Next Issue
Previous Issue

E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Table of Contents

Remote Sens., Volume 6, Issue 10 (October 2014) , Pages 9145-10251

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Readerexternal link to open them.
View options order results:
result details:
Displaying articles 1-49
Export citation of selected articles as:
Open AccessArticle The Spectral Response of the Landsat-8 Operational Land Imager
Remote Sens. 2014, 6(10), 10232-10251; https://doi.org/10.3390/rs61010232
Received: 29 July 2014 / Revised: 4 October 2014 / Accepted: 9 October 2014 / Published: 23 October 2014
Cited by 80 | Viewed by 10508 | PDF Full-text (3738 KB) | HTML Full-text | XML Full-text
Abstract
Abstract: This paper discusses the pre-launch spectral characterization of the Operational Land Imager (OLI) at the component, assembly and instrument levels and relates results of those measurements to artifacts observed in the on-orbit imagery. It concludes that the types of artifacts observed and
[...] Read more.
Abstract: This paper discusses the pre-launch spectral characterization of the Operational Land Imager (OLI) at the component, assembly and instrument levels and relates results of those measurements to artifacts observed in the on-orbit imagery. It concludes that the types of artifacts observed and their magnitudes are consistent with the results of the pre-launch characterizations. The OLI in-band response was characterized both at the integrated instrument level for a sampling of detectors and by an analytical stack-up of component measurements. The out-of-band response was characterized using a combination of Focal Plane Module (FPM) level measurements and optical component level measurements due to better sensitivity. One of the challenges of a pushbroom design is to match the spectral responses for all detectors so that images can be flat-fielded regardless of the spectral nature of the targets in the imagery. Spectral variability can induce striping (detector-to-detector variation), banding (FPM-to-FPM variation) and other artifacts in the final data products. Analyses of the measured spectral response showed that the maximum discontinuity between FPMs due to spectral filter differences is 0.35% for selected targets for all bands except for Cirrus, where there is almost no signal. The average discontinuity between FPMs is 0.12% for the same targets. These results were expected and are in accordance with the OLI requirements. Pre-launch testing identified low levels (within requirements) of spectral crosstalk amongst the three HgCdTe (Cirrus, SWIR1 and SWIR2) bands of the OLI and on-orbit data confirms this crosstalk in the imagery. Further post-launch analyses and simulations revealed that the strongest crosstalk effect is from the SWIR1 band to the Cirrus band; about 0.2% of SWIR1 signal leaks into the Cirrus. Though the total crosstalk signal is only a few counts, it is evident in some scenes when the in-band cirrus signal is very weak. In moist cirrus-free atmospheres and over typical land surfaces, at least 30% of the cirrus signal was due to the SWIR1 band. In the SWIR1 and SWIR2 bands, crosstalk accounts for no more than 0.15% of the total signal. Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
Figures

Graphical abstract

Open AccessArticle Comparison of Different GPP Models in China Using MODIS Image and ChinaFLUX Data
Remote Sens. 2014, 6(10), 10215-10231; https://doi.org/10.3390/rs61010215
Received: 22 July 2014 / Revised: 20 October 2014 / Accepted: 21 October 2014 / Published: 23 October 2014
Cited by 18 | Viewed by 2487 | PDF Full-text (1214 KB) | HTML Full-text | XML Full-text
Abstract
Accurate quantification of gross primary production (GPP) at regional and global scales is essential for carbon budgets and climate change studies. Five models, the vegetation photosynthesis model (VPM), the temperature and greenness model (TG), the alpine vegetation model (AVM), the greenness and radiation
[...] Read more.
Accurate quantification of gross primary production (GPP) at regional and global scales is essential for carbon budgets and climate change studies. Five models, the vegetation photosynthesis model (VPM), the temperature and greenness model (TG), the alpine vegetation model (AVM), the greenness and radiation model (GR), and the MOD17 algorithm, were tested and calibrated at eight sites in China during 2003–2005. Results indicate that the first four models provide more reliable GPP estimation than MOD17 products/algorithm, although MODIS GPP products show better performance in grasslands, croplands, and mixed forest (MF). VPM and AVM produce better estimates in forest sites (R2 = 0.68 and 0.67, respectively); AVM and TG models show satisfactory GPP estimates for grasslands (R2 = 0.91 and 0.9, respectively). In general, the VPM model is the most suitable model for GPP estimation for all kinds of land cover types in China, with R2 higher than 0.34 and root mean square error (RMSE) lower than 48.79%. The relationships between eddy CO2 flux and model parameters (Enhanced Vegetation Index (EVI), photosynthetically active radiation (PAR), land surface temperature (LST), air temperature, and Land Surface Water Index (LSWI)) are further analyzed to investigate the model’s application to various land cover types, which will be of great importance for studying the effects of climatic factors on ecosystem performances. Full article
Figures

Graphical abstract

Open AccessArticle Assessing the Performance of MODIS NDVI and EVI for Seasonal Crop Yield Forecasting at the Ecodistrict Scale
Remote Sens. 2014, 6(10), 10193-10214; https://doi.org/10.3390/rs61010193
Received: 25 August 2014 / Revised: 10 October 2014 / Accepted: 11 October 2014 / Published: 23 October 2014
Cited by 20 | Viewed by 3867 | PDF Full-text (8790 KB) | HTML Full-text | XML Full-text
Abstract
Crop yield forecasting plays a vital role in coping with the challenges of the impacts of climate change on agriculture. Improvements in the timeliness and accuracy of yield forecasting by incorporating near real-time remote sensing data and the use of sophisticated statistical methods
[...] Read more.
Crop yield forecasting plays a vital role in coping with the challenges of the impacts of climate change on agriculture. Improvements in the timeliness and accuracy of yield forecasting by incorporating near real-time remote sensing data and the use of sophisticated statistical methods can improve our capacity to respond effectively to these challenges. The objectives of this study were (i) to investigate the use of derived vegetation indices for the yield forecasting of spring wheat (Triticum aestivum L.) from the Moderate resolution Imaging Spectroradiometer (MODIS) at the ecodistrict scale across Western Canada with the Integrated Canadian Crop Yield Forecaster (ICCYF); and (ii) to compare the ICCYF-model based forecasts and their accuracy across two spatial scales-the ecodistrict and Census Agricultural Region (CAR), namely in CAR with previously reported ICCYF weak performance. Ecodistricts are areas with distinct climate, soil, landscape and ecological aspects, whereas CARs are census-based/statistically-delineated areas. Agroclimate variables combined respectively with MODIS-NDVI and MODIS-EVI indices were used as inputs for the in-season yield forecasting of spring wheat during the 2000–2010 period. Regression models were built based on a procedure of a leave-one-year-out. The results showed that both agroclimate + MODIS-NDVI and agroclimate + MODIS-EVI performed equally well predicting spring wheat yield at the ECD scale. The mean absolute error percentages (MAPE) of the models selected from both the two data sets ranged from 2% to 33% over the study period. The model efficiency index (MEI) varied between −1.1 and 0.99 and −1.8 and 0.99, respectively for the agroclimate + MODIS-NDVI and agroclimate + MODIS-EVI data sets. Moreover, significant improvement in forecasting skill (with decreasing MAPE of 40% and 5 times increasing MEI, on average) was obtained at the finer, ecodistrict spatial scale, compared to the coarser CAR scale. Forecast models need to consider the distribution of extreme values of predictor variables to improve the selection of remote sensing indices. Our findings indicate that statistical-based forecasting error could be significantly reduced by making use of MODIS-EVI and NDVI indices at different times in the crop growing season and within different sub-regions. Full article
(This article belongs to the Special Issue Remote Sensing in Food Production and Food Security)
Figures

