scholarly journals Multi-Temporal Land Cover Change Mapping Using Google Earth Engine and Ensemble Learning Methods

2020 ◽  
Vol 10 (22) ◽  
pp. 8083 ◽  
Author(s):  
Nimisha Wagle ◽  
Tri Dev Acharya ◽  
Venkatesh Kolluru ◽  
He Huang ◽  
Dong Ha Lee
Keyword(s):  
Land Cover ◽  
Ensemble Learning ◽  
Vegetation Index ◽  
Confusion Matrix ◽  
Google Earth ◽  
Gradient Boosting ◽  
Observation Data ◽  
Learning Methods ◽  
Extreme Gradient Boosting ◽  
Google Earth Engine

The study deals with the application of Google Earth Engine (GEE), Landsat data and ensemble-learning methods (ELMs) to map land cover (LC) change over a decade in the Kaski district of Nepal. As Nepal has experienced extensive changes due to natural and anthropogenic activities, monitoring such changes are crucial for understanding relationships and interactions between social and natural phenomena and to promote better decision-making. The main novelty lies in applying the XGBoost classifier for LC mapping over Nepal and monitoring the decadal changes of LC using ELMs. To map the LC change, a yearly cloud-free composite Landsat image was selected for the year 2010 and 2020. Combining the annual normalized difference vegetation index, normalized difference built-up index and modified normalized difference water index, with elevation and slope data from shuttle radar topography mission, supervised classification was performed using a random forest and extreme gradient boosting ELMs. Post classification change detection, validation and accuracy assessment were executed after the preparation of the LC maps. Three evaluation indices, namely overall accuracy (OA), Kappa coefficient, and F1 score from confusion matrix reports, were calculated for all the points used for validation purposes. We have obtained an OA of 0.8792 and 0.875 for RF and 0.8926 and 0.8603 for XGBoost at the 95% confidence level for 2010 and 2020 LC maps, which are better for mountainous terrain. The applied methodology could be significant in utilizing the big earth observation data and overcoming the traditional computational challenges using GEE. In addition, the quantification of changes over time would be helpful for decision-makers to understand current environmental dynamics in the study area.

scholarly journals Automatic Land-Cover Mapping using Landsat Time-Series Data based on Google Earth Engine

2019 ◽  
Vol 11 (24) ◽  
pp. 3023 ◽  
Author(s):  
Shuai Xie ◽  
Liangyun Liu ◽  
Xiao Zhang ◽  
Jiangning Yang ◽  
Xidong Chen ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Vegetation Index ◽  
Time Series Data ◽  
Land Cover Classification ◽  
Google Earth ◽  
Thematic Mapper ◽  
Series Data ◽  
Land Cover Mapping ◽  
Google Earth Engine

The Google Earth Engine (GEE) has emerged as an essential cloud-based platform for land-cover classification as it provides massive amounts of multi-source satellite data and high-performance computation service. This paper proposed an automatic land-cover classification method using time-series Landsat data on the GEE cloud-based platform. The Moderate Resolution Imaging Spectroradiometer (MODIS) land-cover products (MCD12Q1.006) with the International Geosphere–Biosphere Program (IGBP) classification scheme were used to provide accurate training samples using the rules of pixel filtering and spectral filtering, which resulted in an overall accuracy (OA) of 99.2%. Two types of spectral–temporal features (percentile composited features and median composited monthly features) generated from all available Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data from the year 2010 ± 1 were used as input features to a Random Forest (RF) classifier for land-cover classification. The results showed that the monthly features outperformed the percentile features, giving an average OA of 80% against 77%. In addition, the monthly features composited using the median outperformed those composited using the maximum Normalized Difference Vegetation Index (NDVI) with an average OA of 80% against 78%. Therefore, the proposed method is able to generate accurate land-cover mapping automatically based on the GEE cloud-based platform, which is promising for regional and global land-cover mapping.

scholarly journals Mapping and Assessing the Dynamics of Shifting Agricultural Landscapes Using Google Earth Engine Cloud Computing, a Case Study in Mozambique

