scholarly journals Combining remote sensing and GIS climate modelling to estimate daily forest evapotranspiration in a Mediterranean mountain area

2011 ◽  
Vol 8 (1) ◽  
pp. 1125-1159
Author(s):  
J. Cristóbal ◽  
R. Poyatos ◽  
M. Ninyerola ◽  
P. Llorens ◽  
X. Pons
Keyword(s):  
Remote Sensing ◽  
Estimation Error ◽  
Remote Sensing Data ◽  
Geographical Information ◽  
Climate Modelling ◽  
Mountain Area ◽  
Flow Measurements ◽  
Sensing Data ◽  
Landsat 7 ◽  
Landsat 5 Tm

Abstract. Evapotranspiration monitoring allows us to assess the environmental stress on forest and agricultural ecosystems. Nowadays, Remote Sensing and Geographical Information Systems (GIS) are the main techniques used for calculating evapotranspiration at catchment and regional scales. In this study we present a methodology, based on the energy balance equation (B-method), that combines remote sensing imagery with GIS climate modelling to estimate daily evapotranspiration (ETd) for several dates between 2003 and 2005. The three main variables needed to compute ETd were obtained as follows: (i) Land surface temperature by means of the Landsat-5 TM and Landsat-7 ETM+ thermal band, (ii) air temperature by means of multiple regression analysis and spatial interpolation from meteorological ground stations data at satellite pass, and (iii) net radiation by means of the radiative balance. We calculated ETd using remote sensing data at different spatial and temporal scales (TERRA/AQUA MODIS and Landsat-5 TM/Landsat-7 ETM+) and combining three different approaches to calculate the B parameter. We then compared these estimates with sap flow measurements from a Scots pine (Pinus sylvestris L.) stand in a Mediterranean mountain area. This procedure allowed us to better understand the limitations of ETd modelling and how it needs to be improved, especially in heterogeneous forest areas. The method using Landsat data resulted in a good agreement, with a mean RMSE value of about 0.6 mm day−1 and an estimation error of ±30%. The poor agreement obtained using MODIS data reveals that ETd retrieval from coarse resolution remote sensing data is troublesome in these heterogeneous areas, and therefore further research is necessary on this issue.

scholarly journals Combining remote sensing and GIS climate modelling to estimate daily forest evapotranspiration in a Mediterranean mountain area

2011 ◽  
Vol 15 (5) ◽  
pp. 1563-1575 ◽  
Author(s):  
J. Cristóbal ◽  
R. Poyatos ◽  
M. Ninyerola ◽  
P. Llorens ◽  
X. Pons
Keyword(s):  
Remote Sensing ◽  
Estimation Error ◽  
Remote Sensing Data ◽  
Geographical Information ◽  
Climate Modelling ◽  
Mountain Area ◽  
Flow Measurements ◽  
Sensing Data ◽  
Landsat 7 ◽  
Landsat 5 Tm

Abstract. Evapotranspiration monitoring allows us to assess the environmental stress on forest and agricultural ecosystems. Nowadays, Remote Sensing and Geographical Information Systems (GIS) are the main techniques used for calculating evapotranspiration at catchment and regional scales. In this study we present a methodology, based on the energy balance equation (B-method), that combines remote sensing imagery with GIS-based climate modelling to estimate daily evapotranspiration (ETd) for several dates between 2003 and 2005. The three main variables needed to compute ETd were obtained as follows: (i) Land surface temperature by means of the Landsat-5 TM and Landsat-7 ETM+ thermal band, (ii) air temperature by means of multiple regression analysis and spatial interpolation from meteorological ground stations data at satellite pass, and (iii) net radiation by means of the radiative balance. We calculated ETd using remote sensing data at different spatial and temporal scales (Landsat-7 ETM+, Landsat-5 TM and TERRA/AQUA MODIS, with a spatial resolution of 60, 120 and 1000 m, respectively) and combining three different approaches to calculate the B parameter, which represents an average bulk conductance for the daily-integrated sensible heat flux. We then compared these estimates with sap flow measurements from a Scots pine (Pinus sylvestris L.) stand in a Mediterranean mountain area. This procedure allowed us to better understand the limitations of ETd modelling and how it needs to be improved, especially in heterogeneous forest areas. The method using Landsat data resulted in a good agreement, R2 test of 0.89, with a mean RMSE value of about 0.6 mm day−1 and an estimation error of ±30 %. The poor agreement obtained using TERRA/AQUA MODIS, with a mean RMSE value of 1.8 and 2.4 mm day−1 and an estimation error of about ±57 and 50 %, respectively. This reveals that ETd retrieval from coarse resolution remote sensing data is troublesome in these heterogeneous areas, and therefore further research is necessary on this issue. Finally, implementing regional GIS-based climate models as inputs in ETd retrieval have has provided good results, making possible to compute ETd at regional scales.