Graphical abstract

Open AccessArticle A Method to Reconstruct the Solar-Induced Canopy Fluorescence Spectrum from Hyperspectral Measurements
Remote Sens. 2014, 6(10), 10171-10192; https://doi.org/10.3390/rs61010171
Received: 16 June 2014 / Revised: 12 August 2014 / Accepted: 2 September 2014 / Published: 23 October 2014
Cited by 14 | Viewed by 2642 | PDF Full-text (2798 KB) | HTML Full-text | XML Full-text
Abstract
A method for canopy Fluorescence Spectrum Reconstruction (FSR) is proposed in this study, which can be used to retrieve the solar-induced canopy fluorescence spectrum over the whole chlorophyll fluorescence emission region from 640–850 nm. Firstly, the radiance of the solar-induced chlorophyll fluorescence (Fs)
[...] Read more.
A method for canopy Fluorescence Spectrum Reconstruction (FSR) is proposed in this study, which can be used to retrieve the solar-induced canopy fluorescence spectrum over the whole chlorophyll fluorescence emission region from 640–850 nm. Firstly, the radiance of the solar-induced chlorophyll fluorescence (Fs) at five absorption lines of the solar spectrum was retrieved by a Spectral Fitting Method (SFM). The Singular Vector Decomposition (SVD) technique was then used to extract three basis spectra from a training dataset simulated by the model SCOPE (Soil Canopy Observation, Photochemistry and Energy fluxes). Finally, these basis spectra were linearly combined to reconstruct the Fs spectrum, and the coefficients of them were determined by Weighted Linear Least Squares (WLLS) fitting with the five retrieved Fs values. Results for simulated datasets indicate that the FSR method could accurately reconstruct the Fs spectra from hyperspectral measurements acquired by instruments of high Spectral Resolution (SR) and Signal to Noise Ratio (SNR). The FSR method was also applied to an experimental dataset acquired in a diurnal experiment. The diurnal change of the reconstructed Fs spectra shows that the Fs radiance around noon was higher than that in the morning and afternoon, which is consistent with former studies. Finally, the potential and limitations of this method are discussed. Full article
Figures

Graphical abstract

Open AccessArticle Automatic Detection of Small Single Trees in the Forest-Tundra Ecotone Using Airborne Laser Scanning
Remote Sens. 2014, 6(10), 10152-10170; https://doi.org/10.3390/rs61010152
Received: 3 April 2014 / Revised: 13 October 2014 / Accepted: 14 October 2014 / Published: 23 October 2014
Cited by 4 | Viewed by 2063 | PDF Full-text (2244 KB) | HTML Full-text | XML Full-text
Abstract
A large proportion of Norway’s land area is occupied by the forest-tundra ecotone. The vegetation of this temperature-sensitive ecosystem between mountain forest and the alpine zone is expected to be highly affected by climate change and effective monitoring techniques are required. For the
[...] Read more.
A large proportion of Norway’s land area is occupied by the forest-tundra ecotone. The vegetation of this temperature-sensitive ecosystem between mountain forest and the alpine zone is expected to be highly affected by climate change and effective monitoring techniques are required. For the detection of such small pioneer trees, airborne laser scanning (ALS) has been proposed as a useful tool employing laser height data. The objective of this study was to assess the capability of an unsupervised classification for automated monitoring programs of small individual trees using high-density ALS data. Field and ALS data were collected along a 1500 km long transect stretching from northern to southern Norway. Different laser and tree height thresholds were tested in various combinations within an unsupervised classification of tree and nontree raster cells employing different cell sizes. Suitable initial cell sizes for the exclusion of large treeless areas as well as an optimal cell size for tree cell detection were determined. High rates of successful tree cell detection involved high levels of commission error at lower laser height thresholds, however, exceeding the 20 cm laser height threshold, the rates of commission error decreased substantially with a still satisfying rate of successful tree cell detection. Full article
Figures

Graphical abstract

Open AccessArticle Non-Vegetated Playa Morphodynamics Using Multi-Temporal Landsat Imagery in a Semi-Arid Endorheic Basin: Salar de Uyuni, Bolivia
Remote Sens. 2014, 6(10), 10131-10151; https://doi.org/10.3390/rs61010131
Received: 31 July 2014 / Revised: 15 October 2014 / Accepted: 15 October 2014 / Published: 22 October 2014
Cited by 7 | Viewed by 2329 | PDF Full-text (9066 KB) | HTML Full-text | XML Full-text
Abstract
Playas in endorheic basins are of environmental value and highly scientific because of their natural habitats of a wide variety of species and indicators for climatic changes and tectonic activities within continents. Remote sensing, due to its capability of acquiring repetitive data with
[...] Read more.
Playas in endorheic basins are of environmental value and highly scientific because of their natural habitats of a wide variety of species and indicators for climatic changes and tectonic activities within continents. Remote sensing, due to its capability of acquiring repetitive data with synoptic coverage, provides a unique tool to monitor and collect spatial information about playas. Most studies have concentrated on evaporite mineral distribution using remote sensing techniques but research about grain size distribution and geomorphologic changes in playas has been rarely reported. We analysed playa morphodynamics using Landsat time series data in a semi-arid endorheic basin, Salar de Uyuni in Bolivia. The spectral libraries explaining the relationship between surface reflectance and surficial materials are extracted from the Landsat image on 11 November 2012, the collected samples in the area and the precipitation data. Such spectral libraries are then applied to the classification of the other Landsat images from 1985–2011 using maximum likelihood classifier. Four types of surficial materials on the playa are identified: salty surface, silt-rich surface, clay-rich surface and pure salt. The silt-rich surface is related to crevasse splays and river banks while the clay-rich surface is associated with floodplain and channel depressions. The classification results show that the silt-rich surface tends to have a positive relationship with annual precipitation, whereas the salty surface negatively correlates with annual precipitation and there is no correlation between clay-rich surface and annual precipitation. Salty surfaces seem to consist primarily of clay due to their similar characteristics in response to precipitation changes. The classification results also show the development of a crevasse splay and avulsions. The results demonstrate the potential of Landsat imagery to determine the grain size and sedimentary facies distribution on playas in endorheic basins. Full article
Figures

Graphical abstract

Open AccessArticle Graph-Based Divide and Conquer Method for Parallelizing Spatial Operations on Vector Data
Remote Sens. 2014, 6(10), 10107-10130; https://doi.org/10.3390/rs61010107
Received: 29 May 2014 / Revised: 14 October 2014 / Accepted: 14 October 2014 / Published: 22 October 2014
Cited by 2 | Viewed by 2258 | PDF Full-text (2058 KB) | HTML Full-text | XML Full-text
Abstract
In computer science, dependence analysis determines whether or not it is safe to parallelize statements in programs. In dealing with the data-intensive and computationally intensive spatial operations in processing massive volumes of geometric features, this dependence can be well utilized for exploiting the
[...] Read more.
In computer science, dependence analysis determines whether or not it is safe to parallelize statements in programs. In dealing with the data-intensive and computationally intensive spatial operations in processing massive volumes of geometric features, this dependence can be well utilized for exploiting the parallelism. In this paper, we propose a graph-based divide and conquer method for parallelizing spatial operations (GDCMPSO) on vector data. It can represent spatial data dependences in spatial operations through representing the vector features as graph vertices, and their computational dependences as graph edges. By this way, spatial operations can be parallelized in three steps: partitioning the graph into graph components with inter-component edges firstly, simultaneously processing multiple subtasks indicated by the graph components secondly and finally handling remainder tasks denoted by the inter-component edges. To demonstrate how it works, buffer operation and intersection operation under this paradigm are conducted. In a 12-core environment, the two spatial operations both gain obvious performance improvements, and the speedups are more than eight. The testing results suggest that GDCMPSO contributes to a method for parallelizing spatial operations and can greatly improve the computing efficiency on multi-core architectures. Full article
(This article belongs to the Special Issue Remote Sensing Dedicated to Geographical Conditions Monitoring)
Figures

