Remote Sensing

2249 KiB

Open AccessArticle

Comparison of Object-Based Image Analysis Approaches to Mapping New Buildings in Accra, Ghana Using Multi-Temporal QuickBird Satellite Imagery

by Yu Hsin Tsai, Douglas Stow and John Weeks

Remote Sens. 2011, 3(12), 2707-2726; https://doi.org/10.3390/rs3122707 - 16 Dec 2011

Cited by 28 | Viewed by 8559

Abstract

The goal of this study was to map and quantify the number of newly constructed buildings in Accra, Ghana between 2002 and 2010 based on high spatial resolution satellite image data. Two semi-automated feature detection approaches for detecting and mapping newly constructed buildings [...] Read more.

The goal of this study was to map and quantify the number of newly constructed buildings in Accra, Ghana between 2002 and 2010 based on high spatial resolution satellite image data. Two semi-automated feature detection approaches for detecting and mapping newly constructed buildings based on QuickBird very high spatial resolution satellite imagery were analyzed: (1) post-classification comparison; and (2) bi-temporal layerstack classification. Feature Analyst software based on a spatial contextual classifier and ENVI Feature Extraction that uses a true object-based image analysis approach of image segmentation and segment classification were evaluated. Final map products representing new building objects were compared and assessed for accuracy using two object-based accuracy measures, completeness and correctness. The bi-temporal layerstack method generated more accurate results compared to the post-classification comparison method due to less confusion with background objects. The spectral/spatial contextual approach (Feature Analyst) outperformed the true object-based feature delineation approach (ENVI Feature Extraction) due to its ability to more reliably delineate individual buildings of various sizes. Semi-automated, object-based detection followed by manual editing appears to be a reliable and efficient approach for detecting and enumerating new building objects. A bivariate regression analysis was performed using neighborhood-level estimates of new building density regressed on a census-derived measure of socio-economic status, yielding an inverse relationship with R² = 0.31 (n = 27; p = 0.00). The primary utility of the new building delineation results is to support spatial analyses of land cover and land use and demographic change. Full article

(This article belongs to the Special Issue Object-Based Image Analysis)

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Open AccessEditorial

Remote Sensing Open Access Journal: Leading a New Paradigm in Publishing

by Prasad S. Thenkabail

Remote Sens. 2011, 3(12), 2704-2706; https://doi.org/10.3390/rs3122704 - 14 Dec 2011

Viewed by 6308

Abstract

Remote Sensing is a pathfinding open access journal providing great opportunities for the growing community of remote sensing and geoscience scientists and practitioners to publish high quality research and practical papers expeditiously. It is a journal keeping up with the changing times we [...] Read more.

Remote Sensing is a pathfinding open access journal providing great opportunities for the growing community of remote sensing and geoscience scientists and practitioners to publish high quality research and practical papers expeditiously. It is a journal keeping up with the changing times we live in: open access, instant access, free access, and global access from whichever precise latitude and longitude you live in on the planet Earth or for that matter anywhere in space as long as we have internet access! So, open access journals are breaking many paradigms and setting forth new ones that will soon become the norm as we advance into the twenty-first century. The days of inordinate delays in publishing good science research articles are fast disappearing with open access journals. In remote sensing and geoscience, Remote Sensing (https://www.mdpi.com/journal/remotesensing/) is one of the pioneers, thanks to the vision of Dr. Shu-Kun Lin, the publisher. It started in the year 2009 with headquarters in Basel, Switzerland and a branch office in Beijing, China. It will soon complete Volume 3 by the end of 2011. [...] Full article

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Open AccessArticle

Remote Sensing Images in Support of Environmental Protocol: Monitoring the Sugarcane Harvest in São Paulo State, Brazil

by Daniel Alves Aguiar, Bernardo Friedrich Theodor Rudorff, Wagner Fernando Silva, Marcos Adami and Marcio Pupin Mello

Remote Sens. 2011, 3(12), 2682-2703; https://doi.org/10.3390/rs3122682 - 13 Dec 2011

Cited by 82 | Viewed by 15695

Abstract

Traditional manual sugarcane harvesting requires the pre-harvest burning practice which should be gradually banned by 2021 for most of São Paulo State, Brazil, on cultivated sugarcane land (terrain slope ≤12%) according to State Law number 11241. To forward the end of this practice [...] Read more.

