Remote Sensing

647 KiB

Open AccessArticle

Satellite and Ground-Based Sensors for the Urban Heat Island Analysis in the City of Rome

by Roberto Fabrizi, Stefania Bonafoni and Riccardo Biondi

Remote Sens. 2010, 2(5), 1400-1415; https://doi.org/10.3390/rs2051400 - 20 May 2010

Cited by 97 | Viewed by 15203

In this work, the trend of the Urban Heat Island (UHI) of Rome is analyzed by both ground-based weather stations and a satellite-based infrared sensor. First, we have developed a suitable algorithm employing satellite brightness temperatures for the estimation of the air temperature [...] Read more.

In this work, the trend of the Urban Heat Island (UHI) of Rome is analyzed by both ground-based weather stations and a satellite-based infrared sensor. First, we have developed a suitable algorithm employing satellite brightness temperatures for the estimation of the air temperature belonging to the layer of air closest to the surface. UHI spatial characteristics have been assessed using air temperatures measured by both weather stations and brightness temperature maps from the Advanced Along Track Scanning Radiometer (AATSR) on board ENVISAT polar-orbiting satellite. In total, 634 daytime and nighttime scenes taken between 2003 and 2006 have been processed. Analysis of the Canopy Layer Heat Island (CLHI) during summer months reveals a mean growth in magnitude of 3–4 K during nighttime and a negative or almost zero CLHI intensity during daytime, confirmed by the weather stations. Full article

► Show Figures

Figure 1

880 KiB

Open AccessArticle

Modeling Methane Emission from Wetlands in North-Eastern New South Wales, Australia Using Landsat ETM+

by Clement E. Akumu, Sumith Pathirana, Serwan Baban and Daniel Bucher

Remote Sens. 2010, 2(5), 1378-1399; https://doi.org/10.3390/rs2051378 - 17 May 2010

Cited by 30 | Viewed by 13382

Abstract

Natural wetlands constitute a major source of methane emission to the atmosphere, accounting for approximately 32 ± 9.4% of the total methane emission. Estimation of methane emission from wetlands at both local and national scale using process-based models would improve our understanding of [...] Read more.

Natural wetlands constitute a major source of methane emission to the atmosphere, accounting for approximately 32 ± 9.4% of the total methane emission. Estimation of methane emission from wetlands at both local and national scale using process-based models would improve our understanding of their contribution to global methane emission. The aim of the study is to estimate the amount of methane emission from the coastal wetlands in north-eastern New South Wales (NSW), Australia, using Landsat ETM+ and to estimate emission with a temperature increase. Supervised wetland classification was performed using the Maximum Likelihood Standard algorithm. The temperature dependent factor was obtained through land surface temperature (LST) estimation algorithms. Measurements of methane fluxes from the wetlands were performed using static chamber techniques and gas chromatography. A process-based methane emission model, which included productivity factor, wetland area, methane flux, precipitation and evaporation ratio, was used to estimate the amount of methane emission from the wetlands. Geographic information system (GIS) provided the framework for analysis. The variability of methane emission from the wetlands was high, with forested wetlands found to produce the highest amount of methane, i.e., 0.0016 ± 0.00009 teragrams (Tg) in the month of June, 2001. This would increase to 0.0022 ± 0.0001 Tg in the month of June with a 1 °C rise in mean annual temperature by the year 2030 in north-eastern NSW, Australia. Full article

► Show Figures

Figure 1

3128 KiB

Open AccessArticle

Use of Remote Sensing Data and GIS Tools for Seismic Hazard Assessment for Shallow Oilfields and its Impact on the Settlements at Masjed-i-Soleiman Area, Zagros Mountains, Iran

by Hojjat Ollah Safari, Saied Pirasteh, Biswajeet Pradhan and Ladan Khedri Gharibvand

Remote Sens. 2010, 2(5), 1364-1377; https://doi.org/10.3390/rs2051364 - 12 May 2010

Cited by 21 | Viewed by 13908

Abstract

Masjed-i-Soleiman (MIS) is situated in the northern part of the Dezful embayment, which is in the Zagros fold–thrust belt with high seismic activities. MIS faces a shallow buried anticline, formed by the shallowest oilfield with a thick gas cap. The cap rocks of [...] Read more.

