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Remote Sens., Volume 2, Issue 7 (July 2010), Pages 1625-1863

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Research

Open AccessArticle Global Patterns of Cropland Use Intensity
Remote Sens. 2010, 2(7), 1625-1643; doi:10.3390/rs2071625
Received: 20 April 2010 / Revised: 14 June 2010 / Accepted: 18 June 2010 / Published: 24 June 2010
Cited by 33 | PDF Full-text (650 KB) | HTML Full-text | XML Full-text
Abstract
This study presents a global scale analysis of cropping intensity, crop duration and fallow land extent computed by using the global dataset on monthly irrigated and rainfed crop areas MIRCA2000. MIRCA2000 was mainly derived from census data and crop calendars from literature. [...] Read more.
This study presents a global scale analysis of cropping intensity, crop duration and fallow land extent computed by using the global dataset on monthly irrigated and rainfed crop areas MIRCA2000. MIRCA2000 was mainly derived from census data and crop calendars from literature. Global cropland extent was 16 million km2 around the year 2000 of which 4.4 million km2 (28%) was fallow, resulting in an average cropping intensity of 0.82 for total cropland extent and of 1.13 when excluding fallow land. The lowest cropping intensities related to total cropland extent were found for Southern Africa (0.45), Central America (0.49) and Middle Africa (0.54), while highest cropping intensities were computed for Eastern Asia (1.04) and Southern Asia (1.0). In remote or arid regions where shifting cultivation is practiced, fallow periods last 3–10 years or even longer. In contrast, crops are harvested two or more times per year in highly populated, often irrigated tropical or subtropical lowlands where multi-cropping systems are common. This indicates that intensification of agricultural land use is a strategy that may be able to significantly improve global food security. There exist large uncertainties regarding extent of cropland, harvested crop area and therefore cropping intensity at larger scales. Satellite imagery and remote sensing techniques provide opportunities for decreasing these uncertainties and to improve the MIRCA2000 inventory. Full article
(This article belongs to the Special Issue Global Croplands)
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Open AccessArticle Normalizing and Converting Image DC Data Using Scatter Plot Matching
Remote Sens. 2010, 2(7), 1644-1661; doi:10.3390/rs2071644
Received: 20 April 2010 / Revised: 11 May 2010 / Accepted: 7 June 2010 / Published: 24 June 2010
Cited by 9 | PDF Full-text (1654 KB) | HTML Full-text | XML Full-text
Abstract
Remote sensing image data from sources such as Landsat or airborne multispectral digital cameras are typically in the form of digital count (DC) values. To compare images acquired by the same sensor system on different dates, or images acquired by different sensor [...] Read more.
Remote sensing image data from sources such as Landsat or airborne multispectral digital cameras are typically in the form of digital count (DC) values. To compare images acquired by the same sensor system on different dates, or images acquired by different sensor systems, it is necessary to correct for differences in the DC values due to sensor characteristics (gain and offset), illumination of the surface (a function of sun angle), and atmospheric clarity. A method is described for normalizing one image to another, or converting image DC values to surface reflectance. This method is based on the identification of pseudo-invariant features (bare soil line and full canopy point) in the scatter plot of red and near-infrared image pixel values. The method, called “scatter plot matching” (SPM), is demonstrated by normalizing a Landsat-7 ETM+ image to a Landsat-5 TM image, and by converting the pixel DC values in a Landsat-5 TM image to values of surface reflectance. While SPM has some limitations, it represents a simple, straight-forward method for calibrating remote sensing image data. Full article
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Open AccessArticle Design and First Results of an UAV-Borne L-Band Radiometer for Multiple Monitoring Purposes
Remote Sens. 2010, 2(7), 1662-1679; doi:10.3390/rs2071662
Received: 30 April 2010 / Revised: 15 June 2010 / Accepted: 22 June 2010 / Published: 29 June 2010
Cited by 13 | PDF Full-text (7446 KB) | HTML Full-text | XML Full-text
Abstract
UAV (unmanned Aerial Vehicle) platforms represent a challenging opportunity for the deployment of a number of remote sensors. These vehicles are a cost-effective option in front of manned aerial vehicles (planes and helicopters), are easy to deploy due to the short runways [...] Read more.
