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Remote Sens., Volume 2, Issue 2 (February 2010), Pages 388-610

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Research

Open AccessArticle Phenological Characterization of Desert Sky Island Vegetation Communities with Remotely Sensed and Climate Time Series Data
Remote Sens. 2010, 2(2), 388-415; doi:10.3390/rs2020388
Received: 1 December 2009 / Revised: 15 January 2010 / Accepted: 21 January 2010 / Published: 27 January 2010
Cited by 19 | PDF Full-text (7749 KB) | HTML Full-text | XML Full-text
Abstract
Climate change and variability are expected to impact the synchronicity and interactions between the Sonoran Desert and the forested sky islands which represent steep biological and environmental gradients. The main objectives were to examine how well satellite greenness time series data and derived
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Climate change and variability are expected to impact the synchronicity and interactions between the Sonoran Desert and the forested sky islands which represent steep biological and environmental gradients. The main objectives were to examine how well satellite greenness time series data and derived phenological metrics (e.g., season start, peak greenness) can characterize specific vegetation communities across an elevation gradient, and to examine the interactions between climate and phenological metrics for each vegetation community. We found that representative vegetation types (11), varying between desert scrub, mesquite, grassland, mixed oak, juniper and pine, often had unique seasonal and interannual phenological trajectories and spatial patterns. Satellite derived land surface phenometrics (11) for each of the vegetation communities along the cline showed numerous distinct significant relationships in response to temperature (4) and precipitation (7) metrics. Satellite-derived sky island vegetation phenology can help assess and monitor vegetation dynamics and provide unique indicators of climate variability and patterns of change. Full article
(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)
Open AccessArticle Spectral Reflectance of Wheat Residue during Decomposition and Remotely Sensed Estimates of Residue Cover
Remote Sens. 2010, 2(2), 416-431; doi:10.3390/rs2020416
Received: 23 December 2009 / Revised: 20 January 2010 / Accepted: 21 January 2010 / Published: 27 January 2010
Cited by 20 | PDF Full-text (680 KB) | HTML Full-text | XML Full-text
Abstract
Remotely sensed estimates of crop residue cover (fR) are required to assess the extent of conservation tillage over large areas; the impact of decay processes on estimates of residue cover is unknown. Changes in wheat straw composition and spectral reflectance were measured during
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Remotely sensed estimates of crop residue cover (fR) are required to assess the extent of conservation tillage over large areas; the impact of decay processes on estimates of residue cover is unknown. Changes in wheat straw composition and spectral reflectance were measured during the decay process and their impact on estimates of fR were assessed. Proportions of cellulose and hemicellulose declined, while lignin increased. Spectral features associated with cellulose diminished during decomposition. Narrow-band spectral residue indices robustly estimated fR, while broad-band indices were inconsistent. Advanced multi-spectral sensors or hyperspectral sensors are required to assess fR reliably over diverse agricultural landscapes. Full article
Open AccessArticle Comparing Accuracy of Airborne Laser Scanning and TerraSAR-X Radar Images in the Estimation of Plot-Level Forest Variables
Remote Sens. 2010, 2(2), 432-445; doi:10.3390/rs2020432
Received: 7 December 2009 / Revised: 13 January 2010 / Accepted: 22 January 2010 / Published: 28 January 2010
Cited by 19 | PDF Full-text (1281 KB) | HTML Full-text | XML Full-text
Abstract
In this study we compared the accuracy of low-pulse airborne laser scanning (ALS) data, multi-temporal high-resolution noninterferometric TerraSAR-X radar data and a combined feature set derived from these data in the estimation of forest variables at plot level. The TerraSAR-X data set consisted
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In this study we compared the accuracy of low-pulse airborne laser scanning (ALS) data, multi-temporal high-resolution noninterferometric TerraSAR-X radar data and a combined feature set derived from these data in the estimation of forest variables at plot level. The TerraSAR-X data set consisted of seven dual-polarized (HH/HV or VH/VV) Stripmap mode images from all seasons of the year. We were especially interested in distinguishing between the tree species. The dependent variables estimated included mean volume, basal area, mean height, mean diameter and tree species-specific mean volumes. Selection of best possible feature set was based on a genetic algorithm (GA). The nonparametric k-nearest neighbour (k-NN) algorithm was applied to