Remote Sensing

2181 KiB

Open AccessArticle

Application of a Terrestrial Laser Scanner (TLS) to the Study of the Séchilienne Landslide (Isère, France)

by Johan Kasperski, Christophe Delacourt, Pascal Allemand, Pierre Potherat, Marion Jaud and Eric Varrel

Remote Sens. 2010, 2(12), 2785-2802; https://doi.org/10.3390/rs122785 - 17 Dec 2010

Cited by 85 | Viewed by 10067

Abstract

The active Séchilienne landslide (Isère, France) has been continuously monitored by tacheometry, radar and extensometry devices for 25 years. Indeed, if the 3 mil. m3 of rocks in the active zone named ―Ruines‖ fell down, the debris would dam the Romanche valley. The [...] Read more.

The active Séchilienne landslide (Isère, France) has been continuously monitored by tacheometry, radar and extensometry devices for 25 years. Indeed, if the 3 mil. m3 of rocks in the active zone named ―Ruines‖ fell down, the debris would dam the Romanche valley. The breaking of the dam by overtopping and rapid erosion would bring a catastrophic flood and other dramatic consequences throughout the valley. Given the rockfall hazard in the most active zone, it is impossible to use targets in this area: Only reflectorless remote sensing techniques can provide information. A time-series of seven Terrestrial Laser Scanner (TLS) point clouds acquired between 2004 and 2007 enable us to monitor the 3D displacements of the whole scanned area, although point coverage is not homogeneous. From this sequential monitoring, the volume of registered collapses can be deduced and the landslide movement along the main geological structures can be inferred. From monitoring associated subsidence and toppling observed on TLS data, it can be deduced that blocks rearrangements are linked to structural settings and that the Séchilienne landslide is complex. To conclude, TLS point clouds enable an accurate monitoring of the evolution of the inaccessible "Ruines" area and, proven its ability to provide reliable kinematic information, even in areas where on-site instrumentation is infeasible. Full article

(This article belongs to the Special Issue LiDAR)

► Show Figures

Graphical abstract

706 KiB

Open AccessLetter

Estimating Urban Heat Island Effects on the Temperature Series of Uccle (Brussels, Belgium) Using Remote Sensing Data and a Land Surface Scheme

by Rafiq Hamdi

Remote Sens. 2010, 2(12), 2773-2784; https://doi.org/10.3390/rs2122773 - 10 Dec 2010

Cited by 40 | Viewed by 10760

Abstract

In this letter, the urban heat island effects on the temperature time series of Uccle (Brussels, Belgium) during the summers months 1960–1999 was estimated using both ground-based weather stations and remote sensing imagery, combined with a numerical land surface scheme including state-of-the-art urban [...] Read more.

In this letter, the urban heat island effects on the temperature time series of Uccle (Brussels, Belgium) during the summers months 1960–1999 was estimated using both ground-based weather stations and remote sensing imagery, combined with a numerical land surface scheme including state-of-the-art urban parameterization, the Town Energy Balance Scheme. Analysis of urban warming based on remote sensing method reveals that the urban bias on minimum temperature is rising at a higher rate, 2.5 times (2.85 ground-based observed) more, than on maximum temperature, with a linear trend of 0.15 °C (0.19 °C ground-based observed) and 0.06 °C (0.06 °C ground-based observed) per decade respectively. The results based on remote sensing imagery are compatible with estimates of urban warming based on weather stations. Therefore, the technique presented in this work is a useful tool in estimating the urban heat island contamination in long time series, countering the drawbacks of a ground-observational approach. Full article

(This article belongs to the Special Issue Urban Remote Sensing)

► Show Figures

Graphical abstract

1188 KiB

Open AccessArticle

Application of Object Based Classification and High Resolution Satellite Imagery for Savanna Ecosystem Analysis

by Cerian Gibbes, Sanchayeeta Adhikari, Luke Rostant, Jane Southworth and Youliang Qiu

Remote Sens. 2010, 2(12), 2748-2772; https://doi.org/10.3390/rs2122748 - 10 Dec 2010

Cited by 42 | Viewed by 8600

Abstract

Savanna ecosystems are an important component of dryland regions and yet are exceedingly difficult to study using satellite imagery. Savannas are composed are varying amounts of trees, shrubs and grasses and typically traditional classification schemes or vegetation indices cannot differentiate across class type. [...] Read more.