UAV (unmanned Aerial Vehicle) platforms represent a challenging opportunity for the deployment of a number of remote sensors. These vehicles are a cost-effective option in front of manned aerial vehicles (planes and helicopters), are easy to deploy due to the short runways needed, and they allow users to meet the critical requirements of the spatial and temporal resolutions imposed by the instruments. L-band radiometers are an interesting option for obtaining soil moisture maps over local areas with relatively high spatial resolution for precision agriculture, coastal monitoring, estimation of the risk of fires, flood prevention, etc. This paper presents the design of a light-weight, airborne L-band radiometer for deployment in a small UAV, including the hardware and specific software developed for calibration, geo-referencing, and soil moisture retrieval. First results and soil moisture retrievals from different field experiments are presented. Full article
Open AccessArticle Inter-Algorithm Relationships for the Estimation of the Fraction of Vegetation Cover Based on a Two Endmember Linear Mixture Model with the VI Constraint
Remote Sens. 2010, 2(7), 1680-1701; doi:10.3390/rs2071680
Received: 30 April 2010 / Revised: 29 June 2010 / Accepted: 29 June 2010 / Published: 2 July 2010
Cited by 9 | PDF Full-text (471 KB) | HTML Full-text | XML Full-text
Abstract
Measurements of the fraction of vegetation cover (FVC), retrieved from remotely sensed reflectance spectra, serves as a useful measure of land cover changes on the regional and global scales. A linear mixture model (LMM) is frequently employed to analytically estimate the FVC [...] Read more.
Measurements of the fraction of vegetation cover (FVC), retrieved from remotely sensed reflectance spectra, serves as a useful measure of land cover changes on the regional and global scales. A linear mixture model (LMM) is frequently employed to analytically estimate the FVC using the spectral vegetation index (VI) as a constraint. Variations in the application of this algorithm arise due to differences in the choice of endmember spectra and VI model assumptions. As a result, the retrieved FVCs from a single spectrum depend on those choices. Therefore, the mechanism underlying this dependency must be understood fully to improve the interpretation of the results. The objective of this study is to clarify the relationships among algorithms based on the LMM. The relationships were derived analytically by limiting both the number of endmembers and the spectral wavelength band to two each. Numerical experiments were conducted to demonstrate and validate the derived relationships. It was found that the relationships between two algorithms of this kind could be characterized by a single parameter that was determined by the endmember spectra and the coefficients of a VI model equation used in the algorithms. Full article
Open AccessArticle Retrieval of Leaf Area Index (LAI) and Soil Water Content (WC) Using Hyperspectral Remote Sensing under Controlled Glass House Conditions for Spring Barley and Sugar Beet
Remote Sens. 2010, 2(7), 1702-1721; doi:10.3390/rs2071702
Received: 14 May 2010 / Revised: 18 June 2010 / Accepted: 28 June 2010 / Published: 6 July 2010
Cited by 12 | PDF Full-text (333 KB) | HTML Full-text | XML Full-text
Abstract
Leaf area index (LAI) and water content (WC) in the root zone are two major hydro-meteorological parameters that exhibit a dominant control on water, energy and carbon fluxes, and are therefore important for any regional eco-hydrological or climatological study. To investigate the [...] Read more.
Leaf area index (LAI) and water content (WC) in the root zone are two major hydro-meteorological parameters that exhibit a dominant control on water, energy and carbon fluxes, and are therefore important for any regional eco-hydrological or climatological study. To investigate the potential for retrieving these parameter from hyperspectral remote sensing, we have investigated plant spectral reflectance (400–2,500 nm, ASD FieldSpec3) for two major agricultural crops (sugar beet and spring barley) in the mid-latitudes, treated under different water and nitrogen (N) conditions in a greenhouse experiment over the growing period of 2008. Along with the spectral response, we have measured soil water content and LAI for 15 intensive measurement campaigns spread over the growing season and could demonstrate a significant response of plant reflectance characteristics to variations in water content and nutrient conditions. Linear and non-linear dimensionality analysis suggests that the full band reflectance information is well represented by the set of 28 vegetation spectral indices (SI) and most of the variance is explained by three to a maximum of eight variables. Investigation of linear dependencies between LAI and soil WC and pre-selected SI’s indicate that: (1) linear regression using single SI is not sufficient to describe plant/soil variables over the range of experimental conditions, however, some improvement can be seen knowing crop species beforehand; (2) the improvement is superior when applying multiple linear regression using three explanatory SI’s approach. In addition to linear investigations, we applied the non-linear CART (Classification and Regression Trees) technique, which finally did not show the potential for any improvement in the retrieval process. Full article
Open AccessArticle Why is the Ratio of Reflectivity Effective for Chlorophyll Estimation in the Lake Water?
