scholarly journals Thirty-Year Dynamics of LULC at the Dong Thap Muoi Area, Southern Vietnam, Using Google Earth Engine

2021 ◽  
Vol 10 (4) ◽  
pp. 226
Author(s):  
Nguyen An Binh ◽  
Huynh Song Nhut ◽  
Nguyen Ngoc An ◽  
Tran Anh Phuong ◽  
Nguyen Cao Hanh ◽  
...  
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Google Earth ◽  
Ancillary Data ◽  
Landsat Images ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Agricultural Structure ◽  
Unused Land ◽  
Google Earth Engine

The main purpose of this paper is to assess the land use and land cover (LULC) changes for thirty years, from 1990–2020, in the Dong Thap Muoi, a flooded land area of the Mekong River Delta of Vietnam using Google Earth Engine and random forest algorithm. The specific purposes are: (1) determine the main LULC classes and (2) compute and analyze the magnitude and rate of changes for these LULC classes. For the above purposes, 128 Landsat images, topographic maps, land use status maps, cadastral maps, and ancillary data were collected and utilized to derive the LULC maps using the random forest classification algorithm. The overall accuracy of the LULC maps for 1990, 2000, 2010, and 2020 are 88.9, 83.5, 87.1, and 85.6%, respectively. The result showed that the unused land was dominant in 1990 with 28.9 % of the total area, but it was primarily converted to the paddy, a new dominant LULC class in 2020 (45.1%). The forest was reduced significantly from 14.4% in 1990 to only 5.5% of the total area in 2020. Whereas at the same time, the built-up increased from 0.3% to 6.2% of the total area. This research may help the authorities design exploitation policies for the Dong Thap Muoi’s socio-economic development and develop a new, stable, and sustainable ecosystem, promoting the advantages of the region, early forming a diversified agricultural structure.

scholarly journals Mapping National Mangrove Cover for Belize Using Google Earth Engine and Sentinel-2 Imagery

2021 ◽  
Vol 11 (9) ◽  
pp. 4258
Author(s):  
Jordan R. Cissell ◽  
Steven W. J. Canty ◽  
Michael K. Steinberg ◽  
Loraé T. Simpson
Keyword(s):  
Google Earth ◽  
Mangrove Forests ◽  
Random Forest Classification ◽  
High Resolution Mapping ◽  
Mangrove Ecosystems ◽  
Forest Classification ◽  
Resolution Mapping ◽  
Google Earth Engine ◽  
Terrestrial Biodiversity ◽  
Sentinel 2

In this paper, we present the highest-resolution-available (10 m) national map of the mangrove ecosystems of Belize. These important ecosystems are increasingly threatened by human activities and climate change, support both marine and terrestrial biodiversity, and provide critical ecosystem services to coastal communities in Belize and throughout the Mesoamerican Reef ecoregion. Previous national- and international-level inventories document Belizean mangrove forests at spatial resolutions of 30 m or coarser, but many mangrove patches and loss events may be too small to be accurately mapped at these resolutions. Our 10 m map addresses this need for a finer-scale national mangrove inventory. We mapped mangrove ecosystems in Belize as of 2020 by performing a random forest classification of Sentinel-2 Multispectral Instrument imagery in Google Earth Engine. We mapped a total mangrove area of 578.54 km2 in 2020, with 372.04 km2 located on the mainland and 206.50 km2 distributed throughout the country’s islands and cayes. Our findings are substantially different from previous, coarser-resolution national mangrove inventories of Belize, which emphasizes the importance of high-resolution mapping efforts for ongoing conservation efforts.

scholarly journals DETECÇÃO DE ÁREAS DE FLORESTAS INVARIANTES EM SÉRIES TEMPORAIS UTILIZANDO RANDOM FOREST

GEOgraphia ◽  
2021 ◽  
Vol 23 (50) ◽  
Author(s):  
Eduardo Ribeiro Lacerda ◽  
Raúl Sanchéz Vicens
Keyword(s):  
Land Use ◽  
Time Series ◽  
Random Forest ◽  
Change Detection ◽  
Vegetation Change ◽  
Google Earth ◽  
Detection Algorithms ◽  
Palabras Clave ◽  
Change Detection Algorithms ◽  
Google Earth Engine