Graphical abstract

Open AccessArticle Global Retrieval of Diatom Abundance Based on Phytoplankton Pigments and Satellite Data
Remote Sens. 2014, 6(10), 10089-10106; https://doi.org/10.3390/rs61010089
Received: 12 June 2014 / Revised: 28 September 2014 / Accepted: 12 October 2014 / Published: 22 October 2014
Cited by 15 | Viewed by 4143 | PDF Full-text (3336 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Diatoms are the major marine primary producers on the global scale and, recently, several methods have been developed to retrieve their abundance or dominance from satellite remote sensing data. In this work, we highlight the importance of the Southern Ocean (SO) in developing
[...] Read more.
Diatoms are the major marine primary producers on the global scale and, recently, several methods have been developed to retrieve their abundance or dominance from satellite remote sensing data. In this work, we highlight the importance of the Southern Ocean (SO) in developing a global algorithm for diatom using an Abundance Based Approach (ABA). A large global in situ data set of phytoplankton pigments was compiled, particularly with more samples collected in the SO. We revised the ABA to take account of the information on the penetration depth (Zpd) and to improve the relationship between diatoms and total chlorophyll-a (TChla). The results showed that there is a distinct relationship between diatoms and TChla in the SO, and a new global model (ABAZpd) improved the estimation of diatoms abundance by 28% in the SO compared with the original ABA model. In addition, we developed a regional model for the SO which further improved the retrieval of diatoms by 17% compared with the global ABAZpd model. As a result, we found that diatom may be more abundant in the SO than previously thought. Linear trend analysis of diatom abundance using the regional model for the SO showed that there are statistically significant trends, both increasing and decreasing, in diatom abundance over the past eleven years in the region. Full article
Figures

Graphical abstract

Open AccessArticle The Uncertainty of Plot-Scale Forest Height Estimates from Complementary Spaceborne Observations in the Taiga-Tundra Ecotone
Remote Sens. 2014, 6(10), 10070-10088; https://doi.org/10.3390/rs61010070
Received: 30 June 2014 / Revised: 16 September 2014 / Accepted: 29 September 2014 / Published: 21 October 2014
Cited by 9 | Viewed by 2370 | PDF Full-text (3305 KB) | HTML Full-text | XML Full-text
Abstract
Satellite-based estimates of vegetation structure capture broad-scale vegetation characteristics as well as differences in vegetation structure at plot-scales. Active remote sensing from laser altimetry and radar systems is regularly used to measure vegetation height and infer vegetation structural attributes, however, the current uncertainty
[...] Read more.
Satellite-based estimates of vegetation structure capture broad-scale vegetation characteristics as well as differences in vegetation structure at plot-scales. Active remote sensing from laser altimetry and radar systems is regularly used to measure vegetation height and infer vegetation structural attributes, however, the current uncertainty of their spaceborne measurements is likely to mask actual plot-scale differences in vertical structures in sparse forests. In the taiga (boreal forest)—tundra ecotone (TTE) the accumulated effect of subtle plot-scale differences in vegetation height across broad-scales may be significant. This paper examines the uncertainty of plot-scale forest canopy height measurements in northern Siberia Larix stands by combining complementary canopy surface elevations derived from satellite photogrammetry and ground elevations derived from the Geosciences Laser Altimeter System (GLAS) from the ICESat-1 satellite. With a linear model, spaceborne-derived canopy height measurements at the plot-scale predicted TTE stand height ~5 m–~10 m tall (R2 = 0.55, bootstrapped 95% confidence interval of R2 = 0.36–0.74) with an uncertainty ranging from ±0.86 m–1.37 m. A larger sample may mitigate the broad uncertainty of the model fit, however, the methodology provides a means for capturing plot-scale canopy height and its uncertainty from spaceborne data at GLAS footprints in sparse TTE forests and may serve as a basis for scaling up plot-level TTE vegetation height measurements to forest patches. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
Figures

Graphical abstract

Open AccessArticle Slope Estimation from ICESat/GLAS
Remote Sens. 2014, 6(10), 10051-10069; https://doi.org/10.3390/rs61010051
Received: 30 June 2014 / Revised: 24 September 2014 / Accepted: 24 September 2014 / Published: 21 October 2014
Cited by 15 | Viewed by 3989 | PDF Full-text (3259 KB) | HTML Full-text | XML Full-text
Abstract
We present a novel technique to infer ground slope angle from waveform LiDAR, known as the independent slope method (ISM). The technique is applied to large footprint waveforms (\(\sim\) mean diameter) from the Ice, Cloud and Land Elevation Satellite (ICESat) Geoscience Laser Altimeter
[...] Read more.
We present a novel technique to infer ground slope angle from waveform LiDAR, known as the independent slope method (ISM). The technique is applied to large footprint waveforms (\(\sim\) mean diameter) from the Ice, Cloud and Land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) to produce a slope dataset of near-global coverage at \(0.5^{\circ} \times 0.5^{\circ}\) resolution. ISM slope estimates are compared against high resolution airborne LiDAR slope measurements for nine sites across three continents. ISM slope estimates compare better with the aircraft data (R\(^{2}=0.87\) and RMSE\(=5.16^{\circ}\)) than the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) inferred slopes (R\(^{2}=0.71\) and RMSE\(=8.69^{\circ}\)) ISM slope estimates are concurrent with GLAS waveforms and can be used to correct biophysical parameters, such as tree height and biomass. They can also be fused with other DEMs, such as SRTM, to improve slope estimates. Full article
Figures

Graphical abstract

Open AccessArticle Application of a Remote Sensing Method for Estimating Monthly Blue Water Evapotranspiration in Irrigated Agriculture
Remote Sens. 2014, 6(10), 10033-10050; https://doi.org/10.3390/rs61010033
Received: 15 May 2014 / Revised: 14 October 2014 / Accepted: 15 October 2014 / Published: 21 October 2014
Cited by 5 | Viewed by 2932 | PDF Full-text (1030 KB) | HTML Full-text | XML Full-text
Abstract
In this paper we show the potential of combining actual evapotranspiration (ETactual) series obtained from remote sensing and land surface modelling, to monitor community practice in irrigation at a monthly scale. This study estimates blue water evapotranspiration (ETb) in
[...] Read more.
In this paper we show the potential of combining actual evapotranspiration (ETactual) series obtained from remote sensing and land surface modelling, to monitor community practice in irrigation at a monthly scale. This study estimates blue water evapotranspiration (ETb) in irrigated agriculture in two study areas: the Horn of Africa (2010–2012) and the province of Sichuan (China) (2001–2010). Both areas were affected by a drought event during the period of analysis, but are different in terms of water control and storage infrastructure. The monthly ETb results were separated by water source—surface water, groundwater or conjunctive use—based on the Global Irrigated Area Map and were analyzed per country/province. The preliminary results show that the temporal signature of the total ETb allows seasonal patterns to be distinguished within a year and inter-annual ETb dynamics. In Ethiopia, ETb decreased during the dry year, which suggests that less irrigation water was applied. Moreover, an increase of groundwater use was observed at the expense of surface water use. In Sichuan province, ETb in the dry year was of similar magnitude to the previous years or increased, especially in the month of August, which points to a higher amount of irrigation water used. This could be explained by the existence of infrastructure for water storage and water availability, in particular surface water. The application presented in this paper is innovative and has the potential to assess the existence of irrigation, the source of irrigation water, the duration and variability in time, at pixel and country scales, and is especially useful to monitor irrigation practice during periods of drought. Full article
Figures