Remote Sens. 2010, 2(7), 1722-1730; doi:10.3390/rs2071722
Received: 30 May 2010 / Revised: 13 June 2010 / Accepted: 7 July 2010 / Published: 9 July 2010
Cited by 6 | PDF Full-text (583 KB) | HTML Full-text | XML Full-text
Abstract
The reasons why it is effective to estimate the chlorophyll-a concentration with the ratio of spectral radiance reflectance at the red light region and near infrared regions were shown in theory using a two-flow model. It was found that all of the [...] Read more.
The reasons why it is effective to estimate the chlorophyll-a concentration with the ratio of spectral radiance reflectance at the red light region and near infrared regions were shown in theory using a two-flow model. It was found that all of the backscattering coefficients can consequently be ignored by using the ratio of spectral radiance reflectance, which is the ratio of the upward radiance to the downward irradiance, at the red light and near infrared regions. In other words, the ratio can be expressed by using only absorption coefficients, which are more stable for measurement than backscattering coefficients. In addition, the band selection is crucial for producing the band ratio when the chlorophyll-a concentration is estimated without the effects of backscattering. I conclude that the two wavelengths selected must be close, but one must be within the absorption range of chlorophyll-a, and the other must be outside of the absorption range of chlorophyll-a, in order to accurately estimate the chlorophyll-a concentration. Full article
Open AccessArticle The Function of Remote Sensing in Support of Environmental Policy
Remote Sens. 2010, 2(7), 1731-1750; doi:10.3390/rs2071731
Received: 20 May 2010 / Revised: 13 June 2010 / Accepted: 17 June 2010 / Published: 12 July 2010
Cited by 11 | PDF Full-text (666 KB) | HTML Full-text | XML Full-text
Abstract
Limited awareness of environmental remote sensing’s potential ability to support environmental policy development constrains the technology’s utilization. This paper reviews the potential of earth observation from the perspective of environmental policy. A literature review of “remote sensing and policy” revealed that while [...] Read more.
Limited awareness of environmental remote sensing’s potential ability to support environmental policy development constrains the technology’s utilization. This paper reviews the potential of earth observation from the perspective of environmental policy. A literature review of “remote sensing and policy” revealed that while the number of publications in this field increased almost twice as rapidly as that of remote sensing literature as a whole (15.3 versus 8.8% yr−1), there is apparently little academic interest in the societal contribution of environmental remote sensing. This is because none of the more than 300 peer reviewed papers described actual policy support. This paper describes and discusses the potential, actual support, and limitations of earth observation with respect to supporting the various stages of environmental policy development. Examples are given of the use of remote sensing in problem identification and policy formulation, policy implementation, and policy control and evaluation. While initially, remote sensing contributed primarily to the identification of environmental problems and policy implementation, more recently, interest expanded to applications in policy control and evaluation. The paper concludes that the potential of earth observation to control and evaluate, and thus assess the efficiency and effectiveness of policy, offers the possibility of strengthening governance. Full article
Open AccessArticle Wide Area Wetland Mapping in Semi-Arid Africa Using 250-Meter MODIS Metrics and Topographic Variables
Remote Sens. 2010, 2(7), 1751-1766; doi:10.3390/rs2071751
Received: 26 May 2010 / Revised: 3 June 2010 / Accepted: 1 July 2010 / Published: 12 July 2010
Cited by 16 | PDF Full-text (5945 KB) | HTML Full-text | XML Full-text
Abstract
Wetlands in West Africa are among the most vulnerable ecosystems to climate change. West African wetlands are often freshwater transfer mechanisms from wetter climate regions to dryer areas, providing an array of ecosystem services and functions. Often wetland-specific data in Africa is [...] Read more.