the estimation. The research material consisted of 124 circular plots measured at tree level and located in the vicinity of Espoo, Finland. There are large variations in the elevation and forest structure in the study area, making it demanding for image interpretation. The best feature set contained 12 features, nine of them originating from the ALS data and three from the TerraSAR-X data. The relative RMSEs for the best performing feature set were 34.7% (mean volume), 28.1% (basal area), 14.3% (mean height), 21.4% (mean diameter), 99.9% (mean volume of Scots pine), 61.6% (mean volume of Norway spruce) and 91.6% (mean volume of deciduous tree species). The combined feature set outperformed an ALS-based feature set marginally; in fact, the latter was better in the case of species-specific volumes. Features from TerraSAR-X alone performed poorly. However, due to favorable temporal resolution, satellite-borne radar imaging is a promising data source for updating large-area forest inventories based on low-pulse ALS. Full article
(This article belongs to the Special Issue Microwave Remote Sensing)
Open AccessArticle Interannual Changes of Fire Activity in the Protected Areas of the SUN Network and Other Parks and Reserves of the West and Central Africa Region Derived from MODIS Observations
Remote Sens. 2010, 2(2), 446-463; doi:10.3390/rs2020446
Received: 10 December 2009 / Revised: 22 January 2010 / Accepted: 26 January 2010 / Published: 29 January 2010
Cited by 7 | PDF Full-text (353 KB) | HTML Full-text | XML Full-text
Abstract
Time series of fire occurrence, derived from MODIS data, have been used to characterise the spatio-temporal distribution of fire events during the 2004–2009 period in 17 protected areas (PAs) of West and Central Africa, with particular attention to those of the SUN network
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Time series of fire occurrence, derived from MODIS data, have been used to characterise the spatio-temporal distribution of fire events during the 2004–2009 period in 17 protected areas (PAs) of West and Central Africa, with particular attention to those of the SUN network in Senegal, Burkina Faso, Benin and Niger. The temporal distribution of the fire activity and the number of fire occurences are quite different inside the PAs and in their surrounding area. A progressive increase of the length of the burning season is observed in the West Africa PAs. Quantitatively, the general trend over the last five years is an increase of the fire density (+22%) inside the PAs and a decrease (−27%) outside. The results indicate that the capacity of the PAs to maintain the biological diversity of the region is probably decreasing due to the combined effects of the anthropic pressure inside the PAs and of an on-going isolation process. Full article
(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)
Figures

Open AccessArticle Radiometric Calibration for AgCam
Remote Sens. 2010, 2(2), 464-477; doi:10.3390/rs2020464
Received: 8 January 2010 / Revised: 25 January 2010 / Accepted: 26 January 2010 / Published: 1 February 2010
Cited by 15 | PDF Full-text (3285 KB) | HTML Full-text | XML Full-text
Abstract
The student-built Agricultural Camera (AgCam) now onboard the International Space Station observes the Earth surface through two linescan cameras with Charge-Coupled Device (CCD) arrays sensitive to visible and near-infrared wavelengths, respectively. The electro-optical components of the AgCam were characterized using precision calibration equipment;
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The student-built Agricultural Camera (AgCam) now onboard the International Space Station observes the Earth surface through two linescan cameras with Charge-Coupled Device (CCD) arrays sensitive to visible and near-infrared wavelengths, respectively. The electro-optical components of the AgCam were characterized using precision calibration equipment; a method for modeling and applying these measurements was derived. Correction coefficients to minimize effects of optical vignetting, CCD non-uniform quantum efficiency, and CCD dark current are separately determined using a least squares fit approach. Application of correction coefficients yields significant variability reduction in flat-field images; comparable results are obtained when applied to ground test images. Full article
Open AccessArticle Assessing Plant Diversity in a Dry Tropical Forest: Comparing the Utility of Landsat and Ikonos Satellite Images
Remote Sens. 2010, 2(2), 478-496; doi:10.3390/rs2020478
Received: 15 December 2009 / Revised: 8 January 2010 / Accepted: 28 January 2010 / Published: 2 February 2010
Cited by 27 | PDF Full-text (1951 KB) | HTML Full-text | XML Full-text
Abstract
While high expectations have been raised about the utility of high resolution satellite imagery for biodiversity assessment, there has been almost no empirical assessment of its use, particularly in the biodiverse tropics which represent a very challenging environment for such assessment challenge. This
