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Journal ArticleDOI

Automated cropland mapping of continental Africa using Google Earth Engine cloud computing

TL;DR: In this article, an automated cropland mapping algorithm (ACMA) was used to capture extensive knowledge on the croplands of Africa available through ground-based training samples, very high (sub-meter to five-meter) resolution imagery (VHRI), and local knowledge captured during field visits and/or sourced from country reports and literature.
Abstract: The automation of agricultural mapping using satellite-derived remotely sensed data remains a challenge in Africa because of the heterogeneous and fragmental landscape, complex crop cycles, and limited access to local knowledge. Currently, consistent, continent-wide routine cropland mapping of Africa does not exist, with most studies focused either on certain portions of the continent or at most a one-time effort at mapping the continent at coarse resolution remote sensing. In this research, we addressed these limitations by applying an automated cropland mapping algorithm (ACMA) that captures extensive knowledge on the croplands of Africa available through: (a) ground-based training samples, (b) very high (sub-meter to five-meter) resolution imagery (VHRI), and (c) local knowledge captured during field visits and/or sourced from country reports and literature. The study used 16-day time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) composited data at 250-m resolution for the entire African continent. Based on these data, the study first produced accurate reference cropland layers or RCLs (cropland extent/areas, irrigation versus rainfed, cropping intensities, crop dominance, and croplands versus cropland fallows) for the year 2014 that provided an overall accuracy of around 90% for crop extent in different agro-ecological zones (AEZs). The RCLs for the year 2014 (RCL2014) were then used in the development of the ACMA algorithm to create ACMA-derived cropland layers for 2014 (ACL2014). ACL2014 when compared pixel-by-pixel with the RCL2014 had an overall similarity greater than 95%. Based on the ACL2014, the African continent had 296 Mha of net cropland areas (260 Mha cultivated plus 36 Mha fallows) and 330 Mha of gross cropland areas. Of the 260 Mha of net cropland areas cultivated during 2014, 90.6% (236 Mha) was rainfed and just 9.4% (24 Mha) was irrigated. Africa has about 15% of the world’s population, but only about 6% of world’s irrigation. Net cropland area distribution was 95 Mha during season 1, 117 Mha during season 2, and 84 Mha continuous. About 58% of the rainfed and 39% of the irrigated were single crops (net cropland area without cropland fallows) cropped during either season 1 (January-May) or season 2 (June-September). The ACMA algorithm was deployed on Google Earth Engine (GEE) cloud computing platform and applied on MODIS time-series data from 2003 through 2014 to obtain ACMA-derived cropland layers for these years (ACL2003 to ACL2014). The results indicated that over these twelve years, on average: (a) croplands increased by 1 Mha/yr, and (b) cropland fallows decreased by 1 Mha/year. Cropland areas computed from ACL2014 for the 55 African countries were largely underestimated when compared with an independent source of census-based cropland data, with a root-mean-square error (RMSE) of 3.5 Mha. ACMA demonstrated the ability to hind-cast (past years), now-cast (present year), and forecast (future years) cropland products using MODIS 250-m time-series data rapidly, but currently, insufficient reference data exist to rigorously report trends from these results.
Citations
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Journal ArticleDOI
TL;DR: In this paper, the authors present the agronomical variables and plant traits that can be estimated by remote sensing, and describe the empirical and deterministic approaches to retrieve them, and provide a synthesis of the emerging opportunities that should strengthen the role of remote sensing in providing operational, efficient and long-term services for agricultural applications.

631 citations

Journal ArticleDOI
TL;DR: Object-based time-weighted dynamic time warping (TWDTW) method achieved comparable classification results to RF in Romania and Italy, but RF achieved better results in the USA, where the classified crops present high intra-class spectral variability.

556 citations


Cites background from "Automated cropland mapping of conti..."

  • ...There are various studies, using supervised or unsupervised algorithms, dedicated to cropland mapping from time series or single-date remote sensing images (Petitjean et al., 2012b; Xiong et al., 2017; Yan and Roy, 2015)....

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  • ...There are various studies, using supervised or unsupervised algorithms, dedicated to cropland mapping from time series or single-date remote sensing images (Petitjean et al., 2012b; Xiong et al., 2017; Yan and Roy, 2015)....