2020 ◽  
Vol 12 (8) ◽  
pp. 1279 ◽  
Author(s):  
Sosdito Mananze ◽  
Isabel Pôças ◽  
Mário Cunha
Keyword(s):  
Cloud Computing ◽  
Land Cover ◽  
Google Earth ◽  
Agricultural Landscapes ◽  
Landsat 8 ◽  
Textural Features ◽  
Observation Data ◽  
Ground Truth Data ◽  
Spectral Bands ◽  
Google Earth Engine

Land cover maps obtained at high spatial and temporal resolutions are necessary to support monitoring and management applications in areas with many smallholder and low-input agricultural systems, as those characteristic in Mozambique. Various regional and global land cover products based on Earth Observation data have been developed and made publicly available but their application in regions characterized by a large variety of agro-systems with a dynamic nature is limited by several constraints. Challenges in the classification of spatially heterogeneous landscapes, as in Mozambique, include the definition of the adequate spatial resolution and data input combinations for accurately mapping land cover. Therefore, several combinations of variables were tested for their suitability as input for random forest ensemble classifier aimed at mapping the spatial dynamics of smallholder agricultural landscape in Vilankulo district in Mozambique. The variables comprised spectral bands from Landsat 7 ETM+ and Landsat 8 OLI/TIRS, vegetation indices and textural features and the classification was performed within the Google Earth Engine cloud computing for the years 2012, 2015, and 2018. The study of three different years aimed at evaluating the temporal dynamics of the landscape, typically characterized by high shifting nature. For the three years, the best performing variables included three selected spectral bands and textural features extracted using a window size of 25. The classification overall accuracy was 0.94 for the year 2012, 0.98 for 2015, and 0.89 for 2018, suggesting that the produced maps are reliable. In addition, the areal statistics of the class classified as agriculture were very similar to the ground truth data as reported by the Serviços Distritais de Actividades Económicas (SDAE), with an average percentage deviation below 10%. When comparing the three years studied, the natural vegetation classes are the predominant covers while the agriculture is the most important cause of land cover changes.

scholarly journals ANÁLISE DO USO DA TERRA EM UMA SECÇÃO ÀS MARGENS DO RIO TOCANTINS AUXILIADA POR ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA – NDVI

2019 ◽  
Vol 5 (18) ◽  
pp. 9379
Author(s):  
Rodrigo Lima Santos ◽  
Fabrizia Gioppo Nunes
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Vegetation Index ◽  
Google Earth ◽  
Fish Ponds ◽  
Google Earth Engine ◽  
Tocantins River ◽  
Para Determinar ◽  
Del Rio

