scholarly journals Accuracies Achieved in Classifying Five Leading World Crop Types and their Growth Stages Using Optimal Earth Observing-1 Hyperion Hyperspectral Narrowbands on Google Earth Engine

2018 ◽  
Vol 10 (12) ◽  
pp. 2027 ◽  
Author(s):  
Itiya Aneece ◽  
Prasad Thenkabail
Keyword(s):  
United States ◽  
Remote Sensing ◽  
Hyperspectral Imaging ◽  
The United States ◽  
Google Earth ◽  
Growth Stages ◽  
Agricultural Crops ◽  
Spectral Library ◽  
Crop Types ◽  
Google Earth Engine

As the global population increases, we face increasing demand for food and nutrition. Remote sensing can help monitor food availability to assess global food security rapidly and accurately enough to inform decision-making. However, advances in remote sensing technology are still often limited to multispectral broadband sensors. Although these sensors have many applications, they can be limited in studying agricultural crop characteristics such as differentiating crop types and their growth stages with a high degree of accuracy and detail. In contrast, hyperspectral data contain continuous narrowbands that provide data in terms of spectral signatures rather than a few data points along the spectrum, and hence can help advance the study of crop characteristics. To better understand and advance this idea, we conducted a detailed study of five leading world crops (corn, soybean, winter wheat, rice, and cotton) that occupy 75% and 54% of principal crop areas in the United States and the world respectively. The study was conducted in seven agroecological zones of the United States using 99 Earth Observing-1 (EO-1) Hyperion hyperspectral images from 2008–2015 at 30 m resolution. The authors first developed a first-of-its-kind comprehensive Hyperion-derived Hyperspectral Imaging Spectral Library of Agricultural crops (HISA) of these crops in the US based on USDA Cropland Data Layer (CDL) reference data. Principal Component Analysis was used to eliminate redundant bands by using factor loadings to determine which bands most influenced the first few principal components. This resulted in the establishment of 30 optimal hyperspectral narrowbands (OHNBs) for the study of agricultural crops. The rest of the 242 Hyperion HNBs were redundant, uncalibrated, or noisy. Crop types and crop growth stages were classified using linear discriminant analysis (LDA) and support vector machines (SVM) in the Google Earth Engine cloud computing platform using the 30 optimal HNBs (OHNBs). The best overall accuracies were between 75% to 95% in classifying crop types and their growth stages, which were achieved using 15–20 HNBs in the majority of cases. However, in complex cases (e.g., 4 or more crops in a Hyperion image) 25–30 HNBs were required to achieve optimal accuracies. Beyond 25–30 bands, accuracies asymptote. This research makes a significant contribution towards understanding modeling, mapping, and monitoring agricultural crops using data from upcoming hyperspectral satellites, such as NASA’s Surface Biology and Geology mission (formerly HyspIRI mission) and the recently launched HysIS (Indian Hyperspectral Imaging Satellite, 55 bands over 400–950 nm in VNIR and 165 bands over 900–2500 nm in SWIR), and contributions in advancing the building of a novel, first-of-its-kind global hyperspectral imaging spectral-library of agricultural crops (GHISA: www.usgs.gov/WGSC/GHISA).

scholarly journals Progress and Trends in the Application of Google Earth and Google Earth Engine

2021 ◽  
Vol 13 (18) ◽  
pp. 3778
Author(s):  
Qiang Zhao ◽  
Le Yu ◽  
Xuecao Li ◽  
Dailiang Peng ◽  
Yongguang Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Biology Education ◽  
The United States ◽  
Google Earth ◽  
Earth Observation ◽  
Earth System ◽  
Earth System Science ◽  
System Science ◽  
Research Areas ◽  
Google Earth Engine

