Results 1 - 10
of
26
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 ..."
Abstract
-
Cited by 7 (2 self)
- Add to MetaCart
(Show Context)
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
Exploring the use of MODIS NDVI-based phenology indicators for classifying forest general habitat categories. Remote Sens
"... Abstract: The cost effective monitoring of habitats and their biodiversity remains a challenge to date. Earth Observation (EO) has a key role to play in mapping habitat and biodiversity in general, providing tools for the systematic collection of environmental data. The recent GEO-BON European Biodi ..."
Abstract
-
Cited by 6 (0 self)
- Add to MetaCart
(Show Context)
Abstract: The cost effective monitoring of habitats and their biodiversity remains a challenge to date. Earth Observation (EO) has a key role to play in mapping habitat and biodiversity in general, providing tools for the systematic collection of environmental data. The recent GEO-BON European Biodiversity Observation Network project (EBONE) established a framework for an integrated biodiversity monitoring system. Underlying this framework is the idea of integrating in situ with EO and a habitat classification scheme based on General Habitat Categories (GHC), designed with an Earth Observation-perspective. Here we report on EBONE work that explored the use of NDVI-derived phenology metrics for the identification and mapping of Forest GHCs. Thirty-one phenology metrics were extracted from MODIS NDVI time series for Europe. Classifications to discriminate forest types were performed based on a Random Forests ™ classifier in selected regions. Results indicate that date phenology metrics are generally more significant for forest type discrimination. The achieved class accuracies are generally not satisfactory, except for coniferous forests in homogeneous stands (77–82%). The main causes of low classification accuracies were identified as (i) the spatial resolution of the imagery (250 m) which led to
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 ..."
Abstract
-
Cited by 5 (0 self)
- Add to MetaCart
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
Remote sensing and US crop insurance program integrity: Data mining satellite and agricultural data
- WIT Trans. Inf. Commun. Technol
"... The objective of this investigation is to (1) integrate remote sensing data into an existing data warehouse of the US crop insurance program 1990 to 2007, (2) test remote sensing correlations with crop production, and (3) use remotely sensed time series data to assess variation in crop production. P ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
The objective of this investigation is to (1) integrate remote sensing data into an existing data warehouse of the US crop insurance program 1990 to 2007, (2) test remote sensing correlations with crop production, and (3) use remotely sensed time series data to assess variation in crop production. Previously (2000 to 2007) data mining of the data warehouse was based upon probabilistic and algorithmic approaches to identification of possible fraud, waste, or abuse. The value of adding satellite data warehouse to data mining resources is provision of (1) an additional empirical metric, (2) objective data on vegetative health, (3) measurable metrics for capturing the variance of plant health, and (4) a means for measuring the covariance of location and production. Refinement of data mining through the addition of satellite data for routine use to reduce fraud, waste, and abuse will ultimately diminish the frequency of false positives.
Mapping and spatial analysis of the soybean agricultural frontier in Mato Grosso, Brazil, using remote sensing data
"... Abstract The main pioneer frontier considered by geographers is the Amazonian pioneer frontier. The occupation of the Brazilian territory has been carried out through successive economic cycles. Currently, the expansion of soybean crops in Amazonia is considered as the last economic cycle involving ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract The main pioneer frontier considered by geographers is the Amazonian pioneer frontier. The occupation of the Brazilian territory has been carried out through successive economic cycles. Currently, the expansion of soybean crops in Amazonia is considered as the last economic cycle involving new migrations to still unexplored areas. Mapping this frontier is necessary in order to better understand its drivers and think about efficient land use policies to struggle its progress. In this paper, we propose an innovative methodology for mapping the agricultural frontier in the Amazonian state of Mato Grosso (Brazil) using satellite data acquired during the 2000–2006 period. We assume that the frontier evolves through successive land-use stages such as
SEE PROFILE
, 2013
"... Mapping and spatial analysis of the soybean agricultural frontier in Mato Grosso, Brazil, using remote sensing data ..."
Abstract
- Add to MetaCart
(Show Context)
Mapping and spatial analysis of the soybean agricultural frontier in Mato Grosso, Brazil, using remote sensing data
JUTE AND TEA DISCRIMINATION THROUGH FUSION OF SAR AND OPTICAL DATA
"... Abstract—Remote sensing approaches based on both optical and microwave region of EM spectra have been widely adapted for large scale crop monitoring and condition assessment. Visible, infrared and microwave wavelengths are sensitive to different crop characteristics, thus data from optical and radar ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract—Remote sensing approaches based on both optical and microwave region of EM spectra have been widely adapted for large scale crop monitoring and condition assessment. Visible, infrared and microwave wavelengths are sensitive to different crop characteristics, thus data from optical and radar sensors are complementary. Synthetic Aperture Radar (SAR) responds to the large scale crop structure (size, shape and orientation of leaves, stalks, and fruits) and the dielectric properties of the crop canopy. Research is needed to assess the saturation effects of SAR data and to investigate the synergy between the optical and SAR imagery for exploring various dimensions of crop growth which is not possible with any one of them singly with higher degree of accuracy. An attempt has been made to study the potential of SAR and optical data individually and by fusing them to separate various landcover classes. Two-date and three-date SAR data could distinguish jute and tea crop with 70–85 % accuracy, while cloud free
Abstract Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains
, 2006
"... The global environmental change research community requires improved and up-to-date land use/land cover (LULC) datasets at regional to global scales to support a variety of science and policy applications. Considerable strides have been made to improve large-area LULC datasets, but little emphasis h ..."
