing techniques have been established as the most cost-efficient and reliable approaches to gain this information. Over time the development of sensors has evolved from humble cameras carried by pigeons and air balloons to advanced spaceborne sensors currently in use, including optical, microwave, thermal, LiDAR, and Radar sensors. Although they provide rich and complementary information about the land surface from different perspectives, the integrated utilization of them requires expert knowledge, intensive computation, and storage capacity.

Google Earth Engine (GEE), a cloud-based remote sensing data processing platform, provides not only ready-to-use remote sensed datasets, freeing researchers from tedious data preprocessing tasks to focus on creative tasks, but also provides powerful computational capacity, facilitating LULCC monitoring with multi-temporal and multi-sensor data. GEE enables free programmatic access to imagery from various satellites (e.g., MODIS, Landsat, and Sentinel) as well as geospatial datasets (e.g., land-use data, climate, and weather data), through either a JavaScript or Python API. This Special Issue aims at studies that showcase the application of GEE to monitor LULCC, including land cover mapping, land change analysis, and thematic mapping. This includes, but is not limited to, topics, such as: forest change, urban expansion, mining impacts, coastal change, cropland, and specific crops (e.g., rice, maize), at both large-scales and long term.

Dr. Wu Xiao
Dr. Qiusheng Wu
Dr. Xuecao Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Google earth engine
Land use change
Change detection
Cloud computing for land cover/land use monitoring
Vegetation dynamics
Urban expansion and modeling
Mining exploitation impacts on land use
Coastal land use change detection
Land abandonment monitoring
Deep learning and machine learning

Related Special Issue

Remote Sensing of Land Use and Land Change with Google Earth Engine II in Remote Sensing

Published Papers (11 papers)

Download All Papers

Research

18 pages, 3306 KiB

Open AccessArticle

Mapping Maize Cropland and Land Cover in Semi-Arid Region in Northern Nigeria Using Machine Learning and Google Earth Engine

by Ghali Abdullahi Abubakar, Ke Wang, Auwalu Faisal Koko, Muhammad Ibrahim Husseini, Kamal Abdelrahim Mohamed Shuka, Jinsong Deng and Muye Gan

Remote Sens. 2023, 15(11), 2835; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15112835 - 30 May 2023

Cited by 5 | Viewed by 1951

Abstract

The monitoring of crop quantity and quality is vital for global food security. National food security has recently been at the forefront of local and regional research, and has become a vital priority for most developing countries. Therefore, ensuring reliable classification of cropland and other land cover is crucial for sustainable agricultural development and ensuring national food security. A good understanding of the Nigerian agricultural sector is essential to making better decisions and managing operations more efficiently. Scientists, practitioners, and policymakers must exchange reliable information to develop and support agricultural programs and policies. It is essential to develop and implement novel methods for mapping maize cropland and other land cover types. Thus, Seasonal Crop Inventory (SCI) is a valuable tool for farmers, researchers, and policymakers, as it provides critical information on crop production. It informs decisions related to land management, food security, and agricultural policy. In this study, Sentinel-1 and Sentinel-2 images have been combined to map maize cropland and other land covers in northern Nigeria during the 2016–2019 growing season. We employed a technologically advanced space-based remote sensing technique. As a pioneer study that obtained detailed information on northern Nigeria’s cropland, the research utilized platforms such as Google Earth Engine (GEE), a cloud-computing engine using various classification techniques that include Random Forest (RF), Support Vector Machine (SVM), and Classification Regression Trees (CART) algorithms to produce a pixel-based Seasonal Crop Inventory of the study area. The outcome demonstrated a reliable GEE-based mapping of the region’s cropland with satisfactory classification accuracy. It revealed the overall accuracy values and the Kappa coefficients to be above 97% during the different time nodes under study. It also indicated a 98% and 93% producer and user accuracy for the cropland. The research further revealed that the Random Forest performed the best among the three machine-learning models tested in this study for mapping the maize cropland and other land cover classes. Therefore, the study’s findings and the derived crop mapping would greatly help provide valuable information that helps farmers, policymakers, and other stakeholders make more informed decisions about agricultural production, land use planning, and resource management. Full article