Earth system science has changed rapidly due to global environmental changes and the advent of Earth observation technology. Therefore, new tools are required to monitor, measure, analyze, evaluate, and model Earth observation data. Google Earth (GE) was officially launched by Google in 2005 as a ”geobrowser”, and Google Earth Engine (GEE) was released in 2010 as a cloud computing platform with substantial computational capabilities. The use of these two tools or platforms in various applications, particularly as used by the remote sensing community, has developed rapidly. In this paper, we reviewed the applications and trends in the use of GE and GEE by analyzing peer-reviewed articles, dating up to January 2021, in the Web of Science (WoS) core collection using scientometric analysis (i.e., by using CiteSpace) and meta-analysis. We found the following: (1) the number of articles describing the use of GE or GEE increased substantially from two in 2006 to 530 in 2020. The number of GEE articles increased much faster than those concerned with the use of GE. (2) Both GE and GEE were extensively used by the remote sensing community as multidisciplinary tools. GE articles covered a broader range of research areas (e.g., biology, education, disease and health, economic, and information science) and appeared in a broader range of journals than those concerned with the use of GEE. (3) GE and GEE shared similar keywords (e.g., “land cover”, “water”, “model”, “vegetation”, and “forest”), which indicates that their application is of great importance in certain research areas. The main difference was that articles describing the use of GE emphasized its use as a visual display platform, while those concerned with GEE placed more emphasis on big data and time-series analysis. (4) Most applications of GE and GEE were undertaken in countries, such as the United States, China, and the United Kingdom. (5) GEE is an important tool for analysis, whereas GE is used as an auxiliary tool for visualization. Finally, in this paper, the merits and limitations of GE and GEE, and recommendations for further improvements, are summarized from an Earth system science perspective.

scholarly journals GOOGLE EARTH ENGINE: APPLICATION OF ALGORITHMS FOR REMOTE SENSING OF CROPS IN TUSCANY (ITALY)

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 291-296
Author(s):  
J. P. Clemente ◽  
G. Fontanelli ◽  
G. G. Ovando ◽  
Y. L. B. Roa ◽  
A. Lapini ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Google Earth ◽  
Optical Data ◽  
Support Vector ◽  
Use Of Time ◽  
Crop Types ◽  
Google Earth Engine ◽  
Agricultural Areas ◽  
Sentinel 2

Abstract. Remote sensing has become an important mean to assess crop areas, specially for the identification of crop types. Google Earth Engine (GEE) is a free platform that provides a large number of satellite images from different constellations. Moreover, GEE provides pixel-based classifiers, which are used for mapping agricultural areas. The objective of this work is to evaluate the performance of different classification algorithms such as Minimum Distance (MD), Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Trees (CART) and Na¨ıve Bayes (NB) on an agricultural area in Tuscany (Italy). Four different scenarios were implemented in GEE combining different information such as optical and Synthetic Aperture Radar (SAR) data, indices and time series. Among the five classifiers used the best performers were RF and SVM. Integrating Sentinel-1 (S1) and Sentinel-2 (S2) slightly improves the classification in comparison to the only S2 image classifications. The use of time series substantially improves supervised classifications. The analysis carried out so far lays the foundation for the integration of time series of SAR and optical data.

scholarly journals Assessing the Effect of Training Sampling Design on the Performance of Machine Learning Classifiers for Land Cover Mapping Using Multi-Temporal Remote Sensing Data and Google Earth Engine

2021 ◽  
Vol 13 (8) ◽  
pp. 1433
Author(s):  
Shobitha Shetty ◽  
Prasun Kumar Gupta ◽  
Mariana Belgiu ◽  
S. K. Srivastav
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Random Sampling ◽  
Sampling Design ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Multi Temporal ◽  
Google Earth Engine