O surgimento de algoritmos de detecção de mudanças na vegetação na última década é impressionante. Mas os resultados gerados ainda possuem ruído que precisa ser tratado com a utilização de resultados de outros mapeamentos de cobertura vegetal. Além disso, a necessidade de gerar classes de uso do solo invariantes é importante para o melhor entendimento de processos que ocorrem em áreas florestais. Pensando nisso, este trabalho busca criar uma nova forma de mapear essas áreas invariáveis que possam ser utilizadas para mascarar ruídos e também como subsídio para outros estudos de conservação e restauração. A metodologia proposta aqui usa a plataforma Google Earth Engine e um algoritmo de aprendizado de máquina: o Random Forest, para classificar áreas de floresta invariáveis usando todo o acervo de imagens da série temporal Landsat, de uma só vez. Os resultados mostraram que a nova abordagem teve melhor desempenho do que o uso de técnicas mais tradicionais como a agregação de mapeamentos de uso e cobertura anuais, com uma acurácia global de 91,7%. O trabalho busca ainda contribuir com a comunidade de sensoriamento remoto ao apresentar, após exaustivos testes, as melhores opções de variáveis a serem utilizadas neste tipo de classificação. Palavras-chave: Séries Temporais, Detecção de Mudanças, Florestas, Google Earth Engine, Random Forest.DETECTION OF INVARIANT VEGETATION AREAS IN TIME SERIES USING RANDOM FOREST ALGORITHMAbstract: The emergence of vegetation change detection algorithms in the last decade is impressive. But the results still have a lot of noise that needs to be cleaned. And the data cleaning process still uses other landcover mapping results. Besides that, the necessity to generate invariant land use classes is important to know particularly to forest areas. Thinking about that, this paper seeks to create a new form of mapping these invariant areas that can be used to mask noise and as an input on other conservation and restoration studies. The methodology proposed here uses the Google Earth Engine platform and a Random Forest algorithm to classify invariant forest areas using all the image’s collection in the time series at once. The results showed that the new approach performed better than the use of more traditional techniques such as the aggregation of annual land-use and land-cover mappings, with an overall accuracy of 91.7%. Also, this paper seeks to contribute to the remote sensing community showing after exhaustive testing, good options of variables to use on this type of work. Keywords: Time Series, Change Detection, Forests, Google Earth Engine, Random Forest.DETECCIÓN DE ÁREAS DE VEGETACIÓN INVARIANTES EN SÉRIES TEMPORALES UTILIZANDO ALGORITMO RANDOM FORESTResumen: La aparición de algoritmos de detección de cambios en la vegetación en la última década es impresionante. Pero los resultados todavía tienen muchos ruidos que deben ser eliminados. Además, el proceso de limpieza de datos se basa en otros mapas de cobertura de la tierra. Además de eso, es importante conocer la necesidad de generar clases de uso de la tierra invariables, particularmente en las áreas forestales. Pensando en eso, este artículo busca crear una nueva forma de mapear estas áreas invariantes que se pueden utilizar para enmascarar el ruido y como un aporte para otros estudios de conservación y restauración. La metodología propuesta aquí utiliza la plataforma Google Earth Engine y un algoritmo de aprendizaje de máquina: o Random Forest para clasificar áreas invariantes de bosque, utilizando a la vez todas las imágenes de la serie temporal Landsat. Los resultados encontraron que el nuevo enfoque tuvo mejor desempeño que el uso de técnicas tradicionales, con una precisión global del 91,7%. Este trabajo busca además contribuir con la comunidad de la teledetección, mostrando mediante de exhaustivas pruebas, mejores opciones de variables para utilizar en este tipo de clasificación. Palabras clave: Series de Tiempo, Detección de Cambios, Bosques, Google Earth Engine, Random Forest.

scholarly journals DEFORESTATION MAPPING USING SENTINEL-1 AND OBJECT-BASED RANDOM FOREST CLASSIFICATION ON GOOGLE EARTH ENGINE

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 865-872
Author(s):  
V. Yordanov ◽  
M. A. Brovelli
Keyword(s):  
Land Cover ◽  
Agricultural Land ◽  
Weather Conditions ◽  
Google Earth ◽  
Random Forest Classification ◽  
Machine Learning Classification ◽  
Forest Classification ◽  
Object Based ◽  
Short Period ◽  
Google Earth Engine