LAND USE ANALISYS IN A SECTION OF TOCANTINS’S RIVER MARGINAL STRIP SUPPORTED BY NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI)ANÁLISIS DEL USO DE LA TIERRA EN UNA SECCIÓN LOS MARGENES DEL RÍO TOCANTINS AUXILIADOS POR ÍNDICE DE VEGETACIÓN POR DIFERENCIA NORMALIZADA – NDVIRESUMOO mapeamento de uso e cobertura da terra é um instrumento indispensável para uma boa gestão do ambiente em geral, podendo obedecer diferentes recortes espaciais. A adoção de alternativas para aperfeiçoar esse produto, tais como a leitura de dados anuais de Índices de Vegetação torna-o mais efetivo e capaz de oferecer respostas a determinadas questões. Nesta perspectiva, o presente estudo tem como objetivo o mapeamento e reconhecimento de áreas degradadas e de áreas preservadas, em uma secção delimitada as margens do Rio Tocantins, auxiliados por séries temporais de NDVI. A metodologia incluiu o mapeamento de uso e cobertura da terra no ano de 2015; delimitação da Área de Proteção Ambiental (APP) e; a utilização de séries anuais de NDVI, disponibilizadas pela plataforma online do Google Earth Engine. A ferramenta de NDVI é apresentada como uma alternativa a avaliação da conversão de coberturas naturais para diferentes tipologias de uso da terra. Como exemplos, são retratados três pontos de conversões de uso: solo exposto para vegetação regenerada; vegetação natural para tanques de pisciculturas e; solo exposto intercalado à vegetação rasteira para área urbanizada. Os resultados apontam que a APP analisada se encontra em estado de alerta, uma vez que sua conversão em áreas degradadas ultrapassa cerca de 50%, e a ferramenta de NDVI foi essencial para determinar quando ocorreram essas modificações em distintas classes de uso.Palavras-chave: Uso da Terra; Séries Temporais; Rio Tocantins; Imperatriz-MA.ABSTRACTThe mapping of land use and land cover is an indispensable tool for good management of the environment in general, and can obey different spatial cutouts. Adopting alternatives to improve this product, such as reading annual Vegetation Index data makes it more effective and able to provide answers to certain questions. In this perspective, the present study aims to map and recognize degraded areas and preserved areas, in a section delimited the banks of the Tocantins River, aided by NDVI time series. The methodology included land use and land cover mapping in 2015; delimitation of the Environmental Protection Area (APP) and; use of annual NDVI series made available through the Google Earth Engine online platform. The NDVI tool is presented as an alternative to evaluate the conversion of natural coverages for different land use typologies. As examples, three points of use conversions are depicted: exposed soil for regenerated vegetation; natural vegetation for fish ponds and; exposed soil interspersed with undergrowth to urbanized area. The results indicate that the analyzed APP is in a state of alert, since its conversion to degraded areas exceeds about 50%, and the NDVI tool was essential to determine when these changes occurred in different classes of use.Keywords: Land Use; Time Series; Tocantins River; Imperatriz-MA.RESUMENEl mapeo del uso de la tierra y la cobertura de la tierra es una herramienta indispensable para la buena gestión del medio ambiente en general, y puede obedecer a diferentes recortes espaciales. Adoptar alternativas para mejorar este producto, como leer los datos anuales del Índice de Vegetación, lo hace más efectivo y capaz de proporcionar respuestas a ciertas preguntas. En esta perspectiva, este estudio apunta a mapear y reconocer áreas degradadas y áreas preservadas, en una sección delimitada a orillas del río Tocantins, ayudado por series de tiempo NDVI. La metodología incluyó el uso del suelo y el mapeo de la cobertura del suelo en 2015; delimitación del Área de Protección Ambiental (APP) y; uso de la serie anual NDVI disponible a través de la plataforma en línea Google Earth Engine. La herramienta NDVI se presenta como una alternativa para evaluar la conversión de coberturas naturales para diferentes tipologías de uso de la tierra. Como ejemplos, se representan tres conversiones de puntos de uso: suelo expuesto para vegetación regenerada; vegetación natural para estanques de peces y; suelo expuesto intercalado con maleza en el área urbanizada. Los resultados indican que la aplicación analizada se encuentra en estado de alerta, ya que su conversión a áreas degradadas supera aproximadamente el 50%, y la herramienta NDVI fue esencial para determinar cuándo ocurrieron estos cambios en diferentes clases de uso.Palabras clave: Uso de la Tierra; Series Temporales; Río Tocantins; Imperatriz-MA.

scholarly journals A COMPARISON OF TREE-BASED ALGORITHMS FOR COMPLEX WETLAND CLASSIFICATION USING THE GOOGLE EARTH ENGINE

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 313-319
Author(s):  
A. Jamali ◽  
M. Mahdianpari ◽  
İ. R. Karaş
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Well Being ◽  
Google Earth ◽  
The Other ◽  
Gradient Boosting ◽  
Wetland Classification ◽  
Observation Methods ◽  
Extreme Gradient Boosting ◽  
Google Earth Engine

Abstract. Wetlands are endangered ecosystems that are required to be systematically monitored. Wetlands have significant contributions to the well-being of human-being, fauna, and fungi. They provide vital services, including water storage, carbon sequestration, food security, and protecting the shorelines from floods. Remote sensing is preferred over the other conventional earth observation methods such as field surveying. It provides the necessary tools for the systematic and standardized method of large-scale wetland mapping. On the other hand, new cloud computing technologies for the storage and processing of large-scale remote sensing big data such as the Google Earth Engine (GEE) have emerged. As such, for the complex wetland classification in the pilot site of the Avalon, Newfoundland, Canada, we compare the results of three tree-based classifiers of the Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGB) available in the GEE code editor using Sentinel-2 images. Based on the results, the XGB classifier with an overall accuracy of 82.58% outperformed the RF (82.52%) and DT (77.62%) classifiers.

