scholarly journals Application of the Reed-Solomon Algorithm as a Remote Sensing Data Fusion Tool for Land Use Studies

Algorithms ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 188
Author(s):  
Piotr A. Werner
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Data Fusion ◽  
Remote Sensing Data ◽  
Short Review ◽  
Data Sets ◽  
Map Algebra ◽  
Spectral Bands ◽  
Preparatory Stage ◽  
Use Studies

The Reed-Solomon algorithm is well known in different fields of computer science. The novelty of this study lies in the different interpretation of the algorithm itself and its scope of application for remote sensing, especially at the preparatory stage, i.e., data fusion. A short review of the attempts to use different data fusion approaches in geospatial technologies explains the possible usage of the algorithm. The rationale behind its application for data fusion is to include all possible information from all acquired spectral bands, assuming that complete composite information in the form of one compound image will improve both the quality of visualization and some aspects of further quantitative and qualitative analyses. The concept arose from an empirical, heuristic combination of geographic information systems (GIS), map algebra, and two-dimensional cellular automata. The challenges are related to handling big quantitative data sets and the awareness that these numbers are in fact descriptors of a real-world multidimensional view. An empirical case study makes it easier to understand the operationalization of the Reed-Solomon algorithm for land use studies.

scholarly journals A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring

2016 ◽  
Vol 8 (1) ◽  
pp. 70 ◽  
Author(s):  
Neha Joshi ◽  
Matthias Baumann ◽  
Andrea Ehammer ◽  
Rasmus Fensholt ◽  
Kenneth Grogan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Data Fusion ◽  
Remote Sensing Data ◽  
Radar Remote Sensing ◽  
Sensing Data

scholarly journals Land use/cover classification in the Brazilian Amazon using satellite images

2012 ◽  
Vol 47 (9) ◽  
pp. 1185-1208 ◽  
Author(s):  
Dengsheng Lu ◽  
Mateus Batistella ◽  
Guiying Li ◽  
Emilio Moran ◽  
Scott Hetrick ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Data Fusion ◽  
Spatial Information ◽  
Classification Tree ◽  
Remote Sensing Data ◽  
Brazilian Amazon ◽  
Classification Performance ◽  
Sensor Data ◽  
Sensing Data

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation‑based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi‑resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Among the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, have the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical‑based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

scholarly journals CHARACTERISTICS OF THE REMOTE SENSING DATA USED IN THE PROPOSED UNFCCC REDD+ FOREST REFERENCE EMISSION LEVELS (FRELS)

2016 ◽  
Vol XLI-B8 ◽  
pp. 669-672
Author(s):  
B. A. Johnson ◽  
H. Scheyvens ◽  
H. Samejima ◽  
M. Onoda
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Remote Sensing Data ◽  
Forest Degradation ◽  
Median Number ◽  
Forest Carbon ◽  
Data Sets ◽  
Median Percentage ◽  
Forestry Sector

Developing countries must submit forest reference emission levels (FRELs) to the UNFCCC to receive incentives for REDD+ activities (e.g. reducing emissions from deforestation/forest degradation, sustainable management of forests, forest carbon stock conservation/enhancement). These FRELs are generated based on historical CO2 emissions in the land use, land use change, and forestry sector, and are derived using remote sensing (RS) data and in-situ forest carbon measurements. Since the quality of the historical emissions estimates is affected by the quality and quantity of the RS data used, in this study we calculated five metrics (i-v below) to assess the quality and quantity of the data that has been used thus far. Countries could focus on improving on one or more of these metrics for the submission of future FRELs. Some of our main findings were: (i) the median percentage of each country mapped was 100%, (ii) the median historical timeframe for which RS data was used was 11.5 years, (iii) the median interval of forest map updates was 4.5 years, (iv) the median spatial resolution of the RS data was 30m, and (v) the median number of REDD+ activities that RS data was used for operational monitoring of was 1 (typically deforestation). Many new sources of RS data have become available in recent years, so complementary or alternative RS data sets for generating future FRELs can potentially be identified based on our findings; e.g. alternative RS data sets could be considered if they have similar or higher quality/quantity than the currently-used data sets.