(This article belongs to the Special Issue Remote Sensing of Land Use and Land Change with Google Earth Engine)

► Show Figures

Graphical abstract

22 pages, 5900 KiB

Open AccessArticle

Identification of Rubber Plantations in Southwestern China Based on Multi-Source Remote Sensing Data and Phenology Windows

by Guokun Chen, Zicheng Liu, Qingke Wen, Rui Tan, Yiwen Wang, Jingjing Zhao and Junxin Feng

Remote Sens. 2023, 15(5), 1228; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15051228 - 23 Feb 2023

Cited by 3 | Viewed by 1988

Abstract

The continuous transformation from biodiverse natural forests and mixed-use farms into monoculture rubber plantations may lead to a series of hazards, such as natural forest habitats fragmentation, biodiversity loss, as well as drought and water shortage. Therefore, understanding the spatial distribution of rubber plantations is crucial to regional ecological security and a sustainable economy. However, the spectral characteristics of rubber tree is easily mixed with other vegetation such as natural forests, tea plantations, orchards and shrubs, which brings difficulty and uncertainty to regional scale identification. In this paper, we proposed a classification method combines multi-source phenology characteristics and random forest algorithm. On the basis of optimization of input samples and features, phenological spectrum, brightness, greenness, wetness, fractional vegetation cover, topography and other features of rubber were extracted. Five classification schemes were constructed for comparison, and the one with the highest classification accuracy was used to identify the spatial pattern of rubber plantations in 2014, 2016, 2018 and 2020 in Xishuangbanna. The results show that: (1) the identification results are in consistent with field survey and rubber plantations area generally shows a first increasing and then decreasing trend; (2) the Overall Accuracy (OA) and Kappa coefficient of the proposed method are 90.0% and 0.86, respectively, with a Producer’s Accuracy (PA) and User’s Accuracy (UA) of 95.2% and 88.8%, respectively; (3) cross-validation was employed to analyze the accuracy evaluation indexes of the identification results: both PA and UA of the rubber plantations stay stable over 85%, with the minimum fluctuation and best stability of UA value. The OA value and Kappa coefficient were stable in the range of 0.88–0.90 and 0.84–0.86, respectively. The method proposed provides reliable results on spatial distribution of rubber, and is potentially transferable to other mountainous areas as a robust approach for rapid monitoring of rubber plantations. Full article

(This article belongs to the Special Issue Remote Sensing of Land Use and Land Change with Google Earth Engine)

► Show Figures

Figure 1

18 pages, 4797 KiB

Open AccessArticle

Mapping Cropland Abandonment in Mountainous Areas in China Using the Google Earth Engine Platform

by Suchen Xu, Wu Xiao, Chen Yu, Hang Chen and Yongzhong Tan

Remote Sens. 2023, 15(4), 1145; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15041145 - 20 Feb 2023

Cited by 5 | Viewed by 2524

Abstract

Knowledge about the spatial-temporal pattern of cropland abandonment is the premise for the management of abandoned croplands. Traditional mapping approaches of abandoned croplands usually utilize a multi-date classification-based land cover change trajectory. It requires quality training samples for land cover classification at each epoch, which is challenging in regions of smallholder agriculture in the absence of high-resolution imagery. Facing these challenges, a theoretical model is proposed to recognize abandoned croplands based on post-abandonment secondary succession. It applies the continuous change detection and classification (CCDC) temporal segmentation algorithm to Landsat time series (1986~2021) to obtain disjoint segments, representing croplands’ status. The post-abandonment secondary succession showing a greening trend is recognized using NDVI-based harmonic analysis, so as to capture its preceding abandonment. This algorithm is applied to a mountainous area in southwest China, where cropland abandonments are widespread. Validation based on stratified random samples referenced by a vegetation index time series and satellite images shows that the detected abandoned croplands have user accuracy, producer accuracy and an F1 score ranging from 43% to 71%, with variation among abandonment year. The study area has a potential cropland extent of 22,294 km², within which 9252 km² of the cropland was abandoned. The three peak years of abandonment were 1994, 2000, and 2011. The algorithm is suitable to be applied to large-scale mapping due to its automatic manner. Full article