Graphical abstract

Open AccessArticle Irrigated Grassland Monitoring Using a Time Series of TerraSAR-X and COSMO-SkyMed X-Band SAR Data
Remote Sens. 2014, 6(10), 10002-10032; https://doi.org/10.3390/rs61010002
Received: 16 June 2014 / Revised: 25 September 2014 / Accepted: 13 October 2014 / Published: 20 October 2014
Cited by 20 | Viewed by 4206 | PDF Full-text (6480 KB) | HTML Full-text | XML Full-text
Abstract
The objective of this study was to analyze the sensitivity of radar signals in the X-band in irrigated grassland conditions. The backscattered radar signals were analyzed according to soil moisture and vegetation parameters using linear regression models. A time series of radar (TerraSAR-X
[...] Read more.
The objective of this study was to analyze the sensitivity of radar signals in the X-band in irrigated grassland conditions. The backscattered radar signals were analyzed according to soil moisture and vegetation parameters using linear regression models. A time series of radar (TerraSAR-X and COSMO-SkyMed) and optical (SPOT and LANDSAT) images was acquired at a high temporal frequency in 2013 over a small agricultural region in southeastern France. Ground measurements were conducted simultaneously with the satellite data acquisitions during several grassland growing cycles to monitor the evolution of the soil and vegetation characteristics. The comparison between the Normalized Difference Vegetation Index (NDVI) computed from optical images and the in situ Leaf Area Index (LAI) showed a logarithmic relationship with a greater scattering for the dates corresponding to vegetation well developed before the harvest. The correlation between the NDVI and the vegetation parameters (LAI, vegetation height, biomass, and vegetation water content) was high at the beginning of the growth cycle. This correlation became insensitive at a certain threshold corresponding to high vegetation (LAI ~2.5 m2/m2). Results showed that the radar signal depends on variations in soil moisture, with a higher sensitivity to soil moisture for biomass lower than 1 kg/m². HH and HV polarizations had approximately similar sensitivities to soil moisture. The penetration depth of the radar wave in the X-band was high, even for dense and high vegetation; flooded areas were visible in the images with higher detection potential in HH polarization than in HV polarization, even for vegetation heights reaching 1 m. Lower sensitivity was observed at the X-band between the radar signal and the vegetation parameters with very limited potential of the X-band to monitor grassland growth. These results showed that it is possible to track gravity irrigation and soil moisture variations from SAR X-band images acquired at high spatial resolution (an incidence angle near 30°). Full article
Figures

Graphical abstract

Open AccessArticle Now You See It… Now You Don’t: Understanding Airborne Mapping LiDAR Collection and Data Product Generation for Archaeological Research in Mesoamerica
Remote Sens. 2014, 6(10), 9951-10001; https://doi.org/10.3390/rs6109951
Received: 29 July 2014 / Revised: 21 September 2014 / Accepted: 8 October 2014 / Published: 20 October 2014
Cited by 28 | Viewed by 4788 | PDF Full-text (25791 KB) | HTML Full-text | XML Full-text
Abstract
In this paper we provide a description of airborne mapping LiDAR, also known as airborne laser scanning (ALS), technology and its workflow from mission planning to final data product generation, with a specific emphasis on archaeological research. ALS observations are highly customizable, and
[...] Read more.
In this paper we provide a description of airborne mapping LiDAR, also known as airborne laser scanning (ALS), technology and its workflow from mission planning to final data product generation, with a specific emphasis on archaeological research. ALS observations are highly customizable, and can be tailored to meet specific research needs. Thus it is important for an archaeologist to fully understand the options available during planning, collection and data product generation before commissioning an ALS survey, to ensure the intended research questions can be answered with the resultant data products. Also this knowledge is of great use for the researcher trying to understand the quality and limitations of existing datasets collected for other purposes. Throughout the paper we use examples from archeological ALS projects to illustrate the key concepts of importance for the archaeology researcher. Full article
(This article belongs to the Special Issue New Perspectives of Remote Sensing for Archaeology)
Figures