scholarly journals CHARACTERISTICS OF THE REMOTE SENSING DATA USED IN THE PROPOSED UNFCCC REDD+ FOREST REFERENCE EMISSION LEVELS (FRELS)

2016 ◽  
Vol XLI-B8 ◽  
pp. 669-672 ◽  
Author(s):  
B. A. Johnson ◽  
H. Scheyvens ◽  
H. Samejima ◽  
M. Onoda
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Remote Sensing Data ◽  
Forest Degradation ◽  
Median Number ◽  
Forest Carbon ◽  
Data Sets ◽  
Median Percentage ◽  
Forestry Sector

Developing countries must submit forest reference emission levels (FRELs) to the UNFCCC to receive incentives for REDD+ activities (e.g. reducing emissions from deforestation/forest degradation, sustainable management of forests, forest carbon stock conservation/enhancement). These FRELs are generated based on historical CO2 emissions in the land use, land use change, and forestry sector, and are derived using remote sensing (RS) data and in-situ forest carbon measurements. Since the quality of the historical emissions estimates is affected by the quality and quantity of the RS data used, in this study we calculated five metrics (i-v below) to assess the quality and quantity of the data that has been used thus far. Countries could focus on improving on one or more of these metrics for the submission of future FRELs. Some of our main findings were: (i) the median percentage of each country mapped was 100%, (ii) the median historical timeframe for which RS data was used was 11.5 years, (iii) the median interval of forest map updates was 4.5 years, (iv) the median spatial resolution of the RS data was 30m, and (v) the median number of REDD+ activities that RS data was used for operational monitoring of was 1 (typically deforestation). Many new sources of RS data have become available in recent years, so complementary or alternative RS data sets for generating future FRELs can potentially be identified based on our findings; e.g. alternative RS data sets could be considered if they have similar or higher quality/quantity than the currently-used data sets.

Monitoring land use change in the Lake Taupo catchment between 1975, 1990 and 2002 using satellite remote sensing data

2007 ◽  
pp. 17-22
Author(s):  
H. Lilienthal ◽  
A. Brauer ◽  
K. Betteridge ◽  
E. Schnug
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Land Use ◽  
Land Use Change ◽  
Satellite Remote Sensing ◽  
Remote Sensing Data ◽  
Native Vegetation ◽  
Lake Water Quality ◽  
Livestock Farming ◽  
Slow Decline

Conversion of native vegetation into farmed grassland in the Lake Taupo catchment commenced in the late 1950s. The lake's iconic value is being threatened by the slow decline in lake water quality that has become apparent since the 1970s. Keywords: satellite remote sensing, nitrate leaching, land use change, livestock farming, land management

scholarly journals The predictability of reported drought events and impacts in the Ebro Basin using six different remote sensing data sets

2017 ◽  
Vol 21 (9) ◽  
pp. 4747-4765 ◽  
Author(s):  
Clara Linés ◽  
Micha Werner ◽  
Wim Bastiaanssen
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Data Sets ◽  
Drought Management ◽  
Ebro Basin ◽  
Sensing Data ◽  
Drought Impacts ◽  
Management Plans ◽  
Drought Indicators ◽  
Selection Of

Abstract. The implementation of drought management plans contributes to reduce the wide range of adverse impacts caused by water shortage. A crucial element of the development of drought management plans is the selection of appropriate indicators and their associated thresholds to detect drought events and monitor the evolution. Drought indicators should be able to detect emerging drought processes that will lead to impacts with sufficient anticipation to allow measures to be undertaken effectively. However, in the selection of appropriate drought indicators, the connection to the final impacts is often disregarded. This paper explores the utility of remotely sensed data sets to detect early stages of drought at the river basin scale and determine how much time can be gained to inform operational land and water management practices. Six different remote sensing data sets with different spectral origins and measurement frequencies are considered, complemented by a group of classical in situ hydrologic indicators. Their predictive power to detect past drought events