A flowline map of glaciated Canada based on remote sensing dataThis paper is accompanied by a large foldout map entitled A flowline map of glaciated Canada based on remote sensing data (see pocket on back cover).

2010 ◽  
Vol 47 (1) ◽  
pp. 89-101 ◽  
Author(s):  
John Shaw ◽  
Davis Sharpe ◽  
Jeff Harris
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Mineral Exploration ◽  
Ice Sheets ◽  
Remote Sensing Data ◽  
Shuttle Radar Topography Mission ◽  
Geographical Information ◽  
Sensing Data ◽  
Landsat 7 ◽  
Large Scale Flow

The map A flowline map of glaciated Canada based on remote sensing data presents flowlines for the former Laurentide and Cordilleran ice sheets based on flow indicators derived from aggregated, flow-parallel landforms — drumlins and crag and tails, fluting, sinuous ridges and furrows, and rises. An extensive review introduces the concepts and evolution of flowline mapping at continental-ice-sheet and regional scales, emphasizing the use of new remote sensing data. Coherent, glaciologically plausible sets of flowlines mapped as flow tracts reflect large-scale flow structure in the paleo-ice sheets and demarcate fields of flow-parallel bedforms. In addition to flow reconstruction, mapped distributions of fields of glacial terrain types — hummocky terrain, Rogen terrain, and bedrock-dominant terrain — increase our power to interpret flowlines and, in turn, give evidence on the genesis of these terrains. End moraines and eskers also aid map interpretation. Landsat 7 Enhanced Thematic Mapper+ (ETM+) satellite images and Shuttle Radar Topography Mission (SRTM) hill shades provide the basic information for this flowline mapping in a Geographical Information System (ArcMap). Information on the Flowline Map allows us to develop conceptual models of ice sheets and to appreciate regional constraints on applications in mineral exploration, in aggregate and groundwater discovery and assessment, in soil and landform genesis, and in glaciology, paleoclimatology, and paleoceanography.

Estimation of PM10 Distribution using Landsat 7 ETM+ Remote Sensing Data

2017 ◽  
Vol 6 (1) ◽  
pp. 2246-2252 ◽  
Author(s):  
Ajay Roy ◽  
◽  
Anjali Jivani ◽  
Bhuvan Parekh ◽  
◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Landsat 7

scholarly journals Risk management support through India Remote Sensing Satellites

2014 ◽  
Vol XL-8 ◽  
pp. 1-6
Author(s):  
N. Aparna ◽  
A. V. Ramani ◽  
R. Nagaraja
Keyword(s):  
Remote Sensing ◽  
Risk Management ◽  
Satellite Data ◽  
Global Climate ◽  
Remote Sensing Data ◽  
Temporal Variations ◽  
Geographical Information ◽  
Indian Remote Sensing ◽  
Management Support ◽  
Sensing Data

Remote Sensing along with Geographical Information System (GIS) has been proven as a very important tools for the monitoring of the Earth resources and the detection of its temporal variations. A variety of operational National applications in the fields of Crop yield estimation , flood monitoring, forest fire detection, landslide and land cover variations were shown in the last 25 years using the Remote Sensing data. The technology has proven very useful for risk management like by mapping of flood inundated areas identifying of escape routes and for identifying the locations of temporary housing or a-posteriori evaluation of damaged areas etc. The demand and need for Remote Sensing satellite data for such applications has increased tremendously. This can be attributed to the technology adaptation and also the happening of disasters due to the global climate changes or the urbanization. However, the real-time utilization of remote sensing data for emergency situations is still a difficult task because of the lack of a dedicated system (constellation) of satellites providing a day-to-day revisit of any area on the globe. The need of the day is to provide satellite data with the shortest delay. Tasking the satellite to product dissemination to the user is to be done in few hours. Indian Remote Sensing satellites with a range of resolutions from 1 km to 1 m has been supporting disasters both National & International. In this paper, an attempt has been made to describe the expected performance and limitations of the Indian Remote Sensing Satellites available for risk management applications, as well as an analysis of future systems Cartosat-2D, 2E ,Resourcesat-2R &RISAT-1A. This paper also attempts to describe the criteria of satellite selection for programming for the purpose of risk management with a special emphasis on planning RISAT-1(SAR sensor).

