scholarly journals CARACTERÍSTICAS HIDROGEOMORFOMÉTRICAS E DINÂMICA DA COBERTURA DO SOLO NA MICROBACIA PROSPERIDADE, AMAZÔNIA OCIDENTAL, BRASIL

2022 ◽  
Vol 3 (1) ◽  
pp. e311019
Author(s):  
Thaiza Martins de Macedo ◽  
João Ânderson Fulan ◽  
Carlos Victor Lamarão Pereira ◽  
Maria Letícia de Sousa Gomes ◽  
Renato Francisco da Silva Souza ◽  
...  
Keyword(s):  
Google Earth ◽  
Landsat 8

O planejamento e a gestão dos recursos naturais é essencial para alcançar o desenvolvimento sustentável na região amazônica, e tem como base as características da paisagem. Diante disso, o objetivo do presente estudo foi disponibilizar informações sobre as características hidrogeomorfométricas da paisagem e da dinâmica da cobertura do solo na microbacia Prosperidade, Amazônia Ocidental, Brasil. As informações foram obtidas com técnicas de sensoriamento remoto, utilizando softwares (QGIS 2.10.1, Google Earth e TrackMaker Free) e imagens de satélites (ALOS, Landsat 5 e Landsat 8), e equações. A microbacia tem área de 24 km2, perímetro de 27,21 km, forma alongada, altitudes de 241 a 331 m, relevos planos a forte ondulados, rede de drenagem de 35,52 km com padrão dendrítico, rios de 4ª ordem, 2,71 nascentes km-2, densidade de drenagem de 1,48 km km-2, coeficiente de manutenção de 675,7 m2 m-1, índice de sinuosidade de 20,97% e tempo de concentração de 3,01 h. No período de 1984 a 2021, ocorreu um aumento da área de agropecuária e uma redução da área de floresta nativa na microbacia e na zona ripária. A microbacia tem potencial para o desenvolvimento de atividades agropecuárias, contudo, o desmatamento na zona ripária está comprometendo a qualidade dos recursos hídricos. Recomenda-se a manutenção da vegetação nativa remanescente, recuperação da vegetação nativa na zona ripária, adoção de práticas conservacionistas nos sistemas agropecuários e a introdução do componente arbóreo como fonte econômica (ex: sistemas agroflorestais, silvipastoris e agrossilvipastoris).

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 Patterns, Trends and Drivers of Water Transparency in Sri Lanka Using Landsat 8 Observations and Google Earth Engine

2021 ◽  
Vol 13 (11) ◽  
pp. 2193
Author(s):  
Deepakrishna Somasundaram ◽  
Fangfang Zhang ◽  
Sisira Ediriweera ◽  
Shenglei Wang ◽  
Ziyao Yin ◽  
...  
Keyword(s):  
Sri Lanka ◽  
Human Activity ◽  
Google Earth ◽  
Water Transparency ◽  
Conservation Strategies ◽  
Landsat 8 ◽  
Essential Information ◽  
Annual Variations ◽  
North East ◽  
South West Monsoon

Addressing inland water transparency and driver effects to ensure the sustainability and provision of good quality water in Sri Lanka has been a timely prerequisite, especially under the Sustainable Development Goals 2030 agenda. Natural and anthropogenic changes lead to significant variations in water quality in the country. Therefore, an urgent need has emerged to understand the variability, spatiotemporal patterns, changing trends and impact of drivers on transparency, which are unclear to date. This study used all available Landsat 8 images from 2013 to 2020 and a quasi-analytical approach to assess the spatiotemporal Secchi disk depth (ZSD) variability of 550 reservoirs and its relationship with natural (precipitation, wind and temperature) and anthropogenic (human activity and population density) drivers. ZSD varied from 9.68 cm to 199.47 with an average of 64.71 cm and 93% of reservoirs had transparency below 100 cm. Overall, slightly increasing trends were shown in the annual mean ZSD. Notable intra-annual variations were also indicating the highest and lowest ZSD during the north-east monsoon and south-west monsoon, respectively. The highest ZSD was found in wet zone reservoirs, while dry zone showed the least. All of the drivers were significantly affecting the water transparency in the entire island. The combined impact of natural factors on ZSD changes was more significant (77.70%) than anthropogenic variables, whereas, specifically, human activity accounted for the highest variability across all climatic zones. The findings of this study provide the first comprehensive estimation of the ZSD of entire reservoirs and driver contribution and also provides essential information for future sustainable water management and conservation strategies.