Masjed-i-Soleiman (MIS) is situated in the northern part of the Dezful embayment, which is in the Zagros fold–thrust belt with high seismic activities. MIS faces a shallow buried anticline, formed by the shallowest oilfield with a thick gas cap. The cap rocks of this oilfield are highly fractured, which has resulted in leakages from the gas cap. In this paper, we have used remote sensing techniques and image interpretation for the identification of the Niayesh, Lahbari, Andika and MIS fault zones in the studied area. Further, the study exploited seismic potential mapping using the remote sensing techniques. The relationships between the structural controls and localized gas leakage are assessed within the GIS environment. Additionally, field observation data corroborated that the leakages (and seepages) are smashed within the intersection of Niayesh and MIS fault zone, which belongs to the high fractured hinge zone of the MIS anticline. As a result, the reactivation of these active faults may cause large earthquakes with a maximum magnitude of between 6.23 < Ms < 7.05 (Richter scale) and maximum horizontal acceleration 0.26 < a < 0.55 g. Finally, the authors concluded that this anticipated earthquake may cause large scale fracturing of cap rocks, releasing a large volume of H2S gas from the uppermost layer of the reservoir. Full article

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

► Show Figures

Figure 1

1986 KiB

Open AccessArticle

Evaluating Potential of MODIS-based Indices in Determining “Snow Gone” Stage over Forest-dominant Regions

by Navdeep S. Sekhon, Quazi K. Hassan and Robert W. Sleep

Remote Sens. 2010, 2(5), 1348-1363; https://doi.org/10.3390/rs2051348 - 11 May 2010

Cited by 20 | Viewed by 9624

Abstract

“Snow gone” (SGN) stage is one of the critical variables that describe the start of the official forest fire season in the Canadian Province of Alberta. In this paper, our objective is to evaluate the potential of MODIS-based indices for determining the SGN [...] Read more.

“Snow gone” (SGN) stage is one of the critical variables that describe the start of the official forest fire season in the Canadian Province of Alberta. In this paper, our objective is to evaluate the potential of MODIS-based indices for determining the SGN stage. Those included: (i) enhanced vegetation index (EVI), (ii) normalized difference water index (NDWI) using the shortwave infrared (SWIR) spectral bands centered at 1.64 µm (NDWI_1.64µm) and at 2.13 µm (NDWI_2.13µm), and (iii) normalized difference snow index (NDSI). These were calculated using the 500 m 8-day gridded MODIS-based composites of surface reflectance data (i.e., MOD09A1 v.005) for the period 2006–08. We performed a qualitative evaluation of these indices over two forest fire prone natural subregions in Alberta (i.e., central mixedwood and lower boreal highlands). In the process, we generated and compared the natural subregion-specific lookout tower sites average: (i) temporal trends for each of the indices, and (ii) SGN stage using the ground-based observations available from Alberta Sustainable Resource Development. The EVI-values were found to have large uncertainty at the onset of the spring and unable to predict the SGN stages precisely. In terms of NDSI, it showed earlier prediction capabilities. On the contrary, both of the NDWI’s showed distinct pattern (i.e., reached a minimum value before started to increase again during the spring) in relation to observed SGN stages. Thus further analysis was carried out to determine the best predictor by comparing the NDWI’s predicted SGN stages with the ground-based observations at all of the individual lookout tower sites (approximately 120 in total) across the study area. It revealed that NDWI_2.13µm demonstrated better prediction capabilities (i.e., on an average approximately 90% of the observations fell within ±2 periods or ±16 days of deviation) in comparison to NDWI_1.64µm (i.e., on an average approximately 73% of the observations fell within ±2 periods or ±16 days of deviation). Full article

(This article belongs to the Special Issue Multi-Temporal Remote Sensing)

► Show Figures

Figure 1

1376 KiB

Open AccessArticle

Estimating Speed and Direction of Small Dynamic Targets through Optical Satellite Imaging

by Greg Easson, Scott DeLozier and Henrique G. Momm

Remote Sens. 2010, 2(5), 1331-1347; https://doi.org/10.3390/rs2051331 - 7 May 2010

Cited by 14 | Viewed by 9281

Abstract

Moving Target Indicators (MTI) are systems used to distinguish movement from stationary scenes and sometimes to derive the spatial attributes of these objects. These systems are currently used in many sectors such as traffic studies, border surveillance, and military applications. The proposed MTI [...] Read more.