Machine learning classifiers are being increasingly used nowadays for Land Use and Land Cover (LULC) mapping from remote sensing images. However, arriving at the right choice of classifier requires understanding the main factors influencing their performance. The present study investigated firstly the effect of training sampling design on the classification results obtained by Random Forest (RF) classifier and, secondly, it compared its performance with other machine learning classifiers for LULC mapping using multi-temporal satellite remote sensing data and the Google Earth Engine (GEE) platform. We evaluated the impact of three sampling methods, namely Stratified Equal Random Sampling (SRS(Eq)), Stratified Proportional Random Sampling (SRS(Prop)), and Stratified Systematic Sampling (SSS) upon the classification results obtained by the RF trained LULC model. Our results showed that the SRS(Prop) method favors major classes while achieving good overall accuracy. The SRS(Eq) method provides good class-level accuracies, even for minority classes, whereas the SSS method performs well for areas with large intra-class variability. Toward evaluating the performance of machine learning classifiers, RF outperformed Classification and Regression Trees (CART), Support Vector Machine (SVM), and Relevance Vector Machine (RVM) with a >95% confidence level. The performance of CART and SVM classifiers were found to be similar. RVM achieved good classification results with a limited number of training samples.

scholarly journals Machine Learning Comparison and Parameter Setting Methods for the Detection of Dump Sites for Construction and Demolition Waste Using the Google Earth Engine

2021 ◽  
Vol 13 (4) ◽  
pp. 787
Author(s):  
Lei Zhou ◽  
Ting Luo ◽  
Mingyi Du ◽  
Qiang Chen ◽  
Yang Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Google Earth ◽  
Construction And Demolition Waste ◽  
Parameterization Scheme ◽  
Classification Methods ◽  
Demolition Waste ◽  
Optimal Method ◽  
Identification Method ◽  
Google Earth Engine

Machine learning has been successfully used for object recognition within images. Due to the complexity of the spectrum and texture of construction and demolition waste (C&DW), it is difficult to construct an automatic identification method for C&DW based on machine learning and remote sensing data sources. Machine learning includes many types of algorithms; however, different algorithms and parameters have different identification effects on C&DW. Exploring the optimal method for automatic remote sensing identification of C&DW is an important approach for the intelligent supervision of C&DW. This study investigates the megacity of Beijing, which is facing high risk of C&DW pollution. To improve the classification accuracy of C&DW, buildings, vegetation, water, and crops were selected as comparative training samples based on the Google Earth Engine (GEE), and Sentinel-2 was used as the data source. Three classification methods of typical machine learning algorithms (classification and regression trees (CART), random forest (RF), and support vector machine (SVM)) were selected to classify the C&DW from remote sensing images. Using empirical methods, the experimental trial method, and the grid search method, the optimal parameterization scheme of the three classification methods was studied to determine the optimal method of remote sensing identification of C&DW based on machine learning. Through accuracy evaluation and ground verification, the overall recognition accuracies of CART, RF, and SVM for C&DW were 73.12%, 98.05%, and 85.62%, respectively, under the optimal parameterization scheme determined in this study. Among these algorithms, RF was a better C&DW identification method than were CART and SVM when the number of decision trees was 50. This study explores the robust machine learning method for automatic remote sensing identification of C&DW and provides a scientific basis for intelligent supervision and resource utilization of C&DW.

scholarly journals First Report of Okra yellow mosaic Mexico virus in Okra in the United States

Plant Disease ◽  
2010 ◽  
Vol 94 (7) ◽  
pp. 924-924 ◽  
Author(s):  
C. Hernandez-Zepeda ◽  
T. Isakeit ◽  
A. Scott ◽  
J. K. Brown
Keyword(s):  
United States ◽  
The United States ◽  
Full Length ◽  
Bipartite Begomoviruses ◽  
Economic Losses ◽  
Growth Stages ◽  
First Report ◽  
Total Dna ◽  
Hidalgo County ◽  
Whitefly Vector