Traditional manual sugarcane harvesting requires the pre-harvest burning practice which should be gradually banned by 2021 for most of São Paulo State, Brazil, on cultivated sugarcane land (terrain slope ≤12%) according to State Law number 11241. To forward the end of this practice to 2014, a “Green Ethanol” Protocol was established in 2007. The present work aims at analyzing five years of continuous sugarcane harvest monitoring, based on remote sensing images, to evaluate the effectiveness of the Protocol, thus helping decision makers to establish public policies to meet the Protocol’s expected goals. During the last five crop years, sugarcane acreage expanded by 1.5 million ha, which was compensated by a correspondent increase in the green harvested land. However, no significant reduction was observed in the amount of pre-harvest burned land over the same period. Based on the current trend, this goal is likely to be achieved one or two years later (2015–2016), which will be five or six years ahead of 2021 as the goal in the State Law number 11241 states. We thus conclude that the“Green Ethanol” Protocol has been effective with a positive impact on the increase of GH, especially on recently expanded sugarcane fields. Full article

(This article belongs to the Special Issue Remote Sensing in Support of Environmental Policy)

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814 KiB

Open AccessArticle

Remote Sensing and Modeling of Mosquito Abundance and Habitats in Coastal Virginia, USA

by Haley L. Cleckner, Thomas R. Allen and A. Scott Bellows

Remote Sens. 2011, 3(12), 2663-2681; https://doi.org/10.3390/rs3122663 - 12 Dec 2011

Cited by 24 | Viewed by 9023

Abstract

The increase in mosquito populations following extreme weather events poses a major threat to humans because of mosquitoes’ ability to carry disease-causing pathogens, particularly in low-lying, poorly drained coastal plains vulnerable to tropical cyclones. In areas with reservoirs of disease, mosquito abundance information [...] Read more.

The increase in mosquito populations following extreme weather events poses a major threat to humans because of mosquitoes’ ability to carry disease-causing pathogens, particularly in low-lying, poorly drained coastal plains vulnerable to tropical cyclones. In areas with reservoirs of disease, mosquito abundance information can help to identify the areas at higher risk of disease transmission. Using a Geographic Information System (GIS), mosquito abundance is predicted across the City of Chesapeake, Virginia. The mosquito abundance model uses mosquito light trap counts, a habitat suitability model, and dynamic environmental variables (temperature and precipitation) to predict the abundance of the species Culiseta melanura, as well as the combined abundance of the ephemeral species, Aedes vexans and Psorophora columbiae, for the year 2003. Remote sensing techniques were used to quantify environmental variables for a potential habitat suitability index for the mosquito species. The goal of this study was to produce an abundance model that could guide risk assessment, surveillance, and potential disease transmission. Results highlight the utility of integrating field surveillance, remote sensing for synoptic landscape habitat distributions, and dynamic environmental data for predicting mosquito vector abundance across low-lying coastal plains. Limitations of mosquito trapping and multi-source geospatial environmental data are highlighted for future spatial modeling of disease transmission risk. Full article

(This article belongs to the Special Issue Remote Sensing in Coastal Ecosystem)

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Open AccessArticle

Oil Detection in a Coastal Marsh with Polarimetric Synthetic Aperture Radar (SAR)

by Elijah Ramsey III, Amina Rangoonwala, Yukihiro Suzuoki and Cathleen E. Jones

Remote Sens. 2011, 3(12), 2630-2662; https://doi.org/10.3390/rs3122630 - 7 Dec 2011

Cited by 57 | Viewed by 9598

Abstract

The National Aeronautics and Space Administration’s airborne Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) was deployed in June 2010 in response to the Deepwater Horizon oil spill in the Gulf of Mexico. UAVSAR is a fully polarimetric L-band Synthetic Aperture Radar (SAR) sensor [...] Read more.