(This article belongs to the Special Issue Remote Sensing of Land Use and Land Change with Google Earth Engine)

► Show Figures

Figure 1

22 pages, 6273 KiB

Open AccessArticle

Detecting Photovoltaic Installations in Diverse Landscapes Using Open Multi-Source Remote Sensing Data

by Jinyue Wang, Jing Liu and Longhui Li

Remote Sens. 2022, 14(24), 6296; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14246296 - 12 Dec 2022

Cited by 4 | Viewed by 2358

Abstract

Solar photovoltaic (PV) power generation is a vital renewable energy to achieve carbon neutrality. Previous studies which explored mapping PV using open satellite data mainly focus in remote areas. However, the complexity of land cover types can bring much difficulty in PV identification. This study investigated detecting PV in diverse landscapes using freely accessible remote sensing data, aiming to evaluate the transferability of PV detection between rural and urbanized coastal area. We developed a random forest-based PV classifier on Google Earth Engine in two provinces of China. Various features including Sentinel-2 reflectance, Sentinel-1 polarization, spectral indices and their corresponding textures were constructed. Thereafter, features with high permutation importance were retained. Three classification schemes with different training and test samples were, respectively, conducted. Finally, the VIIRS nighttime light data were utilized to refine the initial results. Manually collected samples and existing PV database were used to evaluate the accuracy of our method. The results revealed that the top three important features in detecting PV were the sum average texture of three bands (NDBI, VV, and VH). We found the classifier trained in highly urbanized coastal landscape with multiple PV types was more transferable (OA = 97.24%, kappa = 0.94), whereas the classifier trained in rural landscape with simple PV types was erroneous when applied vice versa (OA = 68.84%, kappa = 0.44). The highest accuracy was achieved when using training samples from both regions as expected (OA = 98.90%, kappa = 0.98). Our method recalled more than 94% PV in most existing databases. In particular, our method has a stronger detection ability of PV installed above water surface, which is often missing in existing PV databases. From this study, we found two main types of errors in mapping PV, including the bare rocks and mountain shadows in natural landscapes and the roofing polyethylene materials in urban settlements. In conclusion, the PV classifier trained in highly urbanized coastal landscapes with multiple PV types is more accurate than the classifier trained in rural landscapes. The VIIRS nighttime light data contribute greatly to remove PV detection errors caused by bare rocks and mountain shadows. The finding in our study can provide reference values for future large area PV monitoring. Full article

(This article belongs to the Special Issue Remote Sensing of Land Use and Land Change with Google Earth Engine)

► Show Figures

Graphical abstract

22 pages, 6208 KiB

Open AccessArticle

Google Earth Engine for Informal Settlement Mapping: A Random Forest Classification Using Spectral and Textural Information

by Dadirai Matarira, Onisimo Mutanga and Maheshvari Naidu

Remote Sens. 2022, 14(20), 5130; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14205130 - 14 Oct 2022

Cited by 13 | Viewed by 4277

Abstract

Accurate and reliable informal settlement maps are fundamental decision-making tools for planning, and for expediting informed management of cities. However, extraction of spatial information for informal settlements has remained a mammoth task due to the spatial heterogeneity of urban landscape components, requiring complex analytical processes. To date, the use of Google Earth Engine platform (GEE), with cloud computing prowess, provides unique opportunities to map informal settlements with precision and enhanced accuracy. This paper leverages cloud-based computing techniques within GEE to integrate spectral and textural features for accurate extraction of the location and spatial extent of informal settlements in Durban, South Africa. The paper aims to investigate the potential and advantages of GEE’s innovative image processing techniques to precisely depict morphologically varied informal settlements. Seven data input models derived from Sentinel 2A bands, band-derived texture metrics, and spectral indices were investigated through a random forest supervised protocol. The main objective was to explore the value of different data input combinations in accurately mapping informal settlements. The results revealed that the classification based on spectral bands + textural information yielded the highest informal settlement identification accuracy (94% F-score). The addition of spectral indices decreased mapping accuracy. Our results confirm that the highest spatial accuracy is achieved with the ‘textural features’ model, which yielded the lowest root-mean-square log error (0.51) and mean absolute percent error (0.36). Our approach highlights the capability of GEE’s complex integrative data processing capabilities in extracting morphological variations of informal settlements in rugged and heterogeneous urban landscapes, with reliable accuracy. Full article

