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18
Harmonizing and Combining Existing Land Cover/Land Use Datasets for Cropland Area Monitoring at the African Continental Scale
, 2012
"... Abstract: Mapping cropland areas is of great interest in diverse fields, from crop monitoring to climate change and food security. Recognizing the value of a reliable and harmonized crop mask that entirely covers the African continent, the objectives of this study were to (i) consolidate the best ex ..."
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Abstract: Mapping cropland areas is of great interest in diverse fields, from crop monitoring to climate change and food security. Recognizing the value of a reliable and harmonized crop mask that entirely covers the African continent, the objectives of this study were to (i) consolidate the best existing land cover/land use datasets, (ii) adapt the Land Cover Classification System (LCCS) for harmonization, (iii) assess the final product, and (iv) compare the final product with two existing datasets. Ten datasets were compared and combined through an expert-based approach in order to create the derived map of cropland areas at 250 m covering the whole of Africa. The resulting cropland mask was compared with two recent cropland extent maps at 1 km: one derived from MODIS and one derived from five existing products. The accuracy of the three products was assessed against a validation sample of 3,591 pixels of 1km regularly distributed over Africa and interpreted using high resolution images, which were collected using the Geo-Wiki tool. The comparison of the resulting crop mask with existing products shows that it has a greater agreement with the expert validation dataset, in particular for places where the
An automated cropland classification algorithm (ACCA) by combining MODIS, Landsat, and Secondary Data for the State of California. Photogramm. Eng. Remote Sensing 2012
"... Abstract: The overarching goal of this research was to develop and demonstrate an automated Cropland Classification Algorithm (ACCA) that will rapidly, routinely, and accurately classify agricultural cropland extent, areas, and characteristics (e.g., irrigated vs. rainfed) over large areas such as a ..."
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Abstract: The overarching goal of this research was to develop and demonstrate an automated Cropland Classification Algorithm (ACCA) that will rapidly, routinely, and accurately classify agricultural cropland extent, areas, and characteristics (e.g., irrigated vs. rainfed) over large areas such as a country or a region through combination of multi-sensor remote sensing and secondary data. In this research, a rule-based ACCA was conceptualized, developed, and demonstrated for the country of Tajikistan using mega file data cubes (MFDCs) involving data from Landsat Global Land Survey (GLS), Landsat Enhanced Thematic Mapper Plus (ETM+) 30 m, Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m time-series, a suite of secondary data (e.g., elevation, slope, precipitation, temperature), and in situ data. First, the process involved producing an accurate reference (or truth) cropland layer (TCL), consisting of cropland extent, areas, and irrigated vs. rainfed cropland areas, for the entire country of Tajikistan based on MFDC of year 2005 (MFDC2005). The methods involved in producing TCL included using ISOCLASS clustering, Tasseled Cap bi-spectral plots, spectro-temporal characteristics from MODIS 250 m monthly normalized difference vegetation index (NDVI) maximum value composites
Mapping Rubber Plantations and Natural Forests in Xishuangbanna (Southwest China) Using Multi-Spectral Phenological Metrics from MODIS Time Series
, 2013
"... Abstract: We developed and evaluated a new approach for mapping rubber plantations and natural forests in one of Southeast Asia’s biodiversity hot spots, Xishuangbanna in China. We used a one-year annual time series of Moderate Resolution Imaging Spectroradiometer (MODIS), Enhanced Vegetation Index ..."
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Abstract: We developed and evaluated a new approach for mapping rubber plantations and natural forests in one of Southeast Asia’s biodiversity hot spots, Xishuangbanna in China. We used a one-year annual time series of Moderate Resolution Imaging Spectroradiometer (MODIS), Enhanced Vegetation Index (EVI) and short-wave infrared (SWIR) reflectance data to develop phenological metrics. These phenological metrics were used to classify rubber plantations and forests with the Random Forest classification algorithm. We evaluated which key phenological characteristics were important to discriminate rubber plantations and natural forests by estimating the influence of each metric on the classification accuracy. As a benchmark, we compared the best classification with a classification based on the full, fitted time series data. Overall classification accuracies derived from EVI and SWIR time series alone were 64.4 % and 67.9%, respectively. Combining the phenological metrics from EVI and SWIR time series improved the accuracy to 73.5%. Using the full, smoothed time series data instead of metrics derived from the time series improved the overall accuracy only slightly (1.3%), indicating that the phenological metrics were sufficient to explain the seasonal changes captured by the MODIS time series. The results demonstrate a promising utility of phenological metrics for mapping and monitoring rubber expansion with MODIS. Remote Sens. 2013, 5 2796
Exploring Patterns and Effects of Aerosol Quantity Flag Anomalies in MODIS Surface Reflectance Products in the Tropics
, 2013
"... supplying a continuous data stream since 2000, lending to detailed time series analysis of the global terrestrial environment. This paper explores a quality anomaly present in the tropics relating to the aerosol quantity flag in the daily MODIS surface reflectance products (MOD09 series) and the 16- ..."