Moving Target Indicators (MTI) are systems used to distinguish movement from stationary scenes and sometimes to derive the spatial attributes of these objects. These systems are currently used in many sectors such as traffic studies, border surveillance, and military applications. The proposed MTI reveals vehicles and their velocities using commercial imagery from a passive optical satellite-mounted sensor. With simple process of image differencing, the MTI can automatically recognize conveyances in motion (speed and direction) represented by polygons formed by a group of pixels from successive images. Micro-change detection with an existing commercial satellite requires special considerations of differences in spatial and spectral resolution between images. Complications involving the movement detection system such as vehicle overlap, vehicle clusters, and zones of low confidence are refined by adding error-reducing modules. This process is tested on a variety of vehicles, their concentrations, and environments, confirming the feasibility of utilizing an MTI with commercial optical satellite imagery for movement recognition and velocity estimation. Full article

► Show Figures

Figure 1

845 KiB

Open AccessArticle

Global Evaluation of Radiosonde Water Vapor Systematic Biases using GPS Radio Occultation from COSMIC and ECMWF Analysis

by Shu-peng Ho, Xinjia Zhou, Ying-Hwa Kuo, Douglas Hunt and Jun-hong Wang

Remote Sens. 2010, 2(5), 1320-1330; https://doi.org/10.3390/rs2051320 - 7 May 2010

Cited by 93 | Viewed by 11965

Abstract

In this study, we compare specific humidity profiles derived from Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) radio occultation (RO) from August to November 2006 with those from different types of radiosonde and from ECMWF global analysis. Comparisons show that COSMIC [...] Read more.

In this study, we compare specific humidity profiles derived from Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) radio occultation (RO) from August to November 2006 with those from different types of radiosonde and from ECMWF global analysis. Comparisons show that COSMIC specific humidity data agree well with ECMWF analysis over different regions of the world for both day and night times. On the contrary, evaluation against COSMIC specific humidity shows a distinct dry bias of Shang-E radiosonde (China) and an obvious wet bias of VIZ-type (USA). No obvious specific humidity biases are found for MRZ (Russia) and MEISEI (Japan) radiosondes. These results demonstrate the usefulness of COSMIC water vapor for quantifying the dry/wet biases among different sensor types. Full article

(This article belongs to the Special Issue Global Positioning Systems (GPS) and Applications)

► Show Figures

Graphical abstract

1579 KiB

Open AccessArticle

Determining Regional Actual Evapotranspiration of Irrigated Crops and Natural Vegetation in the São Francisco River Basin (Brazil) Using Remote Sensing and Penman-Monteith Equation

by Antônio H. de C. Teixeira

Remote Sens. 2010, 2(5), 1287-1319; https://doi.org/10.3390/rs0251287 - 6 May 2010

Cited by 119 | Viewed by 15935

Abstract

To achieve sustainable development and to ensure water availability in hydrological basins, water managers need tools to determine the actual evapotranspiration (ET) on a large scale. Field energy balances from irrigated and natural ecosystems together with a net of agro-meteorological stations were used [...] Read more.