During the okra growing season from August to November of 2009, symptoms reminiscent of geminivirus infection were observed on 75% of ‘Green Emerald’ Abelmoschus esculentus (L.) Moench, plants in a 0.2-km2 field in Hidalgo County, TX. Visible symptoms consisted of irregular yellow patches on leaves, distinctive yellow borders on leaf edges, and chlorosis of subsequently developing leaves. The whitefly vector of begomoviruses, Bemisia tabaci (Genn.), infested okra plants in the early growth stages during late July 2009. Total DNA was isolated from the leaves of three symptomatic okra plant samples (1) and used as the PCR template to amplify a 575-bp fragment of the coat protein gene (CP) using the universal begomovirus primers AV494 and AC1048 (2). PCR products of the expected size were cloned into the pGEM-T Easy (Promega, Madison, WI) and sequenced using the universal M13F and M13 R primers. ClustalV alignment indicated 99 to 100% shared nucleotide (nt) identity, and BLAST analysis revealed that the closest relative was Okra yellow mosaic Mexico virus - Tetekalitla (OkYMMV) (GenBank Accession No. EF591631) at 98%. To amplify the full-length DNA-A and a possible cognate DNA-B component, one plant that was positive by CP-PCR and DNA sequencing was selected for further analysis. Total DNA from this plant was used as template for a second detection method that consisted of rolling circle amplification (RCA) using the TempliPhi 100 Amplification System (GE Healthcare). RCA is a non-sequence-specific approach that permits amplification of circular DNA. The RCA products were linearized to release unit length ~2.6 kb DNA-A and DNA-B components using BamHI, and EcoRI, respectively. These products were cloned into pGEM3zf+ (Promega) and sequenced using M13F and M13 R primers and then by primer walking (>300 base overlap). Full-length DNA-A and DNA-B components were obtained, respectively, at 2,613 bp (GenBank Accession No. HM035059) and 2,594 bp (GenBank Accession No HM035060). Alignment of the DNA-A component using ClustalV (MegAlign, DNASTAR, Madison, WI) with begomoviral sequences available in GenBank indicated that it was 99% identical to OkYMMV DNA-A (GenBank Accession No. DQ022611). The closest relative to the DNA-B component (ClustalV) was Sida golden mosaic virus (SiGMV) (GenBank Accession No. AJ250731) at 73%. The nt identity of the 172-nt ‘common region’ present in the DNA-A and DNA-B components was 99%, and the iterons (predicted Rep binding motif) were identical for the two components, indicating that they are a cognate pair. The genome organization was typical of other New World bipartite begomoviruses. The economic losses due to infection by this virus could not be determined because an early freeze killed the plants. Hidalgo County is adjacent to Tamaulipas, Mexico, where ~50 km2 of okra are grown and the whitefly vector is also present. The identification of OkYMMV based on two independent detection methods, and the presence of begomovirus-like symptoms together with the whitefly vector, provide robust evidence for the association of OkYMMV-TX with diseased okra plants. To our knowledge, this is the first report of OkYMMV-TX infecting okra crops in Texas and in the continental United States. References: (1) J. J. Doyle and J. L. Doyle. Focus 12:13, 1990. (2) S. Wyatt and J. K. Brown. Phytopathology 86:1288, 1996.

scholarly journals First Report of Fusarium proliferatum Causing Root Rot on Soybean (Glycine max) in the United States

Plant Disease ◽  
2011 ◽  
Vol 95 (10) ◽  
pp. 1316-1316 ◽  
Author(s):  
M. M. Díaz Arias ◽  
G. P. Munkvold ◽  
L. F. Leandro
Keyword(s):  
United States ◽  
Root Rot ◽  
Morphological Characteristics ◽  
Dry Weight ◽  
The United States ◽  
Growth Stages ◽  
Species Identity ◽  
First Report ◽  
Soybean Roots ◽  
Soybean Diseases