Wetlands in West Africa are among the most vulnerable ecosystems to climate change. West African wetlands are often freshwater transfer mechanisms from wetter climate regions to dryer areas, providing an array of ecosystem services and functions. Often wetland-specific data in Africa is only available on a per country basis or as point data. Since wetlands are challenging to map, their accuracies are not well considered in global land cover products. In this paper we describe a methodology to map wetlands using well-corrected 250-meter MODIS time-series data for the year 2002 and over a 360,000 km2 large study area in western Burkina Faso and southern Mali (West Africa). A MODIS-based spectral index table is used to map basic wetland morphology classes. The index uses the wet season near infrared (NIR) metrics as a surrogate for flooding, as a function of the dry season chlorophyll activity metrics (as NDVI). Topographic features such as sinks and streamline areas were used to mask areas where wetlands can potentially occur, and minimize spectral confusion. 30-m Landsat trajectories from the same year, over two reference sites, were used for accuracy assessment, which considered the area-proportion of each class mapped in Landsat for every MODIS cell. We were able to map a total of five wetland categories. Aerial extend of all mapped wetlands (class “Wetland”) is 9,350 km2, corresponding to 4.3% of the total study area size. The classes “No wetland”/“Wetland” could be separated with very high certainty; the overall agreement (KHAT) was 84.2% (0.67) and 97.9% (0.59) for the two reference sites, respectively. The methodology described herein can be employed to render wide area base line information on wetland distributions in semi-arid West Africa, as a data-scarce region. The results can provide (spatially) interoperable information feeds for inter-zonal as well as local scale water assessments. Full article
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Open AccessArticle Application of MODIS Products for Air Quality Studies Over Southeastern Italy
Remote Sens. 2010, 2(7), 1767-1796; doi:10.3390/rs2071767
Received: 4 June 2010 / Revised: 24 June 2010 / Accepted: 5 July 2010 / Published: 14 July 2010
Cited by 11 | PDF Full-text (572 KB) | HTML Full-text | XML Full-text
Abstract
Aerosol optical thicknesses (AOTs) by the MODerate Resolution Imaging Spetroradiometer (MODIS) on-board Aqua and Terra satellites, and ground-based measurements of PM10 mass concentrations, collected over three years (2006–2008) at two suburban sites which are 20 km apart, are correlated to assess the [...] Read more.
Aerosol optical thicknesses (AOTs) by the MODerate Resolution Imaging Spetroradiometer (MODIS) on-board Aqua and Terra satellites, and ground-based measurements of PM10 mass concentrations, collected over three years (2006–2008) at two suburban sites which are 20 km apart, are correlated to assess the use of satellite data for regional air quality studies over Southeastern Italy, in the central Mediterranean. Due to the geographical location, this area is affected by local and long-range transported marine, desert (from Sahara), and anthropogenic (from continental Europe) aerosols. 24-hour averaged PM10 mass concentrations span the 1.6–152 µg/m 3 range. Yearly means of PM10 mass concentrations decrease from 2006 to 2008 and vary within the 26–36 µg/m 3 range. Daily mean values of MODIS AOTs vary up to 0.8 at 550 nm, while yearly means span the 0.15–0.17 range. A first assessment of the regression relationship between daily averaged PM10 mass concentrations and MODIS-AOTs shows that linear correlation coefficients ( R ) vary within the 0.20–0.35 range and are affected by the sampling year and the site location. The PM10-AOT correlation becomes stronger (0.34 ≤ R ≤ 0.57) when the analysis is restricted to clear-sky MODIS measurements. The cloud screening procedure adopted within the AERONET network is used in this study to select clear-sky MODIS measurements, since it allows obtaining larger R values than the ones obtained using the cloud fraction MODIS product to select clear-sky MODIS measurements. Using three years of clear-sky measurements to estimate PM10 mass concentrations from MODIS-AOTs, the empirical relation we have found is: PM10 ( m g/m 3 ) = 25 ( m g/m 3 ) + 65 ( m g/m 3 ) × AOT. Over 80% of the differences between the measured and satellite estimated PM10 mass concentrations over the three years are within ±1 standard deviation of the yearly means. The differences between yearly means of calculated and measured mass concentrations that are close to zero in 2006, increase up to 4 m g/m 3 at one siteand 8 m g/m 3 at the other site in 2008. The PM10 mass concentration decrease from 2006 to 2008 contributes to this last result. Our results demonstrate the potential of MODIS data for deriving indirect estimates of PM10 over Southeastern Italy. It is also shown that a stronger relationship between PM10 and MODIS-AOTs is obtained when the AOT is divided by the product of the mixing layer height with the ground wind speed and the analysis restricted to clear sky MODIS measurements. However, we have found that the stronger correlation (0.52 ≤ R ≤ 0.66) does not allow a significant improvement of MODIS-based-estimates of PM10 mass concentrations. Full article
(This article belongs to the Special Issue Atmospheric Remote Sensing)
Open AccessArticle Channel and Floodplain Change Analysis over a 100-Year Period: Lower Yuba River, California
Remote Sens. 2010, 2(7), 1797-1825; doi:10.3390/rs2071797
Received: 30 May 2010 / Revised: 14 June 2010 / Accepted: 13 July 2010 / Published: 19 July 2010
Cited by 9 | PDF Full-text (2410 KB) | HTML Full-text | XML Full-text
Abstract
Hydraulic gold mining in the Sierra Nevada, California (1853–1884) displaced ~1.1 billion m3 of sediment from upland placer gravels that were deposited along piedmont rivers below dams where floods can remobilize them. This study uses topographic and planimetric data from detailed [...] Read more.