To achieve sustainable development and to ensure water availability in hydrological basins, water managers need tools to determine the actual evapotranspiration (ET) on a large scale. Field energy balances from irrigated and natural ecosystems together with a net of agro-meteorological stations were used to develop two models for ET quantification at basin scale, based on the Penman-Monteith equation. The first model (PM1) uses the resistances to the latent heat fluxes estimated from satellite measurements, while the second one (PM2) is based on the ratio of ET to the reference evapotranspiration (ET₀) and its relation to remote sensing parameters. The models were applied in the Low-Middle São Francisco river basin in Brazil and, after comparison against field results, showed good agreements with PM1 and PM2 explaining, respectively, 79% and 89% of the variances and mean square errors (RMSE) of 0.44 and 0.34 mm d⁻¹. Even though the PM1 model was not chosen for ET calculations, the equation for surface resistance (r_s) was applied to infer the soil moisture conditions in a simplified vegetation classification. The maximum values of r_s were for natural vegetation—caatinga (average of 1,937 s m⁻¹). Wine grape and mango orchard presented similar values around 130 s m⁻¹, while table grape presented the lowest ones, averaging 74 s m⁻¹. Petrolina and Juazeiro, in Pernambuco (PE) and Bahia (BA) states, respectively, were highlighted with the biggest irrigated areas. The highest increments are for vineyards and mango orchards. For the first crop the maximum increment was verified between 2003 and 2004 in Petrolina-PE, when the cultivated area increased 151%. In the case of mango orchards the most significant period was from 2005 to 2006 in Juazeiro-BA (129%). As the best performance was for PM2, it was selected and used to analyse the regional ET at daily and annual scales, making use of Landsat images and a geographic information system for different soil moisture conditions. Considering the daily rates of the regional ET, pixels with values lower than 1.0 mm d⁻¹ occurred outside the rainy season, representing the caatinga species. Values from 1.0 to 5.0 mm d⁻¹ during the driest conditions of the year coincided with irrigated crops, being the highest values for table grapes. The highest accumulated ET values during 2006 were for mango orchards, being around 500–1,300 mm yr⁻¹. Vineyards presented lower values, ranging from 450–800 mm yr⁻¹, while in caatinga they were between 200 and 400 mm yr⁻¹. It could be concluded that irrigated mango orchards and vineyards in that year consumed more water than caatinga by factors of 3 and 2, respectively. The mango orchards and vineyard areas, representing 19.4 and 8.2% of the total irrigated area, respectively, resulting in a total evaporative depletion of 0.22 km³ yr⁻¹ in the growing regions comprised of the agro-meteorological stations. Full article

(This article belongs to the Special Issue Global Croplands)

► Show Figures

Figure 1

628 KiB

Open AccessArticle

Sensitivity Analysis of b-factor in Microwave Emission Model for Soil Moisture Retrieval: A Case Study for SMAP Mission

by Dugwon Seo, Tarendra Lakhankar and Reza Khanbilvardi

Remote Sens. 2010, 2(5), 1273-1286; https://doi.org/10.3390/rs2051273 - 4 May 2010

Cited by 10 | Viewed by 11074

Abstract

Sensitivity analysis is critically needed to better understand the microwave emission model for soil moisture retrieval using passive microwave remote sensing data. The vegetation b-factor along with vegetation water content and surface characteristics has significant impact in model prediction. This study evaluates [...] Read more.

Sensitivity analysis is critically needed to better understand the microwave emission model for soil moisture retrieval using passive microwave remote sensing data. The vegetation b-factor along with vegetation water content and surface characteristics has significant impact in model prediction. This study evaluates the sensitivity of the b-factor, which is function of vegetation type. The analysis is carried out using Passive and Active L and S-band airborne sensor (PALS) and measured field soil moisture from Southern Great Plains experiment (SGP99). The results show that the relative sensitivity of the b-factor is 86% in wet soil condition and 88% in high vegetated condition compared to the sensitivity of the soil moisture. Apparently, the b-factor is found to be more sensitive than the vegetation water content, surface roughness and surface temperature; therefore, the effect of the b-factor is fairly large to the microwave emission in certain conditions. Understanding the dependence of the b-factor on the soil and vegetation is important in studying the soil moisture retrieval algorithm, which can lead to potential improvements in model development for the Soil Moisture Active-Passive (SMAP) mission. Full article

► Show Figures

Graphical abstract

1401 KiB

Open AccessArticle

Forecasting Areas Vulnerable to Forest Conversion in the Tam Dao National Park Region, Vietnam

by Duong Dang Khoi and Yuji Murayama

Remote Sens. 2010, 2(5), 1249-1272; https://doi.org/10.3390/rs2051249 - 30 Apr 2010

Cited by 66 | Viewed by 12888

Abstract

Tam Dao National Park (TDNP) is a remaining primary forest that supports some of the highest levels of biodiversity in Vietnam. Forest conversion due to illegal logging and agricultural expansion is a major problem that is hampering biodiversity conservation efforts in the TDNP [...] Read more.