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Journal ArticleDOI
TL;DR: A meta-analysis investigation of recent peer-reviewed GEE articles focusing on several features, including data, sensor type, study area, spatial resolution, application, strategy, and analytical methods confirmed that GEE has and continues to make substantive progress on global challenges involving process of geo-big data.
Abstract: Google Earth Engine (GEE) is a cloud-based geospatial processing platform for large-scale environmental monitoring and analysis. The free-to-use GEE platform provides access to (1) petabytes of publicly available remote sensing imagery and other ready-to-use products with an explorer web app; (2) high-speed parallel processing and machine learning algorithms using Google’s computational infrastructure; and (3) a library of Application Programming Interfaces (APIs) with development environments that support popular coding languages, such as JavaScript and Python. Together these core features enable users to discover, analyze and visualize geospatial big data in powerful ways without needing access to supercomputers or specialized coding expertise. The development of GEE has created much enthusiasm and engagement in the remote sensing and geospatial data science fields. Yet after a decade since GEE was launched, its impact on remote sensing and geospatial science has not been carefully explored. Thus, a systematic review of GEE that can provide readers with the “big picture” of the current status and general trends in GEE is needed. To this end, the decision was taken to perform a meta-analysis investigation of recent peer-reviewed GEE articles focusing on several features, including data, sensor type, study area, spatial resolution, application, strategy, and analytical methods. A total of 349 peer-reviewed articles published in 146 different journals between 2010 and October 2019 were reviewed. Publications and geographical distribution trends showed a broad spectrum of applications in environmental analyses at both regional and global scales. Remote sensing datasets were used in 90% of studies while 10% of the articles utilized ready-to-use products for analyses. Optical satellite imagery with medium spatial resolution, particularly Landsat data with an archive exceeding 40 years, has been used extensively. Linear regression and random forest were the most frequently used algorithms for satellite imagery processing. Among ready-to-use products, the normalized difference vegetation index (NDVI) was used in 27% of studies for vegetation, crop, land cover mapping and drought monitoring. The results of this study confirm that GEE has and continues to make substantive progress on global challenges involving process of geo-big data.

438 citations

Journal ArticleDOI
TL;DR: Analysis of published literature showed that a total of 300 journal papers were published between 2011 and June 2017 that used GEE in their research, spread across 158 journals; Landsat was the most widely used dataset; it is the biggest component of the GEE data portal.
Abstract: The Google Earth Engine (GEE) portal provides enhanced opportunities for undertaking earth observation studies Established towards the end of 2010, it provides access to satellite and other ancillary data, cloud computing, and algorithms for processing large amounts of data with relative ease However, the uptake and usage of the opportunity remains varied and unclear This study was undertaken to investigate the usage patterns of the Google Earth Engine platform and whether researchers in developing countries were making use of the opportunity Analysis of published literature showed that a total of 300 journal papers were published between 2011 and June 2017 that used GEE in their research, spread across 158 journals The highest number of papers were in the journal Remote Sensing, followed by Remote Sensing of Environment There were also a number of papers in premium journals such as Nature and Science The application areas were quite varied, ranging from forest and vegetation studies to medical fields such as malaria Landsat was the most widely used dataset; it is the biggest component of the GEE data portal, with data from the first to the current Landsat series available for use and download Examination of data also showed that the usage was dominated by institutions based in developed nations, with study sites mainly in developed nations There were very few studies originating from institutions based in less developed nations and those that targeted less developed nations, particularly in the African continent

350 citations


Cites methods from "Automated cropland mapping of conti..."

  • ...[20], undertaken to automate cropland mapping of continental Africa using GEE, shows the potential of the platforms application for the African continent....

