scholarly journals A Novel Spatial Simulation Method for Mapping the Urban Forest Carbon Density in Southern China by the Google Earth Engine

2021 ◽  
Vol 13 (14) ◽  
pp. 2792
Author(s):  
Fugen Jiang ◽  
Chuanshi Chen ◽  
Chengjie Li ◽  
Mykola Kutia ◽  
Hua Sun
Keyword(s):  
Spatial Distribution ◽  
Urban Forest ◽  
Southern China ◽  
Simulation Method ◽  
Google Earth ◽  
Forest Carbon ◽  
Landsat 8 ◽  
Carbon Density ◽  
Spatial Simulation ◽  
Google Earth Engine

Urban forest is an important component of terrestrial ecosystems and is highly related to global climate change. However, because of complex city landscapes, deriving the spatial distribution of urban forest carbon density and conducting accuracy assessments are difficult. This study proposes a novel spatial simulation method, optimized geographically weighted logarithm regression (OGWLR), using Landsat 8 data acquired by the Google Earth Engine (GEE) and field survey data to map the forest carbon density of Shenzhen city in southern China. To verify the effectiveness of the novel method, multiple linear regression (MLR), k-nearest neighbors (kNN), random forest (RF) and geographically weighted regression (GWR) models were established for comparison. The results showed that OGWLR achieved the highest coefficient of determination (R2 = 0.54) and the lowest root mean square error (RMSE = 13.28 Mg/ha) among all estimation models. In addition, OGWLR achieved a more consistent spatial distribution of carbon density with the actual situation. The carbon density of the forests in the study area was large in the central and western regions and coastal areas and small in the building and road areas. Therefore, this method can provide a new reference for urban forest carbon density estimation and mapping.

scholarly journals Monitoring Oasis Cotton Fields Expansion in Arid Zones Using the Google Earth Engine: A Case Study in the Ogan-Kucha River Oasis, Xinjiang, China

2022 ◽  
Vol 14 (1) ◽  
pp. 225
Author(s):  
Lijing Han ◽  
Jianli Ding ◽  
Jinjie Wang ◽  
Junyong Zhang ◽  
Boqiang Xie ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Drip Irrigation ◽  
Google Earth ◽  
Cotton Field ◽  
Arid Zones ◽  
Landsat 8 ◽  
Land Resource ◽  
Google Earth Engine ◽  
Cotton Fields ◽  
Sentinel 2

Rapid and accurate mapping of the spatial distribution of cotton fields is helpful to ensure safe production of cotton fields and the rationalization of land-resource planning. As cotton is an important economic pillar in Xinjiang, accurate and efficient mapping of cotton fields helps the implementation of rural revitalization strategy in Xinjiang region. In this paper, based on the Google Earth Engine cloud computing platform, we use a random forest machine-learning algorithm to classify Landsat 5 and 8 and Sentinel 2 satellite images to obtain the spatial distribution characteristics of cotton fields in 2011, 2015 and 2020 in the Ogan-Kucha River oasis, Xinjiang. Unlike previous studies, the mulching process was considered when using cotton field phenology information as a classification feature. The results show that both Landsat 5, Landsat 8 and Sentinel 2 satellites can successfully classify cotton field information when the mulching process is considered, but Sentinel 2 satellite classification results have the best user accuracy of 0.947. Sentinel 2 images can distinguish some cotton fields from roads well because they have higher spatial resolution than Landsat 8. After the cotton fields were mulched, there was a significant increase in spectral reflectance in the visible, red-edge and near-infrared bands, and a decrease in the short-wave infrared band. The increase in the area of oasis cotton fields and the extensive use of mulched drip-irrigation water saving facilities may lead to a decrease in the groundwater level. Overall, the use of mulch as a phenological feature for classification mapping is a good indicator in cotton-growing areas covered by mulch, and mulch drip irrigation may lead to a decrease in groundwater levels in oases in arid areas.

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 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 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.

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.

scholarly journals Canadian Wetland Inventory using Google Earth Engine: The First Map and Preliminary Results

2019 ◽  
Vol 11 (7) ◽  
pp. 842 ◽  
Author(s):  
Meisam Amani ◽  
Sahel Mahdavi ◽  
Majid Afshar ◽  
Brian Brisco ◽  
Weimin Huang ◽  
...  
Keyword(s):  
Google Earth ◽  
Landsat 8 ◽  
Wetland Classification ◽  
Large Area ◽  
Time Intervals ◽  
Comprehensive Information ◽  
Field Samples ◽  
Wetland Inventory ◽  
Google Earth Engine ◽  
Processing Techniques

Although wetlands provide valuable services to humans and the environment and cover a large portion of Canada, there is currently no Canada-wide wetland inventory based on the specifications defined by the Canadian Wetland Classification System (CWCS). The most practical approach for creating the Canadian Wetland Inventory (CWI) is to develop a remote sensing method feasible for large areas with the potential to be updated within certain time intervals to monitor dynamic wetland landscapes. Thus, this study aimed to create the first Canada-wide wetland inventory using Landsat-8 imagery and innovative image processing techniques available within Google Earth Engine (GEE). For this purpose, a large amount of field samples and approximately 30,000 Landsat-8 surface reflectance images were initially processed using several advanced algorithms within GEE. Then, the random forest (RF) algorithm was applied to classify the entire country. The final step was an original CWI map considering the five wetland classes defined by the CWCS (i.e., bog, fen, marsh, swamp, and shallow water) and providing updated and comprehensive information regarding the location and spatial extent of wetlands in Canada. The map had reasonable accuracy in terms of both visual and statistical analyses considering the large area of country that was classified (9.985 million km2). The overall classification accuracy and the average producer and user accuracies for wetland classes exclusively were 71%, 66%, and 63%, respectively. Additionally, based on the final classification map, it was estimated that 36% of Canada is covered by wetlands.

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