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supplying a continuous data stream since 2000, lending to detailed time series analysis of the global terrestrial environment. This paper explores a quality anomaly present in the tropics relating to the aerosol quantity flag in the daily MODIS surface reflectance products (MOD09 series) and the 16-day Vegetation Index (VI) composite products (MOD13 series) derived from the daily observations. While the anomaly is to some extent a known issue reported by the MODIS Land Quality Assessment group, very little is known about the scale of the issue, the nature and patterns of its occurrence, and potential consequences for data analysis, which explains why it is not adequately recognized throughout the literature. Two tropical regions were used to explore the anomaly and demonstrate the effects it has on the quality of selected MODIS products—one in the South American Amazon, the other in mainland Southeast Asia. The origins of the anomaly are described qualitatively in detail, and quantitative estimates of affected evergreen forest area in the MOD13A1 time series are made using blue band thresholding. The anomaly originates in the 1 km State dataset, whereby, under certain conditions, high aerosol quantity pixels are given a low aerosol quantity label, resulting in poor quality pixels with
Remote Sensing of Irrigated Agriculture: Opportunities and Challenges
, 2010
"... Abstract: Over the last several decades, remote sensing has emerged as an effective tool to monitor irrigated lands over a variety of climatic conditions and locations. The objective of this review, which summarizes the methods and the results of existing remote sensing studies, is to synthesize pri ..."
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Abstract: Over the last several decades, remote sensing has emerged as an effective tool to monitor irrigated lands over a variety of climatic conditions and locations. The objective of this review, which summarizes the methods and the results of existing remote sensing studies, is to synthesize principle findings and assess the state of the art. We take a taxonomic approach to group studies based on location, scale, inputs, and methods, in an effort to categorize different approaches within a logical framework. We seek to evaluate the ability of remote sensing to provide synoptic and timely coverage of irrigated lands in several spectral regions. We also investigate the value of archived data that enable comparison of images through time. This overview of the studies to date indicates that remote sensing-based monitoring of irrigation is at an intermediate stage of development at local scales. For instance, there is overwhelming consensus on the efficacy of vegetation indices in identifying irrigated fields. Also, single date imagery, acquired at peak growing season, may suffice to identify irrigated lands, although to multi-date image data are necessary for improved classification and to distinguish different crop types. At local scales, the mapping of irrigated lands with remote sensing is also strongly affected by the
Part of the Earth Sciences Commons, Environmental Sciences Commons, and the Oceanography and Atmospheric Sciences and Meteorology Commons Authors
, 2014
"... Comparing cropland net primary production estimates frominventory, a satellite-based model, and a process-based model in theMidwest of the United States ..."
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Comparing cropland net primary production estimates frominventory, a satellite-based model, and a process-based model in theMidwest of the United States
Earth Syst. Sci. Data, 6, 339–352, 2014
, 2014
"... www.earth-syst-sci-data.net/6/339/2014/ doi:10.5194/essd-6-339-2014 © Author(s) 2014. CC Attribution 3.0 License. Deriving a per-field land use and land cover map in an agricultural mosaic catchment ..."
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www.earth-syst-sci-data.net/6/339/2014/ doi:10.5194/essd-6-339-2014 © Author(s) 2014. CC Attribution 3.0 License. Deriving a per-field land use and land cover map in an agricultural mosaic catchment
REGIONAL SCALE CROP MAPPING USING MULTI-TEMPORAL SATELLITE IMAGERY
"... One of the problems in dealing with optical images for large territories (more than 10,000 sq. km) is the presence of clouds and shadows that result in having missing values in data sets. In this paper, a new approach to classification of multi-temporal optical satellite imagery with missing data du ..."
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One of the problems in dealing with optical images for large territories (more than 10,000 sq. km) is the presence of clouds and shadows that result in having missing values in data sets. In this paper, a new approach to classification of multi-temporal optical satellite imagery with missing data due to clouds and shadows is proposed. First, self-organizing Kohonen maps (SOMs) are used to restore missing pixel values in a time series of satellite imagery. SOMs are trained for each spectral band separately using non-missing values. Missing values are restored through a special procedure that substitutes input sample's missing components with neuron's weight coefficients. After missing data restoration, a supervised classification is performed for multi-temporal satellite images. An ensemble of neural networks, in particular multilayer perceptrons (MLPs), is proposed. Ensembling of neural networks is done by the technique of average committee, i.e. to calculate the average class probability over classifiers and select the class with the highest average posterior probability for the given input sample. The proposed approach is applied for regional scale crop classification using multi temporal Landsat-8 images for the JECAM test site in Ukraine in 2013. It is shown that ensemble of MLPs provides better performance than a single neural network in terms of overall classification accuracy, kappa coefficient, and producer's and user's accuracies for separate classes. The overall accuracy more than 85 % is achieved. The obtained classification map is also validated through estimated crop areas and comparison to official statistics.
Article A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM)
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