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Journal ArticleDOI
TL;DR: In this paper, a pixel-based supervised random forest (RF) machine learning algorithm (MLA) was used on the Google Earth Engine (GEE) cloud computing platform.
Abstract: Mapping high resolution (30-m or better) cropland extent over very large areas such as continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of great importance for addressing the global food and water security challenges. Such cropland extent products capture individual farm fields, small or large, and are crucial for developing accurate higher-level cropland products such as cropping intensities, crop types, crop watering methods (irrigated or rainfed), crop productivity, and crop water productivity. It also brings many challenges that include handling massively large data volumes, computing power, and collecting resource intensive reference training and validation data over complex geographic and political boundaries. Thereby, this study developed a precise and accurate Landsat 30-m derived cropland extent product for two very important, distinct, diverse, and large countries: Australia and China. The study used of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR1, and NDVI) of Landsat-8 every 16-day Operational Land Imager (OLI) data for the years 2013–2015. The classification was performed by using a pixel-based supervised random forest (RF) machine learning algorithm (MLA) executed on the Google Earth Engine (GEE) cloud computing platform. Each band was time-composited over 4–6 time-periods over a year using median value for various agro-ecological zones (AEZs) of Australia and China. This resulted in a 32–48-layer mega-file data-cube (MFDC) for each of the AEZs. Reference training and validation data were gathered from: (a) field visits, (b) sub-meter to 5-m very high spatial resolution imagery (VHRI) data, and (c) ancillary sources such as from the National agriculture bureaus. Croplands versus non-croplands knowledge base for training the RF algorithm were derived from MFDC using 958 reference-training samples for Australia and 2130 reference-training samples for China. The resulting 30-m cropland extent product was assessed for accuracies using independent validation samples: 900 for Australia and 1972 for China. The 30-m cropland extent product of Australia showed an overall accuracy of 97.6% with a producer’s accuracy of 98.8% (errors of omissions = 1.2%), and user’s accuracy of 79% (errors of commissions = 21%) for the cropland class. For China, overall accuracies were 94% with a producer’s accuracy of 80% (errors of omissions = 20%), and user’s accuracy of 84.2% (errors of commissions = 15.8%) for cropland class. Total cropland areas of Australia were estimated as 35.1 million hectares and 165.2 million hectares for China. These estimates were higher by 8.6% for Australia and 3.9% for China when compared with the traditionally derived national statistics. The cropland extent product further demonstrated the ability to estimate sub-national cropland areas accurately by providing an R2 value of 0.85 when compared with province-wise cropland areas of China. The study provides a paradigm-shift on how cropland maps are produced using multi-date remote sensing. These products can be browsed at www.croplands.org and made available for download at NASA’s Land Processes Distributed Active Archive Center (LP DAAC) https://www.lpdaac.usgs.gov/node/1282.

289 citations

References
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Journal ArticleDOI
15 Nov 2013-Science
TL;DR: Intensive forestry practiced within subtropical forests resulted in the highest rates of forest change globally, and boreal forest loss due largely to fire and forestry was second to that in the tropics in absolute and proportional terms.
Abstract: Quantification of global forest change has been lacking despite the recognized importance of forest ecosystem services. In this study, Earth observation satellite data were used to map global forest loss (2.3 million square kilometers) and gain (0.8 million square kilometers) from 2000 to 2012 at a spatial resolution of 30 meters. The tropics were the only climate domain to exhibit a trend, with forest loss increasing by 2101 square kilometers per year. Brazil's well-documented reduction in deforestation was offset by increasing forest loss in Indonesia, Malaysia, Paraguay, Bolivia, Zambia, Angola, and elsewhere. Intensive forestry practiced within subtropical forests resulted in the highest rates of forest change globally. Boreal forest loss due largely to fire and forestry was second to that in the tropics in absolute and proportional terms. These results depict a globally consistent and locally relevant record of forest change.

7,890 citations

BookDOI
17 Sep 1998
TL;DR: This chapter discusses Accuracy Assessment, which examines the impact of sample design on cost, statistical Validity, and measuring Variability in the context of data collection and analysis.
Abstract: Introduction Why Accuracy Assessment? Overview Historical Review Aerial Photography Digital Assessments Data Collection Considerations Classification Scheme Statistical Considerations Data Distribution Randomness Spatial Autocorrelation Sample Size Sampling Scheme Sample Unit Reference Data Collection Basic Collection Forms Basic Analysis Techniques Non-Site Specific Assessments Site Specific Assessments Area Estimation/Correction Practicals Impact of Sample Design on Cost Recommendations for Collecting Reference Data ASources of Variation in Reference Data Photo Interpretation vs. Ground Visitation Interpreter Variability Observations vs. Measurements What is Correct? Labeling Map vs. Labeling the Reference Data Qualitative vs. Quantitative Analysis Local vs. Regional vs. Global Assessments Advanced Topics Beyond the Error Matrix Modifying the Error Matrix Fuzzy Set Theory Measuring Variability Complex Data Sets Change Detection Multi-Layer Assessments California Hardwood Rangeland Monitoring Project Case Study Balancing Statistical Validity with Practical Reality Bibliography