scholarly journals ZAB RIVER (IRAQ) SINUOSITY AND MEANDERING ANALYSIS BASED ON THE REMOTE SENSING DATA

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 91-95
Author(s):  
B. Kalantar ◽  
M. H. Ameen ◽  
H. J. Jumaah ◽  
S. J. Jumaah ◽  
A. A. Halin
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Geographic Information Systems ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Sinuosity Index ◽  
River Region ◽  
Landsat 7 ◽  
Landsat 5 Tm ◽  
River Migration

Abstract. This work studies the meandering and change of paths along the Zab River in Iraq. Landsat-5 TM, Landsat-7 ETM+ and Landsat-8 (2-sets) images were acquired from the years 1989, 1999, 2015 and 2019, respectively, which were used together with Remote sensing and Geographic Information Systems (GIS) techniques to study the changes. To determine the river/stream shape, the Sinuosity Index was calculated to classify Zab River segments into either the straight, sinuous or meandering class. Our findings via image analysis show coarse river migration and that most river segments fall into the two classes of sinuous and meander. In addition, it seems that the east bank of the Zab River region of the basin has extremely shifted where the river passes near the Kirkuk governorate.

scholarly journals Guaranteed Robust Tensor Completion via ∗L-SVD with Applications to Remote Sensing Data

2021 ◽  
Vol 13 (18) ◽  
pp. 3671
Author(s):  
Andong Wang ◽  
Guoxu Zhou ◽  
Qibin Zhao
Keyword(s):  
Remote Sensing ◽  
Estimation Error ◽  
Synthetic Data ◽  
Remote Sensing Data ◽  
Tensor Completion ◽  
Sensing Data ◽  
Incomplete Observations ◽  
Alternating Direction ◽  
Value Decomposition ◽  
Asymptotic Upper Bound

This paper conducts a rigorous analysis for the problem of robust tensor completion, which aims at recovering an unknown three-way tensor from incomplete observations corrupted by gross sparse outliers and small dense noises simultaneously due to various reasons such as sensor dead pixels, communication loss, electromagnetic interferences, cloud shadows, etc. To estimate the underlying tensor, a new penalized least squares estimator is first formulated by exploiting the low rankness of the signal tensor within the framework of tensor ∗L-Singular Value Decomposition (∗L-SVD) and leveraging the sparse structure of the outlier tensor. Then, an algorithm based on the Alternating Direction Method of Multipliers (ADMM) is designed to compute the estimator in an efficient way. Statistically, the non-asymptotic upper bound on the estimation error is established and further proved to be optimal (up to a log factor) in a minimax sense. Simulation studies on synthetic data demonstrate that the proposed error bound can predict the scaling behavior of the estimation error with problem parameters (i.e., tubal rank of the underlying tensor, sparsity of the outliers, and the number of uncorrupted observations). Both the effectiveness and efficiency of the proposed algorithm are evaluated through experiments for robust completion on seven different types of remote sensing data.

scholarly journals INTEGRATION OF REMOTE SENSING DATA AND BASIC GEODATA AT DIFFERENT SCALE LEVELS FOR IMPROVED LAND USE ANALYSES

2015 ◽  
Vol XL-3/W3 ◽  
pp. 85-89 ◽  
Author(s):  
G. Waldhoff ◽  
S. Eichfuss ◽  
G. Bareth
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Spatial Resolution ◽  
Geographical Information Systems ◽  
High Spatial Resolution ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Geographical Information ◽  
Sensing Data ◽  
Very High Spatial Resolution

The classification of remote sensing data is a standard method to retrieve up-to-date land use data at various scales. However, through the incorporation of additional data using geographical information systems (GIS) land use analyses can be enriched significantly. In this regard, the Multi-Data Approach (MDA) for the integration of remote sensing classifications and official basic geodata for a regional scale as well as the achievable results are summarised. On this methodological basis, we investigate the enhancement of land use analyses at a very high spatial resolution by combining WorldView-2 remote sensing data and official cadastral data for Germany (the Automated Real Estate Map, ALK). Our first results show that manifold thematic information and the improved geometric delineation of land use classes can be gained even at a high spatial resolution.

Sign in / Sign up

Export Citation Format

Share Document