Graphical abstract

Open AccessArticle Evaluation of Coastline Changes under Human Intervention Using Multi-Temporal High-Resolution Images: A Case Study of the Zhoushan Islands, China
Remote Sens. 2014, 6(10), 9930-9950; https://doi.org/10.3390/rs6109930
Received: 31 March 2014 / Revised: 16 September 2014 / Accepted: 8 October 2014 / Published: 17 October 2014
Cited by 9 | Viewed by 2653 | PDF Full-text (4049 KB) | HTML Full-text | XML Full-text
Abstract
Continued sea-level rise and coastal development have led to considerable concerns on coastline changes along inhabited islands. Analysis of long-term coastline changes of islands is however limited due to unavailable data and the cost of field work. In this study, high-resolution images taken
[...] Read more.
Continued sea-level rise and coastal development have led to considerable concerns on coastline changes along inhabited islands. Analysis of long-term coastline changes of islands is however limited due to unavailable data and the cost of field work. In this study, high-resolution images taken from 1970–2011 at an interval of about 10 years and topographic maps were collected to determine coastline changes and their drivers in the Zhoushan Islands, China. Results show that nearly all inhabited islands appeared to have noteworthy seaward expansion during the past four decades. Coastline change rates varied among islands, and the annual change rate of Zhoushan Island (the main island) reached 12.83 ± 0.17 m/year during the same period. Since 2003, the study area has been dominated by artificial coast. The proportion of harbor/port and urban/industrial coast has significantly increased, while rocky coasts and shelter-farm coasts have shrunk greatly. Preliminary analysis of drivers for these coastline changes across the Zhoushan Islands highlights the roles of human policies during different periods as well as location, which were the dominant factors controlling the great spatial and temporal complexity of coastline changes of the major islands. Sediment supply from the Yangtze River decreased after the completion of the Three Gorges Dam in 2003; however, the Zhoushan coast rapidly accreted seaward during the last decade and the artificial siltation, coastal engineering, and harbor dredging materials could be responsible for the observed coastline changes. Pressured by rapid development of the port industry, the Zhoushan coast may face unprecedented challenges in coastal use in the near future. This research provides the basic background information for future studies on coastal protection and management. Full article
Open AccessArticle Assessment of Total Suspended Sediment Distribution under Varying Tidal Conditions in Deep Bay: Initial Results from HJ-1A/1B Satellite CCD Images
Remote Sens. 2014, 6(10), 9911-9929; https://doi.org/10.3390/rs6109911
Received: 17 June 2014 / Revised: 25 September 2014 / Accepted: 27 September 2014 / Published: 17 October 2014
Cited by 12 | Viewed by 2455 | PDF Full-text (33282 KB) | HTML Full-text | XML Full-text
Abstract
Using Deep Bay in China as an example, an effective method for the retrieval of total suspended sediment (TSS) concentration using HJ-1A/1B satellite images is proposed. The factors driving the variation of the TSS spatial distribution are also discussed. Two field surveys, conducted
[...] Read more.
Using Deep Bay in China as an example, an effective method for the retrieval of total suspended sediment (TSS) concentration using HJ-1A/1B satellite images is proposed. The factors driving the variation of the TSS spatial distribution are also discussed. Two field surveys, conducted on August 29 and October 26, 2012, showed that there was a strong linear relationship (R2 = 0.9623) between field-surveyed OBS (optical backscatter) measurements (5-31NTU) and laboratory-analyzed TSS concentrations (9.89–35.58 mg/L). The COST image-based atmospheric correction procedure and the pseudo-invariant features (PIF) method were combined to remove the atmospheric effects from the total radiance measurements obtained with different CCDs onboard the HJ-1A/1B satellites. Then, a simple and practical retrieval model was established based on the relationship between the satellite-corrected reflectance band ratio of band 3 and band 2 (Rrs3/Rrs2) and in-situ TSS measurements. The R2 of the regression relationship was 0.807, and the mean relative error (MRE) was 12.78%, as determined through in-situ data validation. Finally, the influences of tide cycles, wind factors (direction and speed) and other factors on the variation of the TSS spatial pattern observed from HJ-1A/1B satellite images from September through November of 2008 are discussed. The results show that HJ-1A/1B satellite CCD images can be used to estimate TSS concentrations under different tides in the study area over synoptic scales without using simultaneous in-situ atmospheric parameters and spectrum data. These findings provide strong informational support for numerical simulation studies on the combined influence of tide cycles and other associated hydrologic elements in Deep Bay. Full article
Open AccessArticle Is Forest Restoration in the Southwest China Karst Promoted Mainly by Climate Change or Human-Induced Factors?
Remote Sens. 2014, 6(10), 9895-9910; https://doi.org/10.3390/rs6109895
Received: 1 July 2014 / Revised: 25 September 2014 / Accepted: 1 October 2014 / Published: 17 October 2014
Cited by 21 | Viewed by 2541 | PDF Full-text (5552 KB) | HTML Full-text | XML Full-text
Abstract
The Southwest China Karst, the largest continuous karst zone in the world, has suffered serious rock desertification due to the large population pressure in the area. Recent trend analyses have indicated general greening trends in this region. The region has experienced mild climate
[...] Read more.
The Southwest China Karst, the largest continuous karst zone in the world, has suffered serious rock desertification due to the large population pressure in the area. Recent trend analyses have indicated general greening trends in this region. The region has experienced mild climate change, and yet significant land use changes, such as afforestation and reforestation. In addition, out-migration has occurred. Whether climate change or human-induced factors, i.e., ecological afforestation projects and out-migration have primarily promoted forest restoration in this region was investigated in this study, using Guizhou Province as the study area. Based on Moderate-Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data, we found general greening trends of the forest from 2000 to 2010. About 89% of the forests have experienced an increase in the annual NDVI, and among which, about 41% is statistically significant. For the summer season, more than 65% of the forests have increases in summer NDVI, and about 16% of the increases are significant. The strongest greening trends mainly occurred in the karst areas. Meanwhile, annual average and summer average temperature in this region have increased and the precipitation in most of the region has decreased, although most of these changes were not statistically significant (p > 0.1). A site-based regression analysis using 19 climate stations with minimum land use changes showed that a warming climate coupled with a decrease in precipitation explained some of the changes in the forest NDVI, but the results were not conclusive. The major changes were attributed to human-induced factors, especially in the karst areas. The implications of an ecological afforestation project and out-migration for forest restoration were also discussed, and the need for further investigations at the household level to better understand the out-migration–environment relationship was identified. Full article
Figures

Graphical abstract

Open AccessArticle Spatial Patterns of Fire Recurrence Using Remote Sensing and GIS in the Brazilian Savanna: Serra do Tombador Nature Reserve, Brazil
Remote Sens. 2014, 6(10), 9873-9894; https://doi.org/10.3390/rs6109873
Received: 5 March 2014 / Revised: 26 September 2014 / Accepted: 29 September 2014 / Published: 16 October 2014
Cited by 10 | Viewed by 3118 | PDF Full-text (4159 KB) | HTML Full-text | XML Full-text
Abstract
The Cerrado is the second largest biome in Brazil after the Amazon and is the savanna with the highest biodiversity in the world. Serra Tombador Natural Reserve (STNR) is the largest private reserve located in Goiás State, and the fourth largest in the
[...] Read more.
The Cerrado is the second largest biome in Brazil after the Amazon and is the savanna with the highest biodiversity in the world. Serra Tombador Natural Reserve (STNR) is the largest private reserve located in Goiás State, and the fourth largest in the Cerrado biome. The present study aimed to map the burnt areas and to describe the spatial patterns of fire recurrence and its interactions with the classes of land-cover that occurred in STNR and its surroundings in the period between 2001 and 2010. Several Landsat TM images acquired around the months of July, August and September, coinciding with the region’s dry season when fire events intensify, were employed to monitor burnt areas. Fire scars were mapped using the supervised Mahalanobis-distance classifier and further refined using expert visual interpretation. Burnt area patterns were described by spatial landscape metrics. The effects of fire on landscape structure were obtained by comparing results among different land-cover classes, and results summarized in terms of fire history and frequencies. During the years covered by the study, 69% of the areas analyzed had fire events. The year with the largest burnt area was 2004, followed by 2001, 2007 and 2010. Thus, the largest fire events occurred in a 3-year cycle, which is compatible with other areas of the Brazilian savanna. The regions with higher annual probabilities of fire recurrence occur in the buffer zone around the park. The year 2004 also had the highest number of burnt area patches (831). In contrast, the burnt area in 2007 showed the most extensive fires with low number of patches (82). The physiognomies that suffered most fires were the native savanna formations. The study also identified areas where fires are frequently recurrent, highlighting priority areas requiring special attention. Thus, the methodology adopted in this study assists in monitoring and recovery of areas affected by fire over time. Full article
Figures

Graphical abstract

Open AccessArticle Evaluating Saturation Correction Methods for DMSP/OLS Nighttime Light Data: A Case Study from China’s Cities
Remote Sens. 2014, 6(10), 9853-9872; https://doi.org/10.3390/rs6109853
Received: 30 June 2014 / Revised: 26 September 2014 / Accepted: 1 October 2014 / Published: 16 October 2014
Cited by 23 | Viewed by 2516 | PDF Full-text (3971 KB) | HTML Full-text | XML Full-text
Abstract
Remotely sensed nighttime lights (NTL) datasets derived from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) have been identified as a good indicator of the urbanization process and have been widely used to study such demographic and economic variables as population distribution
[...] Read more.
Remotely sensed nighttime lights (NTL) datasets derived from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) have been identified as a good indicator of the urbanization process and have been widely used to study such demographic and economic variables as population distribution and density, electricity consumption, and carbon emission. However, one issue must be considered in the application of NTL data, i.e., saturation in the bright cores of urban centers. In this study, we evaluate four correction methods in China’s cities: the linear regression model and the cubic regression model at the regional level, and the Human Settlement Index (HSI) and the Vegetation Adjusted NTL Urban Index (VANUI) at a pixel level. The results suggest that both correction methods at the regional level improve the correlation between NTL data and socioeconomic variables. However, since the methods can only be used on saturated pixels, the correction effects are limited, as the saturated area in Chinese cities is rather small. HSI and VANUI increase the inter-urban variability within certain cities, especially when their vegetation health and abundance is negatively correlated with NTL. However, the indices may induce bias when applied in a large region with a diverse natural environment and vegetation, and the application of HSI with a relatively high sensitivity of HSI to NDVI may be limited as NTL approaches maximum. Proper methods for reducing saturation effects should thus vary with different study areas and research purposes. Full article
(This article belongs to the Special Issue Remote Sensing with Nighttime Lights)
Figures