4,586 citations


"Automated cropland mapping of conti..." refers background or methods in this paper

  • ...…algorithm (ACMA) coded and development for the irrigated and A generated cropland layer for the year 2014 (RCL2014) replicating ACMA generated t years and validated. is required to be collected at appropriate spatial and temporal scales to perform independent validation (Congalton and Green, 2009)....

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  • ...is required to be collected at appropriate spatial and temporal scales to perform independent validation (Congalton and Green, 2009)....

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Journal ArticleDOI
01 Feb 2008-Science
TL;DR: Results indicate South Asia and Southern Africa as two regions that, without sufficient adaptation measures, will likely suffer negative impacts on several crops that are important to large food-insecure human populations.
Abstract: Investments aimed at improving agricultural adaptation to climate change inevitably favor some crops and regions over others. An analysis of climate risks for crops in 12 food-insecure regions was conducted to identify adaptation priorities, based on statistical crop models and climate projections for 2030 from 20 general circulation models. Results indicate South Asia and Southern Africa as two regions that, without sufficient adaptation measures, will likely suffer negative impacts on several crops that are important to large food-insecure human populations. We also find that uncertainties vary widely by crop, and therefore priorities will depend on the risk attitudes of investment institutions.

2,820 citations


"Automated cropland mapping of conti..." refers background in this paper

  • ...Southern Africa will be one of the regions in the world whose crop production is most affected by climate change such as higher temperatures and reduced water supplies, along with other factors like biodiversity loss and ecosystems degradation (Lobell et al., 2008)....

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Journal ArticleDOI
TL;DR: This paper reviews remote sensing implementations of support vector machines (SVMs), a promising machine learning methodology that is particularly appealing in the remote sensing field due to their ability to generalize well even with limited training samples.
Abstract: A wide range of methods for analysis of airborne- and satellite-derived imagery continues to be proposed and assessed. In this paper, we review remote sensing implementations of support vector machines (SVMs), a promising machine learning methodology. This review is timely due to the exponentially increasing number of works published in recent years. SVMs are particularly appealing in the remote sensing field due to their ability to generalize well even with limited training samples, a common limitation for remote sensing applications. However, they also suffer from parameter assignment issues that can significantly affect obtained results. A summary of empirical results is provided for various applications of over one hundred published works (as of April, 2010). It is our hope that this survey will provide guidelines for future applications of SVMs and possible areas of algorithm enhancement.

2,546 citations


"Automated cropland mapping of conti..." refers background or methods in this paper

  • ..., 2010), support vector machines (Mountrakis et al., 2011), random forest algorithm (Tatsumi et al....

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  • ...…and Lunetta, 2012), Fourier harmonic analysis (Zhang et al., 2015), spectral matching techniques (Dheeravath et al., 2010), support vector machines (Mountrakis et al., 2011), random forest algorithm (Tatsumi et al., 2015) and a number of other machine learning algorithms (DeFries, 2000; Duro et…...

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Journal ArticleDOI
TL;DR: This product provides maps of global land cover at 1-km spatial resolution using several classification systems, principally that of the IGBP, and a supervised classification methodology is used that exploits a global database of training sites interpreted from high-resolution imagery in association with ancillary data.

2,379 citations


"Automated cropland mapping of conti..." refers background in this paper

  • ..., 2014), and MODIS (Moderate Resolution Imaging Spectroradiometer) Land Cover (Friedl et al., 2002) do have cropland classes....

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  • ...Global land use/land cover (LULC) products such as global land cover 2000 (Giri et al., 2005), GlobCover 2005/2009 (Arino et al., 2007), Global Land Cover-SHARE (Latham et al., 2014), and MODIS (Moderate Resolution Imaging Spectroradiometer) Land Cover (Friedl et al., 2002) do have cropland classes....

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