(This article belongs to the Special Issue Remote Sensing of Land Use and Land Change with Google Earth Engine)

► Show Figures

Figure 1

18 pages, 4737 KiB

Open AccessArticle

On the Use of Sentinel-2 NDVI Time Series and Google Earth Engine to Detect Land-Use/Land-Cover Changes in Fire-Affected Areas

by Rosa Lasaponara, Nicodemo Abate, Carmen Fattore, Angelo Aromando, Gianfranco Cardettini and Marco Di Fonzo

Remote Sens. 2022, 14(19), 4723; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14194723 - 21 Sep 2022

Cited by 11 | Viewed by 5068

Abstract

This study aims to assess the potential of Sentinel-2 NDVI time series and Google Earth Engine to detect small land-use/land-cover changes (at the pixel level) in fire-disturbed environs. To capture both slow and fast changes, the investigations focused on the analysis of trends in NDVI time series, selected because they are extensively used for the assessment of post-fire dynamics mainly linked to the monitoring of vegetation recovery and fire resilience. The area considered for this study is the central–southern part of the Italian peninsula, in particular the regions of (i) Campania, (ii) Basilicata, (iii) Calabria, (iv) Toscana, (v) Umbria, and (vi) Lazio. For each fire considered, the study covered the period from the year after the event to the present. The multi-temporal analysis was performed using two main data processing steps (i) linear regression to extract NDVI trends and enhance changes over time and (ii) random forest classification to capture and categorize the various changes. The analysis allowed us to identify changes occurred in the selected case study areas and to understand and evaluate the trend indicators that mark a change in land use/land cover. In particular, different types of changes were identified: (i) woodland felling, (ii) remaking of paths and roads, and (ii) transition from wooded area to cultivated field. The reliability of the changes identified was assessed and confirmed by the high multi-temporal resolution offered by Google Earth. Results of this comparison highlighted that the overall accuracy of the classification was higher than 0.86. Full article

(This article belongs to the Special Issue Remote Sensing of Land Use and Land Change with Google Earth Engine)

► Show Figures

Figure 1

19 pages, 4867 KiB

Open AccessArticle

Global 10 m Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover

by Zander S. Venter, David N. Barton, Tirthankar Chakraborty, Trond Simensen and Geethen Singh

Remote Sens. 2022, 14(16), 4101; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14164101 - 21 Aug 2022

Cited by 67 | Viewed by 18909

Abstract

The European Space Agency’s Sentinel satellites have laid the foundation for global land use land cover (LULC) mapping with unprecedented detail at 10 m resolution. We present a cross-comparison and accuracy assessment of Google’s Dynamic World (DW), ESA’s World Cover (WC) and Esri’s Land Cover (Esri) products for the first time in order to inform the adoption and application of these maps going forward. For the year 2020, the three global LULC maps show strong spatial correspondence (i.e., near-equal area estimates) for water, built area, trees and crop LULC classes. However, relative to one another, WC is biased towards over-estimating grass cover, Esri towards shrub and scrub cover and DW towards snow and ice. Using global ground truth data with a minimum mapping unit of 250 m², we found that Esri had the highest overall accuracy (75%) compared to DW (72%) and WC (65%). Across all global maps, water was the most accurately mapped class (92%), followed by built area (83%), tree cover (81%) and crops (78%), particularly in biomes characterized by temperate and boreal forests. The classes with the lowest accuracies, particularly in the tundra biome, included shrub and scrub (47%), grass (34%), bare ground (57%) and flooded vegetation (53%). When using European ground truth data from LUCAS (Land Use/Cover Area Frame Survey) with a minimum mapping unit of <100 m², we found that WC had the highest accuracy (71%) compared to DW (66%) and Esri (63%), highlighting the ability of WC to resolve landscape elements with more detail compared to DW and Esri. Although not analyzed in our study, we discuss the relative advantages of DW due to its frequent and near real-time data delivery of both categorical predictions and class probability scores. We recommend that the use of global LULC products should involve critical evaluation of their suitability with respect to the application purpose, such as aggregate changes in ecosystem accounting versus site-specific change detection in monitoring, considering trade-offs between thematic resolution, global versus. local accuracy, class-specific biases and whether change analysis is necessary. We also emphasize the importance of not estimating areas from pixel-counting alone but adopting best practices in design-based inference and area estimation that quantify uncertainty for a given study area. Full article