scholarly journals Predicting WNV Circulation in Italy Using Earth Observation Data and Extreme Gradient Boosting Model

2020 ◽  
Vol 12 (18) ◽  
pp. 3064
Author(s):  
Luca Candeloro ◽  
Carla Ippoliti ◽  
Federica Iapaolo ◽  
Federica Monaco ◽  
Daniela Morelli ◽  
...  
Keyword(s):  
At Risk ◽  
Environmental Conditions ◽  
Land Surface ◽  
Vegetation Index ◽  
Earth Observation ◽  
Gradient Boosting ◽  
Observation Data ◽  
West Nile ◽  
Extreme Gradient Boosting ◽  
Earth Observation Data

West Nile Disease (WND) is one of the most spread zoonosis in Italy and Europe caused by a vector-borne virus. Its transmission cycle is well understood, with birds acting as the primary hosts and mosquito vectors transmitting the virus to other birds, while humans and horses are occasional dead-end hosts. Identifying suitable environmental conditions across large areas containing multiple species of potential hosts and vectors can be difficult. The recent and massive availability of Earth Observation data and the continuous development of innovative Machine Learning methods can contribute to automatically identify patterns in big datasets and to make highly accurate identification of areas at risk. In this paper, we investigated the West Nile Virus (WNV) circulation in relation to Land Surface Temperature, Normalized Difference Vegetation Index and Surface Soil Moisture collected during the 160 days before the infection took place, with the aim of evaluating the predictive capacity of lagged remotely sensed variables in the identification of areas at risk for WNV circulation. WNV detection in mosquitoes, birds and horses in 2017, 2018 and 2019, has been collected from the National Information System for Animal Disease Notification. An Extreme Gradient Boosting model was trained with data from 2017 and 2018 and tested for the 2019 epidemic, predicting the spatio-temporal WNV circulation two weeks in advance with an overall accuracy of 0.84. This work lays the basis for a future early warning system that could alert public authorities when climatic and environmental conditions become favourable to the onset and spread of WNV.

scholarly journals Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park

2021 ◽  
Vol 13 (12) ◽  
pp. 2299
Author(s):  
Andrea Tassi ◽  
Daniela Gigante ◽  
Giuseppe Modica ◽  
Luciano Di Martino ◽  
Marco Vizzari
Keyword(s):  
Land Cover ◽  
National Park ◽  
Time Window ◽  
Central Italy ◽  
Google Earth ◽  
Data Cube ◽  
Landsat 8 ◽  
Change Analysis ◽  
Object Based ◽  
Google Earth Engine

With the general objective of producing a 2018–2020 Land Use/Land Cover (LULC) map of the Maiella National Park (central Italy), useful for a future long-term LULC change analysis, this research aimed to develop a Landsat 8 (L8) data composition and classification process using Google Earth Engine (GEE). In this process, we compared two pixel-based (PB) and two object-based (OB) approaches, assessing the advantages of integrating the textural information in the PB approach. Moreover, we tested the possibility of using the L8 panchromatic band to improve the segmentation step and the object’s textural analysis of the OB approach and produce a 15-m resolution LULC map. After selecting the best time window of the year to compose the base data cube, we applied a cloud-filtering and a topography-correction process on the 32 available L8 surface reflectance images. On this basis, we calculated five spectral indices, some of them on an interannual basis, to account for vegetation seasonality. We added an elevation, an aspect, a slope layer, and the 2018 CORINE Land Cover classification layer to improve the available information. We applied the Gray-Level Co-Occurrence Matrix (GLCM) algorithm to calculate the image’s textural information and, in the OB approaches, the Simple Non-Iterative Clustering (SNIC) algorithm for the image segmentation step. We performed an initial RF optimization process finding the optimal number of decision trees through out-of-bag error analysis. We randomly distributed 1200 ground truth points and used 70% to train the RF classifier and 30% for the validation phase. This subdivision was randomly and recursively redefined to evaluate the performance of the tested approaches more robustly. The OB approaches performed better than the PB ones when using the 15 m L8 panchromatic band, while the addition of textural information did not improve the PB approach. Using the panchromatic band within an OB approach, we produced a detailed, 15-m resolution LULC map of the study area.