Fusarium spp. are widespread soilborne pathogens that cause important soybean diseases such as damping-off, root rot, Fusarium wilt, and sudden death syndrome. At least 12 species of Fusarium, including F. proliferatum, have been associated with soybean roots, but their relative aggressiveness as root rot pathogens is not known and pathogenicity has not been established for all reported species (2). In collaboration with 12 Iowa State University extension specialists, soybean roots were arbitrarily sampled from three fields in each of 98 Iowa counties from 2007 to 2009. Ten plants were collected from each field at V2-V3 and R3-R4 growth stages (2). Typical symptoms of Fusarium root rot (2) were observed. Symptomatic and asymptomatic root pieces were superficially sterilized in 0.5% NaOCl for 2 min, rinsed three times in sterile distilled water, and placed onto a Fusarium selective medium. Fusarium colonies were transferred to carnation leaf agar (CLA) and potato dextrose agar and later identified to species based on cultural and morphological characteristics. Of 1,230 Fusarium isolates identified, 50 were recognized as F. proliferatum based on morphological characteristics (3). F. proliferatum isolates produced abundant, aerial, white mycelium and a violet-to-dark purple pigmentation characteristic of Fusarium section Liseola. On CLA, microconidia were abundant, single celled, oval, and in chains on monophialides and polyphialides (3). Species identity was confirmed for two isolates by sequencing of the elongation factor (EF1-α) gene using the ef1 and ef2 primers (1). Identities of the resulting sequences (~680 bp) were confirmed by BLAST analysis and the FUSARIUM-ID database. Analysis resulted in a 99% match for five accessions of F. proliferatum (e.g., FD01389 and FD01858). To complete Koch's postulates, four F. proliferatum isolates were tested for pathogenicity on soybean in a greenhouse. Soybean seeds of cv. AG2306 were planted in cones (150 ml) in autoclaved soil infested with each isolate; Fusarium inoculum was applied by mixing an infested cornmeal/sand mix with soil prior to planting (4). Noninoculated control plants were grown in autoclaved soil amended with a sterile cornmeal/sand mix. Soil temperature was maintained at 18 ± 1°C by placing cones in water baths. The experiment was a completely randomized design with five replicates (single plant in a cone) per isolate and was repeated three times. Root rot severity (visually scored on a percentage scale), shoot dry weight, and root dry weight were assessed at the V3 soybean growth stage. All F. proliferatum isolates tested were pathogenic. Plants inoculated with these isolates were significantly different from the control plants in root rot severity (P = 0.001) and shoot (P = 0.023) and root (P = 0.013) dry weight. Infected plants showed dark brown lesions in the root system as well as decay of the entire taproot. F. proliferatum was reisolated from symptomatic root tissue of infected plants but not from similar tissues of control plants. To our knowledge, this is the first report of F. proliferatum causing root rot on soybean in the United States. References: (1) D. M. Geiser et al. Eur. J. Plant Pathol. 110:473, 2004. (2) G. L. Hartman et al. Compendium of Soybean Diseases. 4th ed. The American Phytopathologic Society, St. Paul, MN, 1999. (3) J. F. Leslie and B. A. Summerell. The Fusarium Laboratory Manual. Blackwell Publishing, Oxford, UK, 2006. (4) G. P. Munkvold and J. K. O'Mara. Plant Dis. 86:143, 2002.

Mapping Regional Landscape by Using OpenstreetMap (OSM)

2019 ◽  
pp. 771-790 ◽  
Author(s):  
Di Yang
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Forest Cover ◽  
Regional Scale ◽  
Google Earth ◽  
Computing Power ◽  
New Approach ◽  
Computation Cost ◽  
Regional Landscape ◽  
Google Earth Engine

A forest patterns map over a large extent at high spatial resolution is a heavily computation task but is critical to most regions. There are two major difficulties in generating the classification maps at regional scale: large training points sets and expensive computation cost in classifier modelling. As one of the most well-known Volunteered Geographic Information (VGI) initiatives, OpenstreetMap contributes not only on road network distributions, but the potential of justify land cover and land use. Google Earth Engine is a platform designed for cloud-based mapping with a strong computing power. In this study, we proposed a new approach to generating forest cover map and quantifying road-caused forest fragmentations by using OpenstreetMap in conjunction with remote sensing dataset stored in Google Earth Engine. Additionally, the landscape metrics produced after incorporating OpenStreetMap (OSM) with the forest spatial pattern layers from our output indicated significant levels of forest fragmentation in Yucatan peninsula.

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