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.

scholarly journals MANEJO, QUALIDADE E DINÂMICA DA DEGRADAÇÃO DE PASTAGENS NA MATA ATLÂNTICA DE MINAS GERAIS - BRASIL

Nativa ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 370 ◽  
Author(s):  
Luís Flávio Pereira ◽  
Cecilia Fátima Carlos Ferreira ◽  
Ricardo Morato Fiúza Guimarães
Keyword(s):  
Remote Sensing ◽  
Atlantic Forest ◽  
Quality Index ◽  
Minas Gerais ◽  
Google Earth ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Mata Atlântica ◽  
Mata Atlantica ◽  
Forest Biome

Pastagens sob práticas de manejo ineficientes tornam-se degradadas, provocando sérios problemas socioambientais e econômicos. Assim, entender a dinâmica dos sistemas pastoris e suas interações com o meio físico torna-se essencial na busca de alternativas sustentáveis para a agropecuária. Estudou-se manejo, dinâmica anual e interações socioambientais em pastagens de uma bacia hidrográfica no bioma Mata Atlântica em Minas Gerais, Brasil, durante o ano hidrológico 2016/2017. Utilizou-se dados de campo, relatos de agricultores e sensoriamento remoto via imagens LANDSAT 8 OLI e Google Earth Pro®. Foi proposto um índice de qualidade para pastagens da região. As pastagens apresentaram, em média, qualidade moderada. Níveis de degradação foram altos, oscilando de forma quadrática (níveis 2, 4, 5 e IDP) e potencial (nível 1) com a precipitação (p < 0,01), o que sugere que a irrigação possa ser prática eficiente no controle da degradação. Durante o ano, pelo menos 51,27% das pastagens apresentaram algum sinal de degradação, atingindo-se a marca de 91,32%, no período seco. Os resultados sugerem pior qualidade e maiores níveis de degradação de pastagens em terras elevadas e declivosas. Devido às condições socioambientais locais, indica-se o uso de sistemas silvipastoris agroecológicos no manejo das pastagens.Palavras-chave: uso da terra, sensoriamento remoto, relação solo paisagem, Zona da Mata, índice de qualidade. MANAGEMENT, QUALITY AND DEGRADATION DYNAMICS OF PASTURES IN ATLANTIC FOREST BIOME, MINAS GERAIS – BRASIL ABSTRACT:Pastures under inefficient management practices get degraded, leading to serious socioeconomic and environmental issues. That being said, understanding the dynamics of such systems and their interaction with the environment is essential when it comes to looking towards sustainable alternatives for livestock activities. The management, annual dynamics and socio-environmental interactions in pastures in an hydrographic basin located in Atlantic Forest biome, Minas Gerais, Brasil, were studied during the hydrological year of 2016/2017. Field data and farmers reports were utilized, such as remote sensing via images from LANDSAT 8 OLI and Google Earth Pro®. A quality index was proposed for the pastures, which usually presented medium quality. Degradation levels were high, oscillating in a quadratic basis (levels 2, 4, 5 and IDP) and potential (level 1) with precipitation (p < 0,01), which suggests that irrigation might be an efficient practice when it comes to degradation control. During the year, at least 51,27% of pastures have presented signs of degradation, achieving 91,32% in dry periods. The results suggest less quality and bigger degradation levels in pastures located in high and steep areas. Considering the local environmental conditions, agroecological silvopasture systems are recommended regarding the pastures management.Keywords: land use, remote sensing, soil/landscape relationships, Zona da Mata, quality index.

scholarly journals IDENTIFIKASI PENGGUNAAN LAHAN MENGGUNAKAN CITRA SATELIT LANDSAT 8 MELALUI GOOGLE EARTH ENGINE

2021 ◽  
Vol 2020 (1) ◽  
pp. 798-805
Author(s):  
Ratu Kintan Karina ◽  
Robert Kurniawan
Keyword(s):  
Google Earth ◽  
Landsat 8 ◽  
Google Earth Engine

Penelitian ini dilakukan di Kabupaten Lahat yang mana potensi banjir pada daerah ini juga disebabkan oleh daya guna lahan yang berkurang. Sehingga penelitian ini dilakukan untuk melihat bagaimana peta penggunaan lahan di Kabupaten Lahat dalam satu tahun terakhir dan bagaimana persentase dari setiap lahan tersebut dengan melakukan penginderaan jauh yang memanfaatkan citra satelit Landsat 8. Metode yang digunakan dalam penelitian ini adalah metode deskriptif dan metode analisis citra yang mana semua pengolahan dan analisis dilakukan pada Google Earth Engine. Berdasarkan hasil penelitian, peta penggunaan lahan ini memperoleh akurasi keseluruhan sebesar 89,38% dan akurasi Kappa sebesar 85,21%, dimana sebaran luas penggunaan lahan di Kecamatan Lahat untuk Kawasan Vegetasi seluas 2941,81 km2 atau 82,32%, Badan Air seluas 58,73 km2 atau 1,64%, Lahan Terbangun seluas 177,52 km2 atau 4,97%, Tambak seluas 57,29 km2 atau 1,60%, Rumput/Semak seluas 1,09 km2 atau 0,03%, Lahan Terbuka seluas 39,97 km2 atau 1,12%, dan Sawah seluas 297,30 km2 atau 8,32%. Sehingga dapat disimpulkan bahwa peta penggunaan lahan yang dihasilkan menunjukkan kawasan vegetasi merupakan lahan terluas di Kabupaten Lahat dan lahan rumput/semak belukar merupakan lahan yang paling dikit di Kabupaten Lahat. Namun hasil yang diperoleh tidak menutup kemungkinan adanya kesalahan dalam interpretasi citra sehingga masih perlu dilakukan observasi lapangan untuk mengecek kesesuaian dan memperkuat hasil akurasi penggunaan lahan.