The National Aeronautics and Space Administration’s airborne Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) was deployed in June 2010 in response to the Deepwater Horizon oil spill in the Gulf of Mexico. UAVSAR is a fully polarimetric L-band Synthetic Aperture Radar (SAR) sensor for obtaining data at high spatial resolutions. Starting a month prior to the UAVSAR collections, visual observations confirmed oil impacts along shorelines within northeastern Barataria Bay waters in eastern coastal Louisiana. UAVSAR data along several flight lines over Barataria Bay were collected on 23 June 2010, including the repeat flight line for which data were collected in June 2009. Our analysis of calibrated single-look complex data for these flight lines shows that structural damage of shoreline marsh accompanied by oil occurrence manifested as anomalous features not evident in pre-spill data. Freeman-Durden (FD) and Cloude-Pottier (CP) decompositions of the polarimetric data and Wishart classifications seeded with the FD and CP classes also highlighted these nearshore features as a change in dominant scattering mechanism. All decompositions and classifications also identify a class of interior marshes that reproduce the spatially extensive changes in backscatter indicated by the pre- and post-spill comparison of multi-polarization radar backscatter data. FD and CP decompositions reveal that those changes indicate a transform of dominant scatter from primarily surface or volumetric to double or even bounce. Given supportive evidence that oil-polluted waters penetrated into the interior marshes, it is reasonable that these backscatter changes correspond with oil exposure; however, multiple factors prevent unambiguous determination of whether UAVSAR detected oil in interior marshes. Full article

(This article belongs to the Special Issue Remote Sensing in Coastal Ecosystem)

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1953 KiB

Open AccessArticle

The Importance of Accounting for Atmospheric Effects in the Application of NDVI and Interpretation of Satellite Imagery Supporting Archaeological Research: The Case Studies of Palaepaphos and Nea Paphos Sites in Cyprus

by Athos Agapiou, Diofantos G. Hadjimitsis, Christiana Papoutsa, Dimitrios D. Alexakis and George Papadavid

Remote Sens. 2011, 3(12), 2605-2629; https://doi.org/10.3390/rs3122605 - 2 Dec 2011

Cited by 71 | Viewed by 11489

Abstract

This paper presents the findings of the impact of atmospheric effects when applied on satellite images intended for supporting archaeological research. The study used eleven multispectral Landsat TM/ETM+ images from 2009 until 2010, acquired over archaeological and agricultural areas. The modified Darkest Pixel [...] Read more.

This paper presents the findings of the impact of atmospheric effects when applied on satellite images intended for supporting archaeological research. The study used eleven multispectral Landsat TM/ETM+ images from 2009 until 2010, acquired over archaeological and agricultural areas. The modified Darkest Pixel (DP) atmospheric correction algorithm was applied, as it is considered one of the most simple and effective atmospheric corrections algorithm. The NDVI equation was applied and its values were evaluated before and after the application of atmospheric correction to satellite images, to estimate its possible effects. The results highlighted that atmospheric correction has a significant impact on the NDVI values. This was especially true in seasons where the vegetation has grown. Although the absolute impact on NDVI, after applying the DP, was small (0.06), it was considered important if multi-temporal time series images need to be evaluated and cross-compared. The NDVI differences, before and after atmospheric correction, were assessed using student’s t-test and the statistical differences were found to be significant. It was shown that relative NDVI difference can be as much as 50%, if atmosphere effects are ignored. Finally, the results had proven that atmospheric corrections can enhance the interpretation of satellite images (especially in cases where optical thickness of water vapour is minimized ≈ 0). This fact can assist in the detection and identification of archaeological crop marks. Therefore, removal of atmospheric effects, for archaeological purposes, was found to be of great importance in improving the image enhancement and NDVI values. Full article

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789 KiB

Open AccessArticle

Soil Moisture Estimations Based on Airborne CAROLS L-Band Microwave Data

by Mickaël Pardé, Mehrez Zribi, Jean-Pierre Wigneron, Monique Dechambre, Pascal Fanise, Yann Kerr, Marc Crapeau, Kauzar Saleh, Jean-Christophe Calvet, Clément Albergel, Arnaud Mialon and Natalie Novello

Remote Sens. 2011, 3(12), 2591-2604; https://doi.org/10.3390/rs3122591 - 2 Dec 2011

Cited by 13 | Viewed by 8467

Abstract

The SMOS satellite mission, launched in 2009, allows global soil moisture estimations to be made using the L-band Microwave Emission of the Biosphere (L-MEB) model, which simulates the L-band microwave emissions produced by the soil–vegetation layer. This model was calibrated using various sources [...] Read more.