Graphical abstract

Open AccessArticle Land Surface Temperature Retrieval from Landsat 8 TIRS—Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method
Remote Sens. 2014, 6(10), 9829-9852; https://doi.org/10.3390/rs6109829
Received: 18 April 2014 / Revised: 17 September 2014 / Accepted: 18 September 2014 / Published: 15 October 2014
Cited by 96 | Viewed by 5942 | PDF Full-text (1564 KB) | HTML Full-text | XML Full-text
Abstract
Accurate inversion of land surface geo/biophysical variables from remote sensing data for earth observation applications is an essential and challenging topic for the global change research. Land surface temperature (LST) is one of the key parameters in the physics of earth surface processes
[...] Read more.
Accurate inversion of land surface geo/biophysical variables from remote sensing data for earth observation applications is an essential and challenging topic for the global change research. Land surface temperature (LST) is one of the key parameters in the physics of earth surface processes from local to global scales. The importance of LST is being increasingly recognized and there is a strong interest in developing methodologies to measure LST from the space. Landsat 8 Thermal Infrared Sensor (TIRS) is the newest thermal infrared sensor for the Landsat project, providing two adjacent thermal bands, which has a great benefit for the LST inversion. In this paper, we compared three different approaches for LST inversion from TIRS, including the radiative transfer equation-based method, the split-window algorithm and the single channel method. Four selected energy balance monitoring sites from the Surface Radiation Budget Network (SURFRAD) were used for validation, combining with the MODIS 8 day emissivity product. For the investigated sites and scenes, results show that the LST inverted from the radiative transfer equation-based method using band 10 has the highest accuracy with RMSE lower than 1 K, while the SW algorithm has moderate accuracy and the SC method has the lowest accuracy. Full article
Figures

Graphical abstract

Open AccessArticle Forest Stand Size-Species Models Using Spatial Analyses of Remotely Sensed Data
Remote Sens. 2014, 6(10), 9802-9828; https://doi.org/10.3390/rs6109802
Received: 19 May 2014 / Revised: 19 September 2014 / Accepted: 24 September 2014 / Published: 14 October 2014
Cited by 3 | Viewed by 2118 | PDF Full-text (5333 KB) | HTML Full-text | XML Full-text
Abstract
Regression models to predict stand size classes (sawtimber and saplings) and categories of species (hardwood and softwood) from fractal dimensions (FD) and Moran’s I derived from Landsat Thematic Mapper (TM) data were developed. Three study areas (Oakmulgee National Forest, Bankhead National Forest, and
[...] Read more.
Regression models to predict stand size classes (sawtimber and saplings) and categories of species (hardwood and softwood) from fractal dimensions (FD) and Moran’s I derived from Landsat Thematic Mapper (TM) data were developed. Three study areas (Oakmulgee National Forest, Bankhead National Forest, and Talladega National Forest) were randomly selected and used to develop the prediction models, while one study area, Chattahoochee National Forest, was saved for validation. This study has shown that these spatial analytical indices (FD and Moran’s I) can distinguish between different forest trunk size classes and different categories of species (hardwood and softwood) using Landsat TM data. The results of this study also revealed that there is a linear relationship between each one of the spatial indices and the percentages of sawtimber–saplings size classes and hardwood–softwood categories of species. Given the high number of factors causing errors in the remotely sensed data as well as the Forest Inventory Analysis (FIA) data sets and compared to other studies in the research literature, the sawtimber–saplings models and hardwood–softwood models were reasonable in terms of significance and the levels of explained variance for both spatial indices FD and Moran’s I. The mean absolute percentage errors associated with the stand size classes prediction models and categories of species prediction models that take topographical elevation into consideration ranged from 4.4% to 19.8% and from 12.1% to 18.9%, respectively, while the root mean square errors ranged from 10% to 14% and from 11% to 13%, respectively. Full article
(This article belongs to the Special Issue Earth Observation for Ecosystems Monitoring in Space and Time)
Open AccessArticle Diurnal Dynamics of Wheat Evapotranspiration Derived from Ground-Based Thermal Imagery
Remote Sens. 2014, 6(10), 9775-9801; https://doi.org/10.3390/rs6109775
Received: 14 February 2014 / Revised: 20 September 2014 / Accepted: 24 September 2014 / Published: 14 October 2014
Cited by 1 | Viewed by 2194 | PDF Full-text (3276 KB) | HTML Full-text | XML Full-text
Abstract
The latent heat flux, one of the key components of the surface energy balance, can be inferred from remotely sensed thermal infrared data. However, discrepancies between modeled and observed evapotranspiration are large. Thermal cameras might provide a suitable tool for model evaluation under
[...] Read more.
The latent heat flux, one of the key components of the surface energy balance, can be inferred from remotely sensed thermal infrared data. However, discrepancies between modeled and observed evapotranspiration are large. Thermal cameras might provide a suitable tool for model evaluation under variable atmospheric conditions. Here, we evaluate the results from the Penman-Monteith, surface energy balance and Bowen ratio approaches, which estimate the diurnal course of latent heat fluxes at a ripe winter wheat stand using measured and modeled temperatures. Under overcast conditions, the models perform similarly, and radiometric image temperatures are linearly correlated with the inverted aerodynamic temperature. During clear sky conditions, the temperature of the wheat ear layer could be used to predict daytime turbulent fluxes (root mean squared error and mean absolute error: 20–35 W∙m2, r2: 0.76–0.88), whereas spatially-averaged temperatures caused underestimation of pre-noon and overestimation of afternoon fluxes. Errors are dependent on the models’ ability to simulate diurnal hysteresis effects and are largest during intermittent clouds, due to the discrepancy between the timing of image capture and the time needed for the leaf-air-temperature gradient to adapt to changes in solar radiation. During such periods, we suggest using modeled surface temperatures for temporal upscaling and the validation of image data. Full article
Open AccessArticle Oil Palm Tree Detection with High Resolution Multi-Spectral Satellite Imagery
Remote Sens. 2014, 6(10), 9749-9774; https://doi.org/10.3390/rs6109749
Received: 19 April 2014 / Revised: 20 August 2014 / Accepted: 25 August 2014 / Published: 13 October 2014
Cited by 22 | Viewed by 4664 | PDF Full-text (13092 KB) | HTML Full-text | XML Full-text
Abstract
Oil palm tree is an important cash crop in Thailand. To maximize the productivity from planting, oil palm plantation managers need to know the number of oil palm trees in the plantation area. In order to obtain this information, an approach for palm
[...] Read more.
Oil palm tree is an important cash crop in Thailand. To maximize the productivity from planting, oil palm plantation managers need to know the number of oil palm trees in the plantation area. In order to obtain this information, an approach for palm tree detection using high resolution satellite images is proposed. This approach makes it possible to count the number of oil palm trees in a plantation. The process begins with the selection of the vegetation index having the highest discriminating power between oil palm trees and background. The index having highest discriminating power is then used as the primary feature for palm tree detection. We hypothesize that oil palm trees are located at the local peak within the oil palm area. To enhance the separability between oil palm tree crowns and background, the rank transformation is applied to the index image. The local peak on the enhanced index image is then detected by using the non-maximal suppression algorithm. Since both rank transformation and non-maximal suppression are window based, semi-variogram analysis is used to determine the appropriate window size. The performance of the proposed method was tested on high resolution satellite images. In general, our approach uses produced very accurate results, e.g., about 90 percent detection rate when compared with manual labeling. Full article
Figures