scholarly journals Mapeamento da Vegetação Nativa do Cerrado na Região de Três Lagoas-MS com o Google Earth Engine

2019 ◽  
Vol 71 (3) ◽  
pp. 702-725
Author(s):  
Nayara Vasconcelos Estrabis ◽  
José Marcato Junior ◽  
Hemerson Pistori
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Google Earth ◽  
Support Vector ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Google Earth Engine

O Cerrado é um dos biomas existentes no Brasil e o segundo mais extenso da América do Sul. Possui grande importância devido a sua biodiversidade, ecossistema e principalmente por servir como um reservatório, ou “esponja”, que distribui água para os demais biomas, além de ser berço de nascentes de algumas das maiores bacias da América do Sul. No entanto, devido às atividades antrópicas praticadas (com destaque para a pecuária e silvicultura) e a redução da vegetação nativa, este bioma está ameaçado. Considerado como hotspot em biodiversidade, o Cerrado pode não existir em 2050. Com a necessidade de sua preservação, o objetivo desse trabalho consistiu em investigar o uso de algoritmos de aprendizado de máquina para realizar o mapeamento da vegetação nativa existente na região do município de Três Lagoas, utilizando a plataforma em nuvem Google Earth Engine. O processo foi realizado com uma imagem Landsat-8 OLI, datada de 10 de outubro de 2018, e com os algoritmos Random Forest (RF) e Support Vector Machine (SVM). Na validação da classificação, o RF e o SVM apresentaram índices kappa iguais a 0,94 e 0,97, respectivamente. O RF, quando comparado ao SVM, apresentou classificação mais ruidosa. Por fim, verificou-se a existência de vegetação nativa de aproximadamente 2556 km² ao adotar o RF e 2873 km² ao adotar SVM.

An integrated decision support system using satellite and in-situ data for coastal area hazard mitigation and resilience to natural disasters

2021 ◽  
Author(s):  
Christos Kontopoulos ◽  
Nikos Grammalidis ◽  
Dimitra Kitsiou ◽  
Vasiliki Charalampopoulou ◽  
Anastasios Tzepkenlis ◽  
...  
Keyword(s):  
Decision Support ◽  
Surface Water ◽  
Decision Support System ◽  
Real Time ◽  
Support System ◽  
Satellite Data ◽  
Hazard Mitigation ◽  
Google Earth ◽  
Landsat 8

&lt;p&gt;Nowadays, the importance of coastal areas is greater than ever, with approximately 10% of the global population living in these areas. These zones are an intermediate space between sea and land and are exposed to a variety of natural (e.g. ground deformation, coastal erosion, flooding, tornados, sea level rise, etc.) and anthropogenic (e.g. excessive urbanisation) hazards. Therefore, their conservation and proper sustainable management is deemed crucial both for economic and environmental purposes. The main goal of the Greece-China bilateral research project &amp;#8220;EPIPELAGIC: ExPert Integrated suPport systEm for coastaL mixed urbAn &amp;#8211; industrial &amp;#8211; critical infrastructure monitorinG usIng Combined technologies&amp;#8221; is the design and deployment of an integrated Decision Support System (DSS) for hazard mitigation and resilience. The system exploits near-real time data from both satellite and in-situ sources to efficiently identify and produce alerts for important risks (e.g. coastal flooding, soil erosion, degradation, subsidence), as well as to monitor other important changes (e.g. urbanization, coastline). To this end, a robust methodology has been defined by fusing satellite data (Optical/multispectral, SAR, High Resolution imagery, DEMs etc.) and in situ real-time measurements (tide gauges, GPS/GNSS etc.). For the satellite data pre-processing chain, image composite/mosaic generation techniques will be implemented via Google Earth Engine (GEE) platform in order to access Sentinel 1, Sentinel 2, Landsat 5 and Landsat 8 imagery for the studied time period (1991-2021). These optical and SAR composites will be stored into the main database of the EPIPELAGIC server, after all necessary harmonization and correction techniques, along with other products that are not yet available in GEE (e.g. ERS or Sentinel-1 SLC products) and will have to be locally processed. A Machine Learning (ML) module, using data from this main database will be trained to extract additional high-level information (e.g. coastlines, surface water, urban areas, etc.). Both conventional (e.g. Otsu thresholding, Random Forest, Simple Non-Iterative Clustering (SNIC) algorithm, etc.) and deep learning approaches (e.g. U-NET convolutional networks) will be deployed to address problems such as surface water detection and land cover/use classification. Additionally, in-situ or auxiliary/cadastral datasets will be used as ground truth data. Finally, a Decision Support System (DSS), will be developed to periodically monitor the evolution of these measurements, detect significant changes that may indicate impending risks and hazards, and issue alarms along with suggestions for appropriate actions to mitigate the detected risks. Through the project, the extensive use of Explainable Artificial Intelligence (xAI) techniques will also be investigated in order to provide &amp;#8220;explainable recommendations&amp;#8221; that will significantly facilitate the users to choose the optimal mitigation approach. The proposed integrated monitoring solutions is currently under development and will be applied in two Areas of Interest, namely Thermaic Gulf in Thessaloniki, Greece, and the Yellow River Delta in China. They are expected to provide valuable knowledge, methodologies and modern techniques for exploring the relevant physical mechanisms and offer an innovative decision support tool. Additionally, all project related research activities will provide ongoing support to the local culture, society, economy and environment in both involved countries, Greece and China.&lt;/p&gt;