Tam Dao National Park (TDNP) is a remaining primary forest that supports some of the highest levels of biodiversity in Vietnam. Forest conversion due to illegal logging and agricultural expansion is a major problem that is hampering biodiversity conservation efforts in the TDNP region. Yet, areas vulnerable to forest conversion are unknown. In this paper, we predicted areas vulnerable to forest changes in the TDNP region using multi-temporal remote sensing data and a multi-layer perceptron neural network (MLPNN) with a Markov chain model (MLPNN-M). The MLPNN-M model predicted increasing pressure in the remaining primary forest within the park as well as on the secondary forest in the surrounding areas. The primary forest is predicted to decrease from 18.03% in 2007 to 15.10% in 2014 and 12.66% in 2021. Our results can be used to prioritize locations for future biodiversity conservation and forest management efforts. The combined use of remote sensing and spatial modeling techniques provides an effective tool for monitoring the remaining forests in the TDNP region. Full article

(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)

► Show Figures

Figure 1

1633 KiB

Open AccessArticle

Automatic Detection of Buildings and Changes in Buildings for Updating of Maps

by Leena Matikainen, Juha Hyyppä, Eero Ahokas, Lauri Markelin and Harri Kaartinen

Remote Sens. 2010, 2(5), 1217-1248; https://doi.org/10.3390/rs2051217 - 27 Apr 2010

Cited by 113 | Viewed by 13351

Abstract

There is currently high interest in developing automated methods to assist the updating of map databases. This study presents methods for automatic detection of buildings and changes in buildings from airborne laser scanner and digital aerial image data and shows the potential usefulness [...] Read more.

There is currently high interest in developing automated methods to assist the updating of map databases. This study presents methods for automatic detection of buildings and changes in buildings from airborne laser scanner and digital aerial image data and shows the potential usefulness of the methods with thorough experiments in a 5 km² suburban study area. 96% of buildings larger than 60 m² were correctly detected in the building detection. The completeness and correctness of the change detection for buildings larger than 60 m² were about 85% (including five classes). Most of the errors occurred in small or otherwise problematic buildings. Full article

► Show Figures

Graphical abstract

577 KiB

Open AccessArticle

Applying Multifractal Analysis to Remotely Sensed Data for Assessing PYVV Infection in Potato (Solanum tuberosum L.) Crops

by Perla Chávez, Christian Yarlequé, Oreste Piro, Adolfo Posadas, Víctor Mares, Hildo Loayza, Carlos Chuquillanqui, Percy Zorogastúa, Jaume Flexas and Roberto Quiroz

Remote Sens. 2010, 2(5), 1197-1216; https://doi.org/10.3390/rs2051197 - 27 Apr 2010

Cited by 12 | Viewed by 11154

Abstract

Multispectral reflectance imagery and spectroradiometry can be used to detect stresses affecting crops. Previously, we have shown that changes in spectral reflectance and vegetation indices detected viral infection 14 days before visual symptoms were noticed by the trained eye. Herein we present evidence [...] Read more.

Multispectral reflectance imagery and spectroradiometry can be used to detect stresses affecting crops. Previously, we have shown that changes in spectral reflectance and vegetation indices detected viral infection 14 days before visual symptoms were noticed by the trained eye. Herein we present evidence that shows that the application of multifractal analysis and wavelet transform to spectroradiometrical data improves the diagnostic power of the remote sensing-based methodology proposed in our previous work. The diagnosis of viral infection was effectively enhanced, providing the earliest detection ever reported, as anomalies were detected 29 and 33 days before appearance of visual symptoms in two experiments. Full article

► Show Figures

Figure 1

Journal Menu

Journal Browser

Remote Sens., Volume 2, Issue 5 (May 2010) – 11 articles , Pages 1197-1415

Further Information

Guidelines

MDPI Initiatives

Follow MDPI