Graphical abstract

Open AccessArticle GPR Signal Characterization for Automated Landmine and UXO Detection Based on Machine Learning Techniques
Remote Sens. 2014, 6(10), 9729-9748; https://doi.org/10.3390/rs6109729
Received: 25 June 2014 / Revised: 24 September 2014 / Accepted: 29 September 2014 / Published: 13 October 2014
Cited by 15 | Viewed by 2609 | PDF Full-text (3256 KB) | HTML Full-text | XML Full-text
Abstract
Landmine clearance is an ongoing problem that currently affects millions of people around the world. This study evaluates the effectiveness of ground penetrating radar (GPR) in demining and unexploded ordnance detection using 2.3-GHz and 1-GHz high-frequency antennas. An automated detection tool based on
[...] Read more.
Landmine clearance is an ongoing problem that currently affects millions of people around the world. This study evaluates the effectiveness of ground penetrating radar (GPR) in demining and unexploded ordnance detection using 2.3-GHz and 1-GHz high-frequency antennas. An automated detection tool based on machine learning techniques is also presented with the aim of automatically detecting underground explosive artifacts. A GPR survey was conducted on a designed scenario that included the most commonly buried items in historic battle fields, such as mines, projectiles and mortar grenades. The buried targets were identified using both frequencies, although the higher vertical resolution provided by the 2.3-GHz antenna allowed for better recognition of the reflection patterns. The targets were also detected automatically using machine learning techniques. Neural networks and logistic regression algorithms were shown to be able to discriminate between potential targets and clutter. The neural network had the most success, with accuracies ranging from 89% to 92% for the 1-GHz and 2.3-GHz antennas, respectively. Full article
(This article belongs to the Special Issue Close-Range Remote Sensing by Ground Penetrating Radar)
Open AccessArticle GPR Laboratory Tests For Railways Materials Dielectric Properties Assessment
Remote Sens. 2014, 6(10), 9712-9728; https://doi.org/10.3390/rs6109712
Received: 30 June 2014 / Revised: 30 August 2014 / Accepted: 10 September 2014 / Published: 13 October 2014
Cited by 13 | Viewed by 2516 | PDF Full-text (5674 KB) | HTML Full-text | XML Full-text
Abstract
In railways Ground Penetrating Radar (GPR) studies, the evaluation of materials dielectric properties is critical as they are sensitive to water content, to petrographic type of aggregates and to fouling condition of the ballast. Under the load traffic, maintenance actions and climatic effects,
[...] Read more.
In railways Ground Penetrating Radar (GPR) studies, the evaluation of materials dielectric properties is critical as they are sensitive to water content, to petrographic type of aggregates and to fouling condition of the ballast. Under the load traffic, maintenance actions and climatic effects, ballast condition change due to aggregate breakdown and to subgrade soils pumping, mainly on existing lines with no sub ballast layer. The main purpose of this study was to validate, under controlled conditions, the dielectric values of materials used in Portuguese railways, in order to improve the GPR interpretation using commercial software and consequently the management maintenance planning. Different materials were tested and a broad range of in situ conditions were simulated in laboratory, in physical models. GPR tests were performed with five antennas with frequencies between 400 and 1800 MHz. The variation of the dielectric properties was measured, and the range of values that can be obtained for different material condition was defined. Additionally, in situ GPR measurements and test pits were performed for validation of the dielectric constant of clean ballast. The results obtained are analyzed and the main conclusions are presented herein. Full article
(This article belongs to the Special Issue Close-Range Remote Sensing by Ground Penetrating Radar)
Figures

Graphical abstract

Open AccessArticle Supporting Urban Energy Efficiency with Volunteered Roof Information and the Google Maps API
Remote Sens. 2014, 6(10), 9691-9711; https://doi.org/10.3390/rs6109691
Received: 4 July 2014 / Revised: 29 August 2014 / Accepted: 29 September 2014 / Published: 13 October 2014
Cited by 3 | Viewed by 3083 | PDF Full-text (5327 KB) | HTML Full-text | XML Full-text
Abstract
The Heat Energy Assessment Technologies (HEAT) project uses high-resolution airborne thermal imagery, Geographic Object-Based Image Analysis (GEOBIA), and a Geoweb environment to allow the residents of Calgary, Alberta, Canada to visualize the amount and location of waste heat leaving their houses, communities, and
[...] Read more.
The Heat Energy Assessment Technologies (HEAT) project uses high-resolution airborne thermal imagery, Geographic Object-Based Image Analysis (GEOBIA), and a Geoweb environment to allow the residents of Calgary, Alberta, Canada to visualize the amount and location of waste heat leaving their houses, communities, and the city. To ensure the accuracy of these measures, the correct emissivity of roof materials needs to be known. However, roof material information is not readily available in the Canadian public domain. To overcome this challenge, a unique Volunteered Geographic Information (VGI) application was developed using Google Street View that engages citizens to classify the roof materials of single dwelling residences in a simple and intuitive manner. Since data credibility, quality, and accuracy are major concerns when using VGI, a private Multiple Listing Services (MLS) dataset was used for cross-verification. From May–November 2013, 1244 volunteers from 85 cities and 14 countries classified 1815 roofs in the study area. Results show (I) a 72% match between the VGI and MLS data; and (II) in the majority of cases, roofs with greater than, or equal to five contributions have the same material defined in both datasets. Additionally, this research meets new challenges to the GEOBIA community to incorporate existing GIS vector data within an object-based workflow and engages the public to provide volunteered information for urban objects from which new geo-intelligence is created in support of urban energy efficiency. Full article
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
Figures

Graphical abstract

Open AccessArticle Subsidence Detected by Multi-Pass Differential SAR Interferometry in the Cassino Plain (Central Italy): Joint Effect of Geological and Anthropogenic Factors?
Remote Sens. 2014, 6(10), 9676-9690; https://doi.org/10.3390/rs6109676
Received: 31 July 2014 / Revised: 22 September 2014 / Accepted: 23 September 2014 / Published: 13 October 2014
Cited by 9 | Viewed by 2063 | PDF Full-text (12769 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
In the present work, the Differential SAR Interferometry (DInSAR) technique has been applied to study the surface movements affecting the sedimentary basin of Cassino municipality. Two datasets of SAR images, provided by ERS 1-2 and Envisat missions, have been acquired from 1992 to
[...] Read more.
In the present work, the Differential SAR Interferometry (DInSAR) technique has been applied to study the surface movements affecting the sedimentary basin of Cassino municipality. Two datasets of SAR images, provided by ERS 1-2 and Envisat missions, have been acquired from 1992 to 2010. Such datasets have been processed independently each other and with different techniques nevertheless providing compatible results. DInSAR data show a subsidence rate mostly located in the northeast side of the city, with a subsidence rate decreasing from about 5–6 mm/yr in the period 1992–2000 to about 1–2 mm/yr between 2004 and 2010, highlighting a progressive reduction of the phenomenon. Based on interferometric results and geological/geotechnical observations, the explanation of the detected movements allows to confirm the anthropogenic (surface effect due to building construction) and geological causes (thickness and characteristics of the compressible stratum). Full article
Figures