scholarly journals Qualifying Land Use and Land Cover Dynamics and Their Impacts on Ecosystem Service in Central Himalaya Transboundary Landscape Based on Google Earth Engine

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 173
Author(s):  
Changjun Gu ◽  
Yili Zhang ◽  
Linshan Liu ◽  
Lanhui Li ◽  
Shicheng Li ◽  
...  
Keyword(s):  
Land Use ◽  
Ecosystem Services ◽  
Land Cover ◽  
Ecosystem Service ◽  
Forest Cover ◽  
Google Earth ◽  
Forest Cover Change ◽  
Sacred Landscape ◽  
Lulc Changes ◽  
Google Earth Engine

Land use and land cover (LULC) changes are regarded as one of the key drivers of ecosystem services degradation, especially in mountain regions where they may provide various ecosystem services to local livelihoods and surrounding areas. Additionally, ecosystems and habitats extend across political boundaries, causing more difficulties for ecosystem conservation. LULC in the Kailash Sacred Landscape (KSL) has undergone obvious changes over the past four decades; however, the spatiotemporal changes of the LULC across the whole of the KSL are still unclear, as well as the effects of LULC changes on ecosystem service values (ESVs). Thus, in this study we analyzed LULC changes across the whole of the KSL between 2000 and 2015 using Google Earth Engine (GEE) and quantified their impacts on ESVs. The greatest loss in LULC was found in forest cover, which decreased from 5443.20 km2 in 2000 to 5003.37 km2 in 2015 and which mainly occurred in KSL-Nepal. Meanwhile, the largest growth was observed in grassland (increased by 548.46 km2), followed by cropland (increased by 346.90 km2), both of which mainly occurred in KSL-Nepal. Further analysis showed that the expansions of cropland were the major drivers of the forest cover change in the KSL. Furthermore, the conversion of cropland to shrub land indicated that farmland abandonment existed in the KSL during the study period. The observed forest degradation directly influenced the ESV changes in the KSL. The total ESVs in the KSL decreased from 36.53 × 108 USD y−1 in 2000 to 35.35 × 108 USD y−1 in 2015. Meanwhile, the ESVs of the forestry areas decreased by 1.34 × 108 USD y−1. This shows that the decrease of ESVs in forestry was the primary cause to the loss of total ESVs and also of the high elasticity. Our findings show that even small changes to the LULC, especially in forestry areas, are noteworthy as they could induce a strong ESV response.

scholarly journals Land Cover Classification Based on Cloud Computing Platform

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Nghia Viet Nguyen ◽  
Thu Hoai Thi Trinh ◽  
Hoa Thi Pham ◽  
Trang Thu Thi Tran ◽  
Lan Thi Pham ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Land Cover ◽  
Spatial Data ◽  
Google Earth ◽  
Computer Hardware ◽  
Commercial Software ◽  
Computing Platform ◽  
Land Cover Data ◽  
Cloud Computing Platform ◽  
Google Earth Engine

Land cover is a critical factor for climate change and hydrological models. The extraction of land cover data from remote sensing images has been carried out by specialized commercial software. However, the limitations of computer hardware and algorithms of the commercial software are costly and make it take a lot of time, patience, and skills to do the classification. The cloud computing platform Google Earth Engine brought a breakthrough in 2010 for analyzing and processing spatial data. This study applied Object-based Random Forest classification in the Google Earth Engine platform to produce land cover data in 2010 in the Vu Gia - Thu Bon river basin. The classification results showed 7 categories of land cover consisting of plantation forest, natural forest, paddy field, urban residence, rural residence, bare land, and water surface, with an overall accuracy of 73.9% and kappa of 0.70.

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

2021 ◽  
Vol 13 (22) ◽  
pp. 4683
Author(s):  
Masoumeh Aghababaei ◽  
Ataollah Ebrahimi ◽  
Ali Asghar Naghipour ◽  
Esmaeil Asadi ◽  
Jochem Verrelst
Keyword(s):  
Vegetation Index ◽  
Google Earth ◽  
Optimal Time ◽  
Quantitative Detection ◽  
Landsat 8 ◽  
Vegetation Types ◽  
Landsat Images ◽  
Multi Temporal ◽  
Google Earth Engine ◽  
Land Covers

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.

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