The SMOS satellite mission, launched in 2009, allows global soil moisture estimations to be made using the L-band Microwave Emission of the Biosphere (L-MEB) model, which simulates the L-band microwave emissions produced by the soil–vegetation layer. This model was calibrated using various sources of in situ and airborne data. In the present study, we propose to evaluate the L-MEB model on the basis of a large set of airborne data, recorded by the CAROLS radiometer during the course of 20 flights made over South West France (the SMOSMANIA site), and supported by simultaneous soil moisture measurements, made in 2009 and 2010. In terms of volumetric soil moisture, the retrieval accuracy achieved with the L-MEB model, with two default roughness parameters, ranges between 8% and 13%. Local calibrations of the roughness parameter, using data from the 2009 flights for different areas of the site, allowed an accuracy of approximately 5.3% to be achieved with the 2010 CAROLS data. Simultaneously we estimated the vegetation optical thickness (t) and we showed that, when roughness is locally adjusted, MODIS NDVI values are correlated (R² = 0.36) to t. Finally, as a consequence of the significant influence of the roughness parameter on the estimated absolute values of soil moisture, we propose to evaluate the relative variability of the soil moisture, using a default soil roughness parameter. The soil moisture variations are estimated with an uncertainty of approximately 6%. Full article

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Open AccessArticle

Analysis of Vegetation Behavior in a North African Semi-Arid Region, Using SPOT-VEGETATION NDVI Data

by Rim Amri, Mehrez Zribi, Zohra Lili-Chabaane, Benoit Duchemin, Claire Gruhier and Abdelghani Chehbouni

Remote Sens. 2011, 3(12), 2568-2590; https://doi.org/10.3390/rs3122568 - 29 Nov 2011

Cited by 67 | Viewed by 9618

Abstract

The analysis of vegetation dynamics is essential in semi-arid regions, in particular because of the frequent occurrence of long periods of drought. In this paper, multi-temporal series of the Normalized Difference of Vegetation Index (NDVI), derived from SPOT-VEGETATION satellite data between [...] Read more.

The analysis of vegetation dynamics is essential in semi-arid regions, in particular because of the frequent occurrence of long periods of drought. In this paper, multi-temporal series of the Normalized Difference of Vegetation Index (NDVI), derived from SPOT-VEGETATION satellite data between September 1998 and June 2010, were used to analyze the vegetation dynamics over the semi-arid central region of Tunisia. A study of the persistence of three types of vegetation (pastures, annual agriculture and olive trees) is proposed using fractal analysis, in order to gain insight into the stability/instability of vegetation dynamics. In order to estimate the state of vegetation cover stress, we propose evaluating the properties of an index referred to as the Vegetation Anomaly Index (VAI). A positive VAI indicates high vegetation dynamics, whereas a negative VAI indicates the presence of vegetation stress. The VAI is tested for the above three types of vegetation, during the study period from 1998 to 2010, and is compared with other drought indices. The VAI is found to be strongly correlated with precipitation. Full article

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1754 KiB

Open AccessArticle

Exploring Land Use and Land Cover Effects on Air Quality in Central Alabama Using GIS and Remote Sensing

by Stephen D. Superczynski and Sundar A. Christopher

Remote Sens. 2011, 3(12), 2552-2567; https://doi.org/10.3390/rs3122552 - 25 Nov 2011

Cited by 49 | Viewed by 11551

Abstract

Air pollution has been a major topic of debate in highly developed areas over the last quarter century and therefore mitigation of poor air quality for health and environmental reasons has been a primary focus for local governments. Particulate matter, especially finer particles [...] Read more.

Air pollution has been a major topic of debate in highly developed areas over the last quarter century and therefore mitigation of poor air quality for health and environmental reasons has been a primary focus for local governments. Particulate matter, especially finer particles (PM_2.5), is detrimental to human health, and urban expansion is thought to be a contributing factor to enhanced levels of PM_2.5. However, there is limited research on the connection between land use and land cover change (LULC) and PM_2.5 emissions. Using high resolution LANDSAT imagery from the past 12 years along with ground observations of PM_2.5 mass concentrations in the Birmingham, AL region, we explore the links between the PM_2.5 mass concentrations and LULC trends. Utilization of GIS allowed us to seamlessly analyze county-based patterns of LULC change and PM_2.5 concentrations and display them in an easy to interpret manner. We found a moderate-to-strong correlation between PM_2.5observations and the urban area surrounding monitoring sites in 1998 and 2010. We also discuss factors such as local climate and topography and EPA imposed standards that can confound these comparisons. Finally, we determine the next steps that are required to fully quantify the cause and effect between LULC and air quality. Full article

(This article belongs to the Special Issue Remote Sensing in Support of Environmental Policy)

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Remote Sens., Volume 3, Issue 12 (December 2011) – 9 articles , Pages 2552-2726

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