Abstract. Deforestation can be defined as the conversion of forest land cover to another type. It is a process that has massively accelerated its rate and extent in the last several decades. Mainly due to human activities related to socio-economic processes as population growth, expansion of agricultural land, wood extraction, etc. In the meantime, there are great efforts by governments and agencies to reduce these deforestation processes by implementing regulations, which cannot always be properly monitored whether are followed or not. In this work is proposed an approach that can provide forest loss estimations for a short period of time, by using Synthetic Aperture Radar imagery for an area in the Brazilian Amazon. SAR are providing data with almost no alteration due to weather conditions, however they may present other limitations. To mitigate the speckle effect, here was applied the dry coefficient, which is the mean of image values under the first quartile while preserving the spatial resolution. While for obtaining land cover maps containing only forest and non-forest areas an object-based machine learning classification on the Google Earth Engine platform was applied. The preliminary tests were carried out in a bitemporal manner between 2015 and 2019, followed by applying the approach monthly for the year of 2020. The outputs yielded very satisfactory and accurate results, allowing to estimate the forest dynamics for the area under consideration for each month.

scholarly journals Extraction of Old Towns in Hangzhou (2000–2018) from Landsat Time Series Image Stacks

2021 ◽  
Vol 13 (13) ◽  
pp. 2438
Author(s):  
Hao Ni ◽  
Peng Gong ◽  
Xuecao Li
Keyword(s):  
Time Series ◽  
Random Forest ◽  
City Planning ◽  
Large City ◽  
Google Earth ◽  
Urban Land Use ◽  
Specific Method ◽  
Rapid Urbanization ◽  
Random Forest Classification ◽  
Forest Classification

With rapid urbanization in recent decades, more and more urban renewal has taken place in China. Meanwhile, the early developed areas without change have become old towns, which need special attention in future city planning. However, other than field surveys, there is no specific method to identify old towns. To fill this gap, we used time-series image stacks established from Landsat Surface Reflectance Tier 1 data on the Google Earth Engine (GEE) platform, facilitated by Global Urban Boundary (GUB), Essential Urban Land Use Categories (EULUC) and Global Artificial Impervious Area (GAIA) data. The LandTrendr change detection algorithm was applied to extract detailed information from 14 band/index trajectories. These features were then used as inputs to two methods of old town identification: statistical thresholding and random forest classification. We assessed these two methods in a rapidly developing large city, Hangzhou, and subsequently obtained overall accuracies of 81.33% and 90.67%, respectively. Red band, NIR band and related indices show higher importance in random forest classification, and the magnitude feature plays an outstanding role. The final map of Hangzhou during the 2000–2018 period shows that the old towns were concentrated in the downtown region near West Lake within the urban boundaries in 2000, and far fewer than the renewed areas. The results could serve as references in the provincial and national planning of future urban developments.

scholarly journals COMPARISON OF DIFFERENT CLASSIFICATION TECHNIQUES TO IDENTIFY COCONUT GROWING AREAS IN KOZIKHODE DISTRICT, KERALA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 321-324
Author(s):  
N. Singh ◽  
S. Roy ◽  
P. Kumar ◽  
M. M. Kimothi ◽  
S. Mamatha
Keyword(s):  
Random Forest ◽  
Google Earth ◽  
Random Forest Classification ◽  
Classification Technique ◽  
Forest Classification ◽  
High Resolution Imagery ◽  
The Government ◽  
Kerala State ◽  
Sentinel 2A ◽  
Government Statistics

