Application of Google earth engine python API and NAIP imagery for land use and land cover classification: A case study in Florida, USA

Prasai, Ritika; Schwertner, T. Wayne; Mainali, Kumar P.; Mathewson, Heather A.; Kafley, Hemanta; Thapa, Swosthi; Adhikari, D.; Medley, Paul; Drake, Jason B.

doi:10.1016/j.ecoinf.2021.101474

Cited by 37 publications

(18 citation statements)

References 23 publications

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“…The emergence of Google Earth Engine (GEE) has greatly promoted the research on land use by remote sensing. GEE not only provides massive satellite image datasets and geographic datasets (Kumar and Mutanga, 2018) but also provides API interfaces (Prasai et al, 2021), analysis algorithms and tools based on JavaScript and Python languages (Gorelick et al, 2017;Amani et al, 2020). Programming languages are directly used to analyze and process remote sensing data in GEE (Chang et al, 2018;Mutanga and Kumar, 2019), which avoids the tedious processes of data download, preprocessing, and image classification brought about by traditional remote sensing analysis models.…”

Section: Introductionmentioning

confidence: 99%

Research on temporal and spatial evolution of land use and landscape pattern in Anshan City based on GEE

Zhang

2022

Front. Environ. Sci.

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Frequent mining activities can bring about problems such as soil erosion and environmental pollution, which are detrimental to the efficient use of land and the sustainable development of cities. Existing studies have paid little attention to mining areas and lack comparative analysis of landscape changes in multiple mining pits. In this paper, the main urban area of Anshan City, where the mining areas are concentrated, was used as the research area, and the Landsat TM/OLI surface reflectance (SR) data of the Google Earth Engine (GEE) platform and the random forest algorithm were used to map the land use in 2008, 2014, and 2020. On this basis, land use dynamics and landscape pattern indices were used to analyze the changes in land use and landscape patterns in the Anshan City area. In addition, a moving window method was combined to further analyze and compare the landscape changes between different pits. The results show that:1. From 2008 to 2020, the construction land in Anshan urban area continued to decline, the forest land continued to expand, and the construction land was shifted to the forest land and cultivated land. Mining land increased before 2014 and remained almost unchanged after 2014, which is in line with the actual situation. 2. During the study period, the landscape fragmentation degree and landscape heterogeneity in the urban area of Anshan kept increasing. The high value areas of landscape fragmentation were the urban-rural combination areas and the mining areas. Among them, the reclamation of Dagushan and Donganshan is better, while the reclamation of Anqian, Yanqianshan and Xiaolingzi mines needs to be strengthened. 3. The random forest algorithm based on GEE shows a high degree of accuracy for land use classification. The overall classification accuracy in 3 years exceeds 90% and the kappa coefficient exceeds 0.85. The study results can be used as an essential reference for optimizing the urban ecological environment and provide technical backing for the urbanization construction and rational use of land in Anshan City.

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Section: Introductionmentioning

confidence: 99%

Research on temporal and spatial evolution of land use and landscape pattern in Anshan City based on GEE

Zhang

2022

Front. Environ. Sci.

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“…This cloud-based processor proved to be a powerful and efficient tool. Thus, we believe that the open-source nature of GEE Python API and its library of remote sensing data could impact remote sensing projects throughout the world [27][28][29][30][31][32][33][34][35][36].…”

Section: Discussionmentioning

confidence: 99%

Using Google Earth Engine for the complete pipeline of temporal analysis of NDVI in Chitwan National Park of Nepal

2022

JRAR

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Advent of cloud computing and Google Earth Engine has completely changed the way we acquire, manage and process large satellite data. Here, we analyze temporal dynamics of vegetation productivity in Chitwan National Park, Nepal (CNP) for the period of 1988 to 2020 using Google Earth Engine (GEE) Python API, without using local computer for data storage or analysis. Specifically, we computed normalized difference vegetation index (NDVI) from Landsat data and analyzed the time-series NDVI using a Thiel Sen estimator and by decomposing it to the trend and season components We completed remote sensing analyses including image retrieval, image analysis, classification and report generation in GEE Python API, which is a free resource. Our results showed that NDVI in CNP increased at the rate of 0.0006/year (p<0.05, R2 = 0.66). We observed variation in NDVI during pre-monsoon (p<0.05, R2 =0.79) and monsoon (p<0.05, R2 =0.79) seasons which were 0.0005/ year and 0.0007/year, respectively. An annual increase in NDVI value in CNP indicates an increase in primary productivity that is assumed to support higher animal species richness. Using NDVI in GEE Python API proved to be an effective and efficient tool for monitoring the primary productivity of ecologically important sites such as protected areas.

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“…Therefore, most of the discussed service provider also provide research grants to such individuals and researchers and encourage using their platform. Using the services from multiple service providers [12][13][14][15][16][17][18] and its training materials, frequently usable applications has also been developed [19][20][21][22][23], [26][27]. Table 6 gives more details about the grant and support information.…”

Section: Research Grant and Training Materialsmentioning

confidence: 99%

Representative Service Providers and Their Selection in Cloud Computing Domain; A Comprehensive Overview

Dahal¹,

Prasai²

2022

Preprint

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The popularity of cloud computing is growing owing to its large data storage capacity and high computation power. It provides online, on-demand, scalable application solution, removes hardware and software barriers for non-specialist, rapidly integrates and deploys desired and necessary facilities, supports quick upgrading and addition of features. Users get benefitted with the selection of the appropriate cloud computing platform for their projects. Here, our paper provides a comprehensive overview of the services provided to the users by the most common cloud computing service providers. This paper could be used as a reference while selecting the best service provider based on the requirements of the projects.

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Application of Google earth engine python API and NAIP imagery for land use and land cover classification: A case study in Florida, USA

Cited by 37 publications

References 23 publications

Research on temporal and spatial evolution of land use and landscape pattern in Anshan City based on GEE

Research on temporal and spatial evolution of land use and landscape pattern in Anshan City based on GEE

Using Google Earth Engine for the complete pipeline of temporal analysis of NDVI in Chitwan National Park of Nepal

Representative Service Providers and Their Selection in Cloud Computing Domain; A Comprehensive Overview

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