Savanna ecosystems are an important component of dryland regions and yet are exceedingly difficult to study using satellite imagery. Savannas are composed are varying amounts of trees, shrubs and grasses and typically traditional classification schemes or vegetation indices cannot differentiate across class type. This research utilizes object based classification (OBC) for a region in Namibia, using IKONOS imagery, to help differentiate tree canopies and therefore woodland savanna, from shrub or grasslands. The methodology involved the identification and isolation of tree canopies within the imagery and the creation of tree polygon layers had an overall accuracy of 84%. In addition, the results were scaled up to a corresponding Landsat image of the same region, and the OBC results compared to corresponding pixel values of NDVI. The results were not compelling, indicating once more the problems of these traditional image analysis techniques for savanna ecosystems. Overall, the use of the OBC holds great promise for this ecosystem and could be utilized more frequently in studies of vegetation structure. Full article

► Show Figures

Figure 1

1220 KiB

Open AccessArticle

The Browning of Alaska’s Boreal Forest

by Mary Beth Parent and David Verbyla

Remote Sens. 2010, 2(12), 2729-2747; https://doi.org/10.3390/rs2122729 - 08 Dec 2010

Cited by 48 | Viewed by 9327

Abstract

We used twelve Landsat scenes from the 1980s–2009 and regional 2000–2009 MODIS data to examine the long-term trend in the normalized difference vegetation index (NDVI) within unburned areas of the Alaskan boreal forest. Our analysis shows that there has been a declining trend [...] Read more.

We used twelve Landsat scenes from the 1980s–2009 and regional 2000–2009 MODIS data to examine the long-term trend in the normalized difference vegetation index (NDVI) within unburned areas of the Alaskan boreal forest. Our analysis shows that there has been a declining trend in NDVI in this region, with the strongest “browning trend” occurring in eastern Alaska where the climate during the growing season is relatively dry and warm. Possible reasons for the "browning trend" are decreased vegetation due to temperature-induced drought stress and increased infestations of insect pests. Full article

► Show Figures

Figure 1

447 KiB

Open AccessArticle

Using the Surface Reflectance MODIS Terra Product to Estimate Turbidity in Tampa Bay, Florida

by Max J. Moreno-Madrinan, Mohammad Z. Al-Hamdan, Douglas L. Rickman and Frank E. Muller-Karger

Remote Sens. 2010, 2(12), 2713-2728; https://doi.org/10.3390/rs2122713 - 07 Dec 2010

Cited by 51 | Viewed by 12657

Abstract

Turbidity is a commonly-used index of the factors that determine light penetration in the water column. Consistent estimation of turbidity is crucial to design environmental and restoration management plans, to predict fate of possible pollutants, and to estimate sedimentary fluxes into the ocean. [...] Read more.

Turbidity is a commonly-used index of the factors that determine light penetration in the water column. Consistent estimation of turbidity is crucial to design environmental and restoration management plans, to predict fate of possible pollutants, and to estimate sedimentary fluxes into the ocean. Traditional methods monitoring fixed geographical locations at fixed intervals may not be representative of the mean water turbidity in estuaries between intervals, and can be expensive and time consuming. Although remote sensing offers a good solution to this limitation, it is still not widely used due in part to required complex processing of imagery. There are satellite-derived products, including the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra surface reflectance daily product (MOD09GQ) Band 1 (620–670 nm) which are now routinely available at 250 m spatial resolution and corrected for atmospheric effect. This study shows this product to be useful to estimate turbidity in Tampa Bay, Florida, after rainfall events (R² = 0.76, n = 34). Within Tampa Bay, Hillsborough Bay (HB) and Old Tampa Bay (OTB) presented higher turbidity compared to Middle Tampa Bay (MTB) and Lower Tampa Bay (LTB). Full article