Assessment of the State of the Territories of Closed Waste Disposal Facilities by Vegetation Indices

2021 ◽  
Vol 25 (9) ◽  
pp. 30-37
Author(s):  
N.N. Sliusar ◽  
A.P. Belousova ◽  
G.M. Batrakova ◽  
R.D. Garifzyanov ◽  
M. Huber-Humer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Waste Disposal ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Cost Effective ◽  
Google Earth ◽  
The State ◽  
Landsat 8 ◽  
The Earth

The possibilities of using remote sensing of the Earth data to assess the formation of phytocenoses at reclaimed dumps and landfills are presented. The objects of study are landfills and dumps in the Perm Territory, which differed from each other in the types and timing of reclamation work. The state of the vegetation cover on the reclaimed and self-overgrowing objects was compared with the reference plots with naturally formed herbage of zonal meadow vegetation. The process of reclamation of the territory of closed landfills was assessed by the presence and homogeneity of the vegetation layer and by the values of the vegetation index NDVI. To identify the dynamics of changes in the vegetation cover, we used multi-temporal satellite images from the open resources of Google Earth and images in the visible and infrared ranges of the Landsat-5/TM and Landsat-8/OLI satellites. It is shown that the data of remote sensing of the Earth, in particular the analysis of vegetation indices, can be used to assess the dynamics of overgrowing of territories of reclaimed waste disposal facilities, as well as an additional and cost-effective method for monitoring the restoration of previously disturbed territories.

Índice de vegetación y cambio climático: una evaluación multitemporal de imágenes Landsat en la laguna Pucarani en el periodo 1972 al 2018

2021 ◽  
Vol 3 (2) ◽  
pp. 96-106
Author(s):  
Alejandro Jean Pier Mamani Vargas ◽  
Carmen Rosa Román Arce
Keyword(s):  
Google Earth ◽  
Landsat 8 ◽  
Google Earth Engine

La investigación evaluó la relación entre los índices de vegetación y el cambio climático haciendo una evaluación multitemporal de imágenes Landsat para el periodo 1972 – 2018 en la laguna Paucarani, Tacna. La información utilizada corresponde a imágenes satelitales Landsat 5 y Landsat 8 nivel 1 T corregidas en nivel de reflectancia superficial seleccionadas de la plataforma Google Earth Engine en donde se generó el índice de vegetación de diferencia normalizada “NDVI” para los años 1986, 1995, 2010 y 2018 y el índice de agua de diferencia normalizada “NDWI”. Los resultados indican que los valores medios de NDVI más altos corresponden a los meses de diciembre a mayo con valores iguales o mayores a 0,1, por el contrario, en los meses de julio y agosto los valores medios de NDVI disminuyen, presentando el año 1986 el valor de 0,078 y el año 2018 el valor de 0,065. De los valores de precipitación se verificó que estos presentan una frecuencia cíclica con años húmedos y secos, con respecto a la frecuencia de precipitación, se determinó que el año 1986 presenta una frecuencia de precipitación desde el mes de diciembre hasta abril en comparación con el año 2018 que presenta frecuencia para los meses enero y febrero. De los valores de promedio anual de temperatura mínima, se verificó que estos presentan una tendencia de aumento a partir del año 2012 con valores menores a -6,50 °C en comparación con valores de hasta -12,4 °C presentes en años anteriores. Se concluye que la variable precipitación, temperatura mínima y los valores del índice de vegetación “NDVI” presenta una relación con un grado de significancia < 0,05 según Pearson.

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