(This article belongs to the Special Issue Remote Sensing of Land Use and Land Change with Google Earth Engine)

► Show Figures

Figure 1

19 pages, 2280 KiB

Open AccessArticle

PlanetScope, Sentinel-2, and Sentinel-1 Data Integration for Object-Based Land Cover Classification in Google Earth Engine

by Marco Vizzari

Remote Sens. 2022, 14(11), 2628; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14112628 - 31 May 2022

Cited by 29 | Viewed by 10228

Abstract

PlanetScope (PL) high-resolution composite base maps have recently become available within Google Earth Engine (GEE) for the tropical regions thanks to the partnership between Google and the Norway’s International Climate and Forest Initiative (NICFI). Object-based (OB) image classification in the GEE environment has increased rapidly due to the broadly recognized advantages of applying these approaches to medium- and high-resolution images. This work aimed to assess the advantages for land cover classification of (a) adopting an OB approach with PL data; and (b) integrating the PL datasets with Sentinel 2 and Sentinel 1 data both in Pixel-based (PB) or OB approaches. For this purpose, in this research, we compared ten LULC classification approaches (PB and OB, all based on the Random Forest (RF) algorithm), where the three satellite datasets were used according to different levels of integration and combination. The study area, which is 69,272 km² wide and located in central Brazil, was selected within the tropical region, considering a preliminary availability of sample points and its complex landscape mosaic composed of heterogeneous agri-natural spaces, including scattered settlements. Using only the PL dataset with a typical RF PB approach produced the worse overall accuracy (OA) results (67%), whereas adopting an OB approach for the same dataset yielded very good OA (82%). The integration of PL data with the S2 and S1 datasets improved both PB and OB overall accuracy outputs (82 vs. 67% and 91 vs. 82%, respectively). Moreover, this research demonstrated the OB approaches’ applicability in GEE, even in vast study areas and using high-resolution imagery. Although additional applications are necessary, the proposed methodology appears to be very promising for properly exploiting the potential of PL data in GEE. Full article

(This article belongs to the Special Issue Remote Sensing of Land Use and Land Change with Google Earth Engine)

► Show Figures

Graphical abstract

24 pages, 12119 KiB

Open AccessArticle

Human Disturbance on the Land Surface Environment in Tropical Islands: A Remote Sensing Perspective

by Tianmeng Fu, Li Zhang, Bowei Chen and Min Yan

Remote Sens. 2022, 14(9), 2100; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14092100 - 27 Apr 2022

Cited by 3 | Viewed by 2033

Abstract

Geographically isolated islands are under great stress due to global climate change, as well as the social and environmental issues relating to human activities. It is necessary to monitor and analyze the spatial–temporal changes of the land surface environment in species-rich tropical islands in order to realize the sustainable development and protection of island areas. In this study, we extracted the land cover and coastline information of three tropical islands from 1990 to 2020 based on the Google Earth Engine platform and the Random Forest algorithm. The results showed that: (1) different tropical islands have similar characteristics in terms of land surface environment changes, with the amount of artificial surface and cultivated land increasing, the forest and mangrove areas decreasing, and the amount of artificial coastline increasing; (2) human disturbance plays an important role in changes in the land surface environment. Population growth, immigration policies, food security, and human activities related to achieving economic profits are likely responsible for these land cover changes; and (3) the main factors driving coastline changes include natural processes (topography, ecological ecosystems, sea-level rise, sea waves, and storms) and human activities (sand mining, tourism, port construction, aquaculture expansion, and mangrove deforestation). Understanding these changes will help tropical islands and coastal zones to make suitable policies for land management and respond to climate change and sustainable development challenges. Full article