<p><strong>Abstract.</strong> This study was envisaged to map the coconut growing areas in Kerala state of India, using multidate NDVI obtained from sentinel 2A MSI data, having spatial resolution as 10 m. 95% Cloud free satellite images were taken for classification and date of pass considered for the study were 16th February, 2017 and 18th December, 2017 for Kozhikode district of Kerala. In this study bio-window of coconut plantation was identified using NDVI images of two dates. It was observed that interclass variations were more prominent in February image. Forest, dense and moderately dense coconut plantations have significantly different NDVI values in February image whereas in December image all three features have similar values. Hence, February image was classified using three classification methods i.e. ISODATA, maximum likelihood and random forest classification to assess which method is better to distinguish coconut plantation from other classes. Random Forest classification technique was found to be more accurate in identifying coconut plantation. Area was also estimated for Kozhikode district and compared with the government statistics. Google Earth was taken as reference to identify coconut plantation as it has a unique star shaped canopy, which is clearly visible in high-resolution imagery.</p>

scholarly journals Análise da dinâmica do uso e cobertura da terra do Município de Moju-PA, utilizando Google Earth Engine

2020 ◽  
Vol 13 (5) ◽  
pp. 2332
Author(s):  
Samuel Salin Gonçalves De Souza ◽  
Jones Remo Barbosa Vale ◽  
Merilene Do Socorro Silva Costa ◽  
Bruna Ribeiro Chagas ◽  
Carolina Da Silva Gonçalves ◽  
...  
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Land Cover ◽  
Oil Palm ◽  
Google Earth ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Landsat 5 Tm ◽  
Google Earth Engine ◽  
Temporal And Spatial

Em razão das grandes transformações na paisagem ocasionadas pelas principais atividades econômicas do município de Moju, como as práticas agropecuárias, esta pesquisa procurou fazer uma análise temporal e espacial das mudanças de uso e cobertura da terra na localidade, nos anos de 1999 e 2018, por meio de imagens de satélite disponibilizadas pela plataforma Google Earth Engine. Utilizou-se imagem do satélite Landsat-5/TM referente ao ano 1999, e imagem do satélite Landsat-8/OLI-TIRS correspondente ao ano de 2018, ambas disponíveis no Google Earth Engine (GEE). Realizou-se a classificação temporal e espacial de uso e cobertura da terra por meio da aplicação do algoritmo Random Forest. Utilizou-se as análises qualitativas e quantitativas para os dados mapeados, com o objetivo de realizar um detalhamento sobre a dinâmica do uso e cobertura da terra por meio de tabela e mapa. Os resultados apontam que houve uma perda de mais de 878 km² de cobertura vegetal correspondendo cerca de 12% de perda que veio em decorrência das atividades antrópicas que ocorreram em Moju, principalmente, em relação à agricultura com os cultivos de dendê, mandioca, coco e à pecuária com as áreas de pastagens, pois, juntos apresentam um aumento de mais de mais de 70% que equivalem a 1.037,0938 km². Portanto, constatou-se que o desenvolvimento econômico do município de Moju segue o padrão de desenvolvimento dos municípios amazônicos, onde ocorre a diminuição das áreas florestais para a expansão de suas atividades produtivas, como os cultivos de dendê, sendo este um dos principais indutores do desflorestamento no município. Analysis of the dynamics of the use and land coverage of the Municipality of Moju-pa, using the Google Earth Engine A B S T R A C TDue to the great transformations in the landscape caused by the main economic activities of the municipality of Moju, such as agricultural practices, this research sought to make a temporal and spatial analysis of changes in land use and coverage in the locality, in the years 1999 and 2018, by through satellite images made available by the Google Earth Engine platform. An image of the Landsat-5 / TM satellite for the year 1999 was used, and an image of the Landsat-8 / OLI-TIRS satellite for the year 2018, both available on the Google Earth Engine (GEE). The temporal and spatial classification of land use and land cover was carried out by applying the Random Forest algorithm. Qualitative and quantitative analyzes were used for the mapped data, with the aim of detailing the dynamics of land use and land cover using a table and map. The results show that there was a loss of more than 878 km² of vegetation cover, corresponding to about 12% of the loss that came as a result of the anthropic activities that occurred in Moju, mainly, in relation to agriculture with oil palm, cassava, coconut and livestock with pasture areas, because together they show an increase of more than more than 70%, which is equivalent to 1,037.0938 km². Therefore, it was found that the economic development of the municipality of Moju follows the pattern of development of the Amazonian municipalities, where there is a decrease in forest areas for the expansion of their productive activities, such as oil palm cultivation, which is one of the main drivers of the deforestation in the municipality.Keywords: deforestation; agricultural activities; transformations in the landscape.

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