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

► Show Figures

Graphical abstract

725 KiB

Open AccessArticle

Temporal and Spatial Aspects of Snow Distribution in the Nam Co Basin on the Tibetan Plateau from MODIS Data

by Jan Kropacek, Chen Feng, Markus Alle, Shichang Kang and Volker Hochschild

Remote Sens. 2010, 2(12), 2700-2712; https://doi.org/10.3390/rs2122700 - 07 Dec 2010

Cited by 24 | Viewed by 8074

Abstract

Large areas of the Tibetan plateau are only covered by a sparse network of ground snow sampling stations, while the snow cover is highly heterogeneously distributed due to wind, topography etc. Nevertheless, the snow accumulation and spatial patterns play an important role [...] Read more.

Large areas of the Tibetan plateau are only covered by a sparse network of ground snow sampling stations, while the snow cover is highly heterogeneously distributed due to wind, topography etc. Nevertheless, the snow accumulation and spatial patterns play an important role in the hydrological cycle. It releases moisture during the dry spring period before the onset of the monsoon season. Widely used MODIS snow cover products have been available globally since 2002. The understanding of the temporal and spatial distribution of snow cover in a given region calls for a comprehensive data representation method. In this paper a method to visualize both spatial and temporal aspects of snow cover distribution from MODIS 8-day composite data is presented. It is based on RGB display of the snow cover data which is grouped according to season. The RGB syntheses of snow cover distribution (RSD) were generated for the Nam Co Basin in the central part of the Tibetan Plateau during the years of 2002–2009. An alternating pattern of monsoon and autumn snow cover was identified in the western part of the basin which corresponds to the biennial character of the variations of the Indian monsoon. Monsoon snow cover was found in RSD images for the years 2002, 2004 and 2008 whereas in years 2003 and 2009 the autumn snow cover is dominant. The eastern part of the basin does not follow this general pattern since it is affected by the so called “lake effect”, which is a snow fall induced by the passing of dry and cold westerlies over the lake surface during the winter months. The years 2002, 2006 and 2007 were identified as years with a particularly strong lake effect from the RSD images. Areas with permanent snow cover and areas that were snow free were both found to be relatively stable. Comparison of the lake effect at Nam Co with nearby Siling Co, where the lake effect is smaller or absent, suggests that the presence of an effective barrier on the opposite side of the lake is a prerequisite for the occurrence of the strong lake effect. Full article

► Show Figures

Figure 1

222 KiB

Open AccessArticle

Relationships Between Errors Propagated in Fraction of Vegetation Cover by Algorithms Based on a Two-Endmember Linear Mixture Model

by Kenta Obata and Hiroki Yoshioka

Remote Sens. 2010, 2(12), 2680-2699; https://doi.org/10.3390/rs2122680 - 02 Dec 2010

Cited by 6 | Viewed by 6144

Abstract

Remotely sensed reflectance spectra may be biased by several intervening factors, and the biases are propagated into estimations of the fraction of vegetation cover (FVC) by algorithms based on a linear mixture model (LMM). The errors propagated in FVCs depend on the retrieval [...] Read more.

Remotely sensed reflectance spectra may be biased by several intervening factors, and the biases are propagated into estimations of the fraction of vegetation cover (FVC) by algorithms based on a linear mixture model (LMM). The errors propagated in FVCs depend on the retrieval algorithm used, due to differences in the assumptions of the model as well as constraints employed in the algorithm. These differences should be fully understood prior to algorithm selection for practical applications. Although numerous studies have investigated the relationships between errors propagated by different algorithms, these relationships have not been fully understood from a deterministic perspective. This study introduces a technique for deriving the analytical underpinnings of error propagation in FVC based on several LMM-based algorithms. The derivation assumes that measurement noise is band-correlated additive noise. The bias errors propagated in FVC depended on the endmember spectra assumed in the algorithm, the target spectrum, and the coefficients of the spectral vegetation index, which were employed as constraints, as well as magnitude of the input error. It was found that the relationships among the propagated errors assume asymmetric elliptical forms with coefficients that are determined by the input variables. These results suggest that the relationships depend heavily on the choice of endmember spectra as well as the spectrum of the target pixel and the vegetation index employed as a constraint. The present findings should assist in the selection of an optimum algorithm based on prior knowledge of the target field. Full article