Graphical abstract

Open AccessArticle Wheat Yield Forecasting for Punjab Province from Vegetation Index Time Series and Historic Crop Statistics
Remote Sens. 2014, 6(10), 9653-9675; https://doi.org/10.3390/rs6109653
Received: 18 April 2014 / Revised: 14 September 2014 / Accepted: 16 September 2014 / Published: 13 October 2014
Cited by 17 | Viewed by 3246 | PDF Full-text (2897 KB) | HTML Full-text | XML Full-text
Abstract
Policy makers, government planners and agricultural market participants in Pakistan require accurate and timely information about wheat yield and production. Punjab Province is by far the most important wheat producing region in the country. The manual collection of field data and data processing
[...] Read more.
Policy makers, government planners and agricultural market participants in Pakistan require accurate and timely information about wheat yield and production. Punjab Province is by far the most important wheat producing region in the country. The manual collection of field data and data processing for crop forecasting by the provincial government requires significant amounts of time before official reports can be released. Several studies have shown that wheat yield can be effectively forecast using satellite remote sensing data. In this study, we developed a methodology for estimating wheat yield and area for Punjab Province from freely available Landsat and MODIS satellite imagery approximately six weeks before harvest. Wheat yield was derived by regressing reported yield values against time series of four different peak-season MODIS-derived vegetation indices. We also tested deriving wheat area from the same MODIS time series using a regression-tree approach. Among the four evaluated indices, WDRVI provided more consistent and accurate yield forecasts compared to NDVI, EVI2 and saturation-adjusted normalized difference vegetation index (SANDVI). The lowest RMSE values at the district level for forecast versus reported yield were found when using six or more years of training data. Forecast yield for the 2007/2008 to 2012/2013 growing seasons were within 0.2% and 11.5% of final reported values. Absolute deviations of wheat area and production forecasts from reported values were slightly greater compared to using the previous year's or the three- or six-year moving average values, implying that 250-m MODIS data does not provide sufficient spatial resolution for providing improved wheat area and production forecasts. Full article
Figures

Graphical abstract

Open AccessReview Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives
Remote Sens. 2014, 6(10), 9600-9652; https://doi.org/10.3390/rs6109600
Received: 30 May 2014 / Revised: 24 September 2014 / Accepted: 25 September 2014 / Published: 13 October 2014
Cited by 76 | Viewed by 3560 | PDF Full-text (1447 KB) | HTML Full-text | XML Full-text | Correction
Abstract
Landslides represent major natural hazards, which cause every year significant loss of lives and damages to buildings, properties and lifelines. In the last decades, a significant increase in landslide frequency took place, in concomitance to climate change and the expansion of urbanized areas.
[...] Read more.
Landslides represent major natural hazards, which cause every year significant loss of lives and damages to buildings, properties and lifelines. In the last decades, a significant increase in landslide frequency took place, in concomitance to climate change and the expansion of urbanized areas. Remote sensing techniques represent a powerful tool for landslide investigation: applications are traditionally divided into three main classes, although this subdivision has some limitations and borders are sometimes fuzzy. The first class comprehends techniques for landslide recognition, i.e., the mapping of past or active slope failures. The second regards landslide monitoring, which entails both ground deformation measurement and the analysis of any other changes along time (e.g., land use, vegetation cover). The third class groups methods for landslide hazard analysis and forecasting. The aim of this paper is to give an overview on the applications of remote-sensing techniques for the three categories of landslide investigations, focusing on the achievements of the last decade, being that previous studies have already been exhaustively reviewed in the existing literature. At the end of the paper, a new classification of remote-sensing techniques that may be pertinently adopted for investigating specific typologies of soil and rock slope failures is proposed. Full article
Open AccessArticle Small Footprint Full-Waveform Metrics Contribution to the Prediction of Biomass in Tropical Forests
Remote Sens. 2014, 6(10), 9576-9599; https://doi.org/10.3390/rs6109576
Received: 20 June 2014 / Revised: 28 September 2014 / Accepted: 29 September 2014 / Published: 10 October 2014
Cited by 18 | Viewed by 2617 | PDF Full-text (3306 KB) | HTML Full-text | XML Full-text
Abstract
We tested metrics from full-waveform (FW) LiDAR (light detection and ranging) as predictors for forest basal area (BA) and aboveground biomass (AGB), in a tropical moist forest. Three levels of metrics are tested: (i) peak-level, based on each return echo; (ii) pulse-level, based
[...] Read more.
We tested metrics from full-waveform (FW) LiDAR (light detection and ranging) as predictors for forest basal area (BA) and aboveground biomass (AGB), in a tropical moist forest. Three levels of metrics are tested: (i) peak-level, based on each return echo; (ii) pulse-level, based on the whole return signal from each emitted pulse; and (iii) plot-level, simulating a large footprint LiDAR dataset. Several of the tested metrics have significant correlation, with two predictors, found by stepwise regression, in particular: median distribution of the height above ground (nZmedian) and fifth percentile of total pulse return intensity (i_tot5th). The former contained the most information and explained 58% and 62% of the variance in AGB and BA values; stepwise regression left us with two and four predictors, respectively, explaining 65% and 79% of the variance. For BA, the predictors were standard deviation, median and fifth percentile of total return pulse intensity (i_totstdDev, i_totmedian and i_tot5th) and nZmedian, whereas for AGB, only the last two were used. The plot-based metric showed that the median height of echo count (HOMTC) performs best, with very similar results as nZmedian, as expected. Cross-validation allowed the analysis of residuals and model robustness. We discuss our results considering our specific case scenario of a complex forest structure with a high degree of variability in terms of biomass. Full article
Figures

Graphical abstract

Open AccessReview Assessing Land Degradation and Desertification Using Vegetation Index Data: Current Frameworks and Future Directions
Remote Sens. 2014, 6(10), 9552-9575; https://doi.org/10.3390/rs6109552
Received: 6 August 2014 / Revised: 12 September 2014 / Accepted: 23 September 2014 / Published: 10 October 2014
Cited by 54 | Viewed by 6143 | PDF Full-text (1063 KB) | HTML Full-text | XML Full-text
Abstract
Land degradation and desertification has been ranked as a major environmental and social issue for the coming decades. Thus, the observation and early detection of degradation is a primary objective for a number of scientific and policy organisations, with remote sensing methods being
[...] Read more.
Land degradation and desertification has been ranked as a major environmental and social issue for the coming decades. Thus, the observation and early detection of degradation is a primary objective for a number of scientific and policy organisations, with remote sensing methods being a candidate choice for the development of monitoring systems. This paper reviews the statistical and ecological frameworks of assessing land degradation and desertification using vegetation index data. The development of multi-temporal analysis as a desertification assessment technique is reviewed, with a focus on how current practice has been shaped by controversy and dispute within the literature. The statistical techniques commonly employed are examined from both a statistical as well as ecological point of view, and recommendations are made for future research directions. The scientific requirements for degradation and desertification monitoring systems identified here are: (I) the validation of methodologies in a robust and comparable manner; and (II) the detection of degradation at minor intensities and magnitudes. It is also established that the multi-temporal analysis of vegetation index data can provide a sophisticated measure of ecosystem health and variation, and that, over the last 30 years, considerable progress has been made in the respective research. Full article
(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)
Figures

Graphical abstract

Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top