Hydraulic gold mining in the Sierra Nevada, California (1853–1884) displaced ~1.1 billion m3 of sediment from upland placer gravels that were deposited along piedmont rivers below dams where floods can remobilize them. This study uses topographic and planimetric data from detailed 1906 topographic maps, 1999 photogrammetric data, and pre- and post-flood aerial photographs to document historic sediment erosion and deposition along the lower Yuba River due to individual floods at the reach scale. Differencing of 3 × 3-m topographic data indicates substantial changes in channel morphology and documents 12.6 × 106 m3 of erosion and 5.8 × 106 m3 of deposition in these reaches since 1906. Planimetric and volumetric measurements document spatial and temporal variations of channel enlargement and lateral migration. Over the last century, channels incised up to ~13 m into mining sediments, which dramatically decreased local flood frequencies and increased flood conveyance. These adjustments were punctuated by event-scale geomorphic changes that redistributed sediment and associated contaminants to downstream lowlands. Full article
Open AccessArticle Microwave Radiometer Resolution Optimization Using Variable Observation Times
Remote Sens. 2010, 2(7), 1826-1843; doi:10.3390/rs2071826
Received: 7 June 2010 / Revised: 5 July 2010 / Accepted: 7 July 2010 / Published: 20 July 2010
Cited by 5 | PDF Full-text (471 KB) | HTML Full-text | XML Full-text
Abstract
This manuscript first revises the performance of total power, Dicke-type and noise-injection microwave radiometers. Equations for the radiometric resolution are revised or derived, and their performance in terms of the radiometric resolution improvement with respect to the ideal total power radiometer resolution [...] Read more.
This manuscript first revises the performance of total power, Dicke-type and noise-injection microwave radiometers. Equations for the radiometric resolution are revised or derived, and their performance in terms of the radiometric resolution improvement with respect to the ideal total power radiometer resolution is evaluated. It is then shown that the radiometric resolution of noise-injection radiometers can be optimized by adjusting dynamically the integration times devoted to the three measurements: antenna, antenna plus noise, and reference load. Numerical results are then presented to illustrate the dependence of the radiometric resolution with different instrument parameters. Experimental results are finally presented to corroborate the predicted performance. It is also shown that in many cases of interest these integration times can be set to a constant value with little degradation with respect to the optimum case, but better than the case in which the total integration time is divided in three equal subintervals. Full article
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Open AccessArticle Estimating Global Cropland Extent with Multi-year MODIS Data
Remote Sens. 2010, 2(7), 1844-1863; doi:10.3390/rs2071844
Received: 25 May 2010 / Revised: 18 July 2010 / Accepted: 18 July 2010 / Published: 21 July 2010
Cited by 62 | PDF Full-text (1750 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
This study examines the suitability of 250 m MODIS (MODerate Resolution Imaging Spectroradiometer) data for mapping global cropland extent. A set of 39 multi-year MODIS metrics incorporating four MODIS land bands, NDVI (Normalized Difference Vegetation Index) and thermal data was employed to [...] Read more.
This study examines the suitability of 250 m MODIS (MODerate Resolution Imaging Spectroradiometer) data for mapping global cropland extent. A set of 39 multi-year MODIS metrics incorporating four MODIS land bands, NDVI (Normalized Difference Vegetation Index) and thermal data was employed to depict cropland phenology over the study period. Sub-pixel training datasets were used to generate a set of global classification tree models using a bagging methodology, resulting in a global per-pixel cropland probability layer. This product was subsequently thresholded to create a discrete cropland/non-cropland indicator map using data from the USDA-FAS (Foreign Agricultural Service) Production, Supply and Distribution (PSD) database describing per-country acreage of production field crops. Five global land cover products, four of which attempted to map croplands in the context of multiclass land cover classifications, were subsequently used to perform regional evaluations of the global MODIS cropland extent map. The global probability layer was further examined with reference to four principle global food crops: corn, soybeans, wheat and rice. Overall results indicate that the MODIS layer best depicts regions of intensive broadleaf crop production (corn and soybean), both in correspondence with existing maps and in associated high probability matching thresholds. Probability thresholds for wheat-growing regions were lower, while areas of rice production had the lowest associated confidence. Regions absent of agricultural intensification, such as Africa, are poorly characterized regardless of crop type. The results reflect the value of MODIS as a generic global cropland indicator for intensive agriculture production regions, but with little sensitivity in areas of low agricultural intensification. Variability in mapping accuracies between areas dominated by different crop types also points to the desirability of a crop-specific approach rather than attempting to map croplands in aggregate. Full article
(This article belongs to the Special Issue Global Croplands)
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