Abstract
- Add to MetaCart
(Show Context)
The global environmental change research community requires improved and up-to-date land use/land cover (LULC) datasets at regional to global scales to support a variety of science and policy applications. Considerable strides have been made to improve large-area LULC datasets, but little emphasis has been placed on thematically detailed crop mapping, despite the considerable influence of management activities in the cropland sector on various environmental processes and the economy. Time-series MODIS 250 m Vegetation Index (VI) datasets hold considerable promise for large-area crop mapping in an agriculturally intensive region such as the U.S. Central Great Plains, given their global coverage, intermediate spatial resolution, high temporal resolution (16-day composite period), and cost-free status. However, the specific spectral– temporal information contained in these data has yet to be thoroughly explored and their applicability for large-area crop-related LULC classification is relatively unknown. The objective of this research was to investigate the general applicability of the time-series MODIS 250 m Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) datasets for crop-related LULC classification in this region. A combination of graphical and statistical analyses were performed on a 12-month time-series of MODIS EVI and NDVI data from more than 2000 cropped field sites across the U.S. state of Kansas. Both MODIS VI datasets were found to have sufficient spatial, spectral, and temporal resolutions to detect unique multi-temporal signatures for each of the region's major crop types (alfalfa, corn, sorghum, soybeans, and winter wheat) and management practices (double crop, fallow, and irrigation). Each crop's multi-temporal VI signature was consistent with its general
SERIES FROM MODIS
"... Globally acquired data from both MODIS instruments are suitable for science quality time series, because the unique concept of pixel-level quality information of each MODIS land product allows a detailed analysis of the data usability. MODIS datasets are regularly updated and reprocessed to meet pre ..."
Abstract
- Add to MetaCart
(Show Context)
Globally acquired data from both MODIS instruments are suitable for science quality time series, because the unique concept of pixel-level quality information of each MODIS land product allows a detailed analysis of the data usability. MODIS datasets are regularly updated and reprocessed to meet present science requirements. This study compares time series of present collection 4 and currently released collection 5 data for the vegetation index product (MOD13). Considerable adjustments were made including changes in cloud and aerosol detection, compositing, and a redesign of the quality information layer. A software package for time series generation of MODIS data (TiSeG) was adjusted to collection 5 products. The quality requirements of collection 5 data are stricter and collection 5 flags are more sensitive to atmospheric disturbances. The newly introduced reliability dataset is important for accurate cloud detection. Compared to the NDVI, the EVI time series has a higher dynamic range for vegetated units and a better inherent temporal consistency also for lower quality composites. 1.
REGIONAL SCALE LAND USE/LAND COVER CLASSIFICATION USING TEMPORAL SERIES OF MODIS DATA
"... This paper describes a methodology for systematic land use/land cover classification on a regional scale, with emphasis on a low cost and highly automatized approach. This methodology is based on multitemporal analyses of surface reflectance data from the Moderate Resolution Imaging Spectroradiomete ..."
Abstract
- Add to MetaCart
(Show Context)
This paper describes a methodology for systematic land use/land cover classification on a regional scale, with emphasis on a low cost and highly automatized approach. This methodology is based on multitemporal analyses of surface reflectance data from the Moderate Resolution Imaging Spectroradiometer (MODIS), which is located on board NASA’s Terra and Aqua satellites and features high temporal frequency, extensive coverage, and extremely low costs for data acquisition. A sequence of automatized procedures were developed for MODIS data pre-processing, as well as for the training and execution of a supervised classification algorithm, where temporal profiles are fitted to smooth polynomial curves and intelligent curve features are then computed in order to reduce data dimensionality and improve profile interpretability, thus providing a more robust classification approach. A case study was performed in the High Taquari Basin, in the states of Mato Grosso do Sul and Mato Grosso, Brazil, which showed that the method was indeed capable of generalizing well over the entire region of study (over 25,000km 2), effectively discriminating between areas of agriculture, pasture, and savannah. The methodology was also seen to be quite successful in identifying areas of deforestation, which is of particular interest for the monitoring of land use and land use change in the region. 1.