► Show Figures

Figure 1

626 KiB

Open AccessArticle

Classification of Defoliated Trees Using Tree-Level Airborne Laser Scanning Data Combined with Aerial Images

by Tuula Kantola, Mikko Vastaranta, Xiaowei Yu, Paivi Lyytikainen-Saarenmaa, Markus Holopainen, Mervi Talvitie, Sanna Kaasalainen, Svein Solberg and Juha Hyyppa

Remote Sens. 2010, 2(12), 2665-2679; https://doi.org/10.3390/rs2122665 - 26 Nov 2010

Cited by 53 | Viewed by 9507

Abstract

Climate change and rising temperatures have been observed to be related to the increase of forest insect damage in the boreal zone. The common pine sawfly (Diprion pini L.) (Hymenoptera, Diprionidae) is regarded as a significant threat to boreal pine forests. Defoliation [...] Read more.

Climate change and rising temperatures have been observed to be related to the increase of forest insect damage in the boreal zone. The common pine sawfly (Diprion pini L.) (Hymenoptera, Diprionidae) is regarded as a significant threat to boreal pine forests. Defoliation by D. pini can cause severe growth loss and tree mortality in Scots pine (Pinus sylvestris L.) (Pinaceae). In this study, logistic LASSO regression, Random Forest (RF) and Most Similar Neighbor method (MSN) were investigated for predicting the defoliation level of individual Scots pines using the features derived from airborne laser scanning (ALS) data and aerial images. Classification accuracies from 83.7% (kappa 0.67) to 88.1% (kappa 0.76) were obtained depending on the method. The most accurate result was produced using RF with a combination of data from the two sensors, while the accuracies when using ALS and image features separately were 80.7% and 87.4%, respectively. Evidently, the combination of ALS and aerial images in detecting needle losses is capable of providing satisfactory estimates for individual trees. Full article

► Show Figures

Figure 1

472 KiB

Open AccessArticle

Broad-Scale Environmental Conditions Responsible for Post-Fire Vegetation Dynamics

by Grant M. Casady and Stuart E. Marsh

Remote Sens. 2010, 2(12), 2643-2664; https://doi.org/10.3390/rs2122643 - 25 Nov 2010

Cited by 22 | Viewed by 7197

Abstract

Ecosystem response to disturbance is influenced by environmental conditions at a number of scales. Changes in climate have altered fire regimes across the western United States, and have also likely altered spatio-temporal patterns of post-fire vegetation regeneration. Fire occurrence data and a vegetation [...] Read more.

Ecosystem response to disturbance is influenced by environmental conditions at a number of scales. Changes in climate have altered fire regimes across the western United States, and have also likely altered spatio-temporal patterns of post-fire vegetation regeneration. Fire occurrence data and a vegetation index (NDVI) derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR) were used to monitor post-fire vegetation from 1989 to 2007. We first investigated differences in post-fire rates of vegetation regeneration between ecoregions. We then related precipitation, temperature, and elevation records at four temporal scales to rates of post-fire vegetation regeneration to ascertain the influence of climate on post-fire vegetation dynamics. We found that broad-scale climate factors are an important influence on post-fire vegetation regeneration. Most notably, higher rates of post-fire regeneration occurred with warmer minimum temperatures. Increases in precipitation also resulted in higher rates of post-fire vegetation growth. While explanatory power was slight, multiple statistical approaches provided evidence for real ecological drivers of post-fire regeneration that should be investigated further at finer scales. The sensitivity of post-disturbance vegetation dynamics to climatic drivers has important ramifications for the management of ecosystems under changing climatic conditions. Shifts in temperature and precipitation regimes are likely to result in changes in post-disturbance dynamics, which could represent important feedbacks into the global climate system. Full article

► Show Figures

Figure 1

Journal Menu

Journal Browser

Remote Sens., Volume 2, Issue 12 (December 2010) – 9 articles , Pages 2643-2802

Further Information

Guidelines

MDPI Initiatives

Follow MDPI