(This article belongs to the Special Issue Remote Sensing of Land Use and Land Change with Google Earth Engine)

► Show Figures

Graphical abstract

18 pages, 2976 KiB

Open AccessArticle

Land Use and Land Cover Mapping Using Sentinel-2, Landsat-8 Satellite Images, and Google Earth Engine: A Comparison of Two Composition Methods

by Vahid Nasiri, Azade Deljouei, Fardin Moradi, Seyed Mohammad Moein Sadeghi and Stelian Alexandru Borz

Remote Sens. 2022, 14(9), 1977; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14091977 - 20 Apr 2022

Cited by 58 | Viewed by 12196

Abstract

Accurate and real-time land use/land cover (LULC) maps are important to provide precise information for dynamic monitoring, planning, and management of the Earth. With the advent of cloud computing platforms, time series feature extraction techniques, and machine learning classifiers, new opportunities are arising in more accurate and large-scale LULC mapping. In this study, we aimed at finding out how two composition methods and spectral–temporal metrics extracted from satellite time series can affect the ability of a machine learning classifier to produce accurate LULC maps. We used the Google Earth Engine (GEE) cloud computing platform to create cloud-free Sentinel-2 (S-2) and Landsat-8 (L-8) time series over the Tehran Province (Iran) as of 2020. Two composition methods, namely, seasonal composites and percentiles metrics, were used to define four datasets based on satellite time series, vegetation indices, and topographic layers. The random forest classifier was used in LULC classification and for identifying the most important variables. Accuracy assessment results showed that the S-2 outperformed the L-8 spectral–temporal metrics at the overall and class level. Moreover, the comparison of composition methods indicated that seasonal composites outperformed percentile metrics in both S-2 and L-8 time series. At the class level, the improved performance of seasonal composites was related to their ability to provide better information about the phenological variation of different LULC classes. Finally, we conclude that this methodology can produce LULC maps based on cloud computing GEE in an accurate and fast way and can be used in large-scale LULC mapping. Full article

(This article belongs to the Special Issue Remote Sensing of Land Use and Land Change with Google Earth Engine)

► Show Figures

Graphical abstract

15 pages, 4647 KiB

Open AccessArticle

Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform

by Masoumeh Aghababaei, Ataollah Ebrahimi, Ali Asghar Naghipour, Esmaeil Asadi and Jochem Verrelst

Remote Sens. 2021, 13(22), 4683; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13224683 - 19 Nov 2021

Cited by 9 | Viewed by 4029

Abstract

Vegetation Types (VTs) are important managerial units, and their identification serves as essential tools for the conservation of land covers. Despite a long history of Earth observation applications to assess and monitor land covers, the quantitative detection of sparse VTs remains problematic, especially in arid and semiarid areas. This research aimed to identify appropriate multi-temporal datasets to improve the accuracy of VTs classification in a heterogeneous landscape in Central Zagros, Iran. To do so, first the Normalized Difference Vegetation Index (NDVI) temporal profile of each VT was identified in the study area for the period of 2018, 2019, and 2020. This data revealed strong seasonal phenological patterns and key periods of VTs separation. It led us to select the optimal time series images to be used in the VTs classification. We then compared single-date and multi-temporal datasets of Landsat 8 images within the Google Earth Engine (GEE) platform as the input to the Random Forest classifier for VTs detection. The single-date classification gave a median Overall Kappa (OK) and Overall Accuracy (OA) of 51% and 64%, respectively. Instead, using multi-temporal images led to an overall kappa accuracy of 74% and an overall accuracy of 81%. Thus, the exploitation of multi-temporal datasets favored accurate VTs classification. In addition, the presented results underline that available open access cloud-computing platforms such as the GEE facilitates identifying optimal periods and multitemporal imagery for VTs classification. Full article

(This article belongs to the Special Issue Remote Sensing of Land Use and Land Change with Google Earth Engine)

► Show Figures