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While high expectations have been raised about the utility of high resolution satellite imagery for biodiversity assessment, there has been almost no empirical assessment of its use, particularly in the biodiverse tropics which represent a very challenging environment for such assessment challenge. This research evaluates the use of high spatial resolution (IKONOS) and medium spatial resolution (Landsat ETM+) satellite imagery for assessing vegetation diversity in a dry tropical forest in central India. Contrary to expectations, across multiple measures of plant distribution and diversity, the resolution of IKONOS data is too fine for the purpose of plant diversity assessment and Landsat imagery performs better. Full article
(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)
Open AccessArticle Urban and Peri-Urban Agriculture in Developing Countries Studied using Remote Sensing and In Situ Methods
Remote Sens. 2010, 2(2), 497-513; doi:10.3390/rs2020497
Received: 16 December 2009 / Revised: 26 January 2010 / Accepted: 27 January 2010 / Published: 2 February 2010
Cited by 11 | PDF Full-text (180 KB) | HTML Full-text | XML Full-text
Abstract
Urban farming, practiced by about 800 million people globally, has contributed significantly to food security and food safety. The practice has sustained livelihood of the urban and peri-urban low income dwellers in developing countries for many years. Its popularity among the urban low
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Urban farming, practiced by about 800 million people globally, has contributed significantly to food security and food safety. The practice has sustained livelihood of the urban and peri-urban low income dwellers in developing countries for many years. Its popularity among the urban low income is largely due to lack of formal jobs and as a means of adding up to household income. There is increasing need to sustainably manage urban farming in developing nations in recent times. Population increase due to rural-urban migration and natural, coupled with infrastructure developments are competing with urban farming for available space and scarce resources such as water for irrigation. Lack of reliable data on the extent of urban/peri-urban areas being used for farming has affected developing sustainable policies to manage urban farming in Accra. Using ground based survey methods to map the urban farmlands are inherently problematic and prohibitively expensive. This has influenced accurate assessment of the future role of urban farming in enhancing food security. Remote sensing, however, allows areas being used as urban farmlands to be rapidly established at relatively low cost. This paper will review advances in the use of remote sensing technology to develop an integrated monitoring technique for urban farmlands in Accra. Full article
(This article belongs to the Special Issue Global Croplands)
Open AccessCommunication Using Lidar-Derived Vegetation Profiles to Predict Time since Fire in an Oak Scrub Landscape in East-Central Florida
Remote Sens. 2010, 2(2), 514-525; doi:10.3390/rs2020514
Received: 7 January 2010 / Revised: 2 February 2010 / Accepted: 3 February 2010 / Published: 11 February 2010
Cited by 14 | PDF Full-text (420 KB) | HTML Full-text | XML Full-text
Abstract
Disturbance plays a fundamental role in determining the vertical structure of vegetation in many terrestrial ecosystems, and knowledge of disturbance histories is vital for developing effective management and restoration plans. In this study, we investigated the potential of using vertical vegetation profiles derived
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Disturbance plays a fundamental role in determining the vertical structure of vegetation in many terrestrial ecosystems, and knowledge of disturbance histories is vital for developing effective management and restoration plans. In this study, we investigated the potential of using vertical vegetation profiles derived from discrete-return lidar to predict time since fire (TSF) in a landscape of oak scrub in east-central Florida. We predicted that fire influences vegetation structure at the mesoscale (i.e., spatial scales of tens of meters to kilometers). To evaluate this prediction, we binned lidar returns into 1m vertical by 5 × 5 m horizontal cells and averaged the resulting profiles over a range of horizontal window sizes (0 to 500 m on a side). We then performed a series of resampling tests to compare the performance of support vector machine (SVM), k-nearest neighbor (k-NN), logistic regression, and linear discriminant analysis (LDA) classifiers and to estimate the amount of training data necessary to achieve satisfactory performance. Our results indicate that: (1) the SVMs perform significantly better than the other classifiers, (2) SVM classifiers may require relatively small training data sets, and (3) the highest classification accuracies occur with averaging over windows representing sizes in the mesoscale range. Full article
(This article belongs to the Special Issue LiDAR)
Open AccessArticle Phenological Classification of the United States: A Geographic Framework for Extending Multi-Sensor Time-Series Data
Remote Sens. 2010, 2(2), 526-544; doi:10.3390/rs2020526
Received: 22 December 2009 / Revised: 3 February 2010 / Accepted: 3 February 2010 / Published: 11 February 2010
Cited by 19 | PDF Full-text (1294 KB) | HTML Full-text | XML Full-text
Abstract
This study introduces a new geographic framework, phenological classification, for the conterminous United States based on Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time-series data and a digital elevation model. The resulting pheno-class map is comprised of 40 pheno-classes, each
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This study introduces a new geographic framework, phenological classification, for the conterminous United States based on Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time-series data and a digital elevation model. The resulting pheno-class map is comprised of 40 pheno-classes, each having unique phenological and topographic characteristics. Cross-comparison of the pheno-classes with the 2001 National Land Cover Database indicates that the new map contains additional phenological and climate information. The pheno-class framework may be a suitable basis for the development of an Advanced Very High Resolution Radiometer (AVHRR)-MODIS NDVI translation algorithm and for various biogeographic studies. Full article
Open AccessArticle Soil Line Influences on Two-Band Vegetation Indices and Vegetation Isolines: A Numerical Study
Remote Sens. 2010, 2(2), 545-561; doi:10.3390/rs2020545
Received: 16 December 2009 / Revised: 20 January 2010 / Accepted: 2 February 2010 / Published: 12 February 2010
Cited by 6 | PDF Full-text (243 KB) | HTML Full-text | XML Full-text
Abstract
Influences of soil line variations on two-band vegetation indices (VIs) and their vegetation isolines in red and near-infrared (NIR) reflectance space are investigated based on recently derived relationships between the relative variations of VIs with variations of the soil line parameters in the
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Influences of soil line variations on two-band vegetation indices (VIs) and their vegetation isolines in red and near-infrared (NIR) reflectance space are investigated based on recently derived relationships between the relative variations of VIs with variations of the soil line parameters in the accompanying paper by Yoshioka et al. [1]. The soil line influences are first demonstrated numerically in terms of variations of vegetation isolines and VI values along with the isolines. A hypothetical case is then analyzed by assuming the discrepancies between the general and regional soil lines for a Southern Brazil area reported elsewhere. The results indicate the validity of our analytical approach for the evaluation of soil line influences and the applicability for adjustment of VI errors using external data sources of soil reflectance spectra. Full article
Open AccessArticle Value of Using Different Vegetative Indices to Quantify Agricultural Crop Characteristics at Different Growth Stages under Varying Management Practices
Remote Sens. 2010, 2(2), 562-578; doi:10.3390/rs2020562
Received: 25 December 2009 / Revised: 5 February 2010 / Accepted: 8 February 2010 / Published: 23 February 2010
Cited by 55 | PDF Full-text (827 KB) | HTML Full-text | XML Full-text
Abstract
The paper investigates the value of using distinct vegetation indices to quantify and characterize agricultural crop characteristics at different growth stages. Research was conducted on four crops (corn, soybean, wheat, and canola) over eight years grown under different tillage practices and nitrogen management
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The paper investigates the value of using distinct vegetation indices to quantify and characterize agricultural crop characteristics at different growth stages. Research was conducted on four crops (corn, soybean, wheat, and canola) over eight years grown under different tillage practices and nitrogen management practices that varied rate and timing. Six different vegetation indices were found most useful, depending on crop phenology and management practices: (a) simple ratio for biomass, (b) NDVI for intercepted PAR, (c) SAVI for early stages of LAI, (d) EVI for later stages of LAI, (e) CIgreen for leaf chlorophyll, (f) NPCI for chlorophyll during later stages, and (g) PSRI to quantify plant senescence. There were differences among varieties of corn and soybean for the vegetation indices during the growing season and these differences were a function of growth stage and vegetative index. These results clearly imply the need to use multiple vegetation indices to best capture agricultural crop characteristics. Full article
(This article belongs to the Special Issue Global Croplands)
Open AccessArticle Artificial Neural Network Approach for Mapping Contrasting Tillage Practices
Remote Sens. 2010, 2(2), 579-590; doi:10.3390/rs2020579
Received: 18 December 2009 / Revised: 5 January 2010 / Accepted: 8 February 2010 / Published: 23 February 2010
Cited by 15 | PDF Full-text (159 KB) | HTML Full-text | XML Full-text
Abstract
Tillage information is crucial for environmental modeling as it directly affects evapotranspiration, infiltration, runoff, carbon sequestration, and soil losses due to wind and water erosion from agricultural fields. However, collecting this information can be time consuming and costly. Remote sensing approaches are promising
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Tillage information is crucial for environmental modeling as it directly affects evapotranspiration, infiltration, runoff, carbon sequestration, and soil losses due to wind and water erosion from agricultural fields. However, collecting this information can be time consuming and costly. Remote sensing approaches are promising for rapid collection of tillage information on individual fields over large areas. Numerous regression-based models are available to derive tillage information from remote sensing data. However, these models require information about the complex nature of underlying watershed characteristics and processes. Unlike regression-based models, Artificial Neural Network (ANN) provides an efficient alternative to map complex nonlinear relationships between an input and output datasets without requiring a detailed knowledge of underlying physical relationships. Limited or no information currently exist quantifying ability of ANN models to identify contrasting tillage practices from remote sensing data. In this study, a set of Landsat TM-based ANN models was developed to identify contrasting tillage practices in the Texas High Plains. Observed tillage data from Moore and Ochiltree Counties were used to develop and evaluate the models, respectively. The overall classification accuracy for the 15 models developed with the Moore County dataset varied from 74% to 91%. Statistical evaluation of these models against the Ochiltree County dataset produced results with an overall classification accuracy varied from 66% to 80%. The ANN models based on TM band 5 or indices of TM Band 5 may provide consistent and accurate tillage information when applied to the Texas High Plains. Full article
Open AccessArticle Tracking Fires in India Using Advanced Along Track Scanning Radiometer (A)ATSR Data
Remote Sens. 2010, 2(2), 591-610; doi:10.3390/rs2020591
Received: 1 December 2009 / Revised: 27 January 2010 / Accepted: 1 February 2010 / Published: 24 February 2010
Cited by 10 | PDF Full-text (809 KB) | HTML Full-text | XML Full-text
Abstract
Forest fires pose a threat more serious than illegal felling in developing countries and are a cause of major concern for environmental security. Fires in tropical forests, though not devastating on a large scale as compared to large and infrequent fires in boreal
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Forest fires pose a threat more serious than illegal felling in developing countries and are a cause of major concern for environmental security. Fires in tropical forests, though not devastating on a large scale as compared to large and infrequent fires in boreal or Mediterranean systems, still cause loss to biodiversity and economic and monetary value. In India, human-induced forest fires increasingly affect legally protected nature conservation areas. An array of satellite sensors that are now available can be deployed to monitor such events on a global and local scale. The present study uses night-time Advanced Along Track Scanning Radiometer (A)ATSR satellite data from the last nine years to identify high fire-prone zones, fire affected areas in protected zones and the distribution of these incidents in relation to bio-geographic zones. Central India, with its vegetation type that is just right for fire ignition and spread, was observed to be the most severely affected area with maximum fire incidences. The bio-geographic zone comprising this area–such as the Deccan peninsula, which includes provinces like Central Highlands, Eastern Highlands, Central Plateau and Chhota Nagpur–was observed to be the most affected, accounting for approximately 36% of the total fire occurrences during the period 1997–2005. In protected areas, 778 fire incidents were observed within the last eight years. Comparison of (A)ATSR fire locations with MODIS active fire data for the Western Ghats (mainly of tropical evergreen forests and savannahs) and the Eastern Ghats (tropical deciduous) showed a spatial agreement of 72% with a minimum distance between the two products of 100 m. This study focuses on regions in India that are vulnerable to forest fires during specific time-frames and appraises the situation with an aim to minimize such incidents, if not completely stop the fire spread and its consequent destruction and loss. Our main objective is to understand seasonal and spatial variation in fire pattern and to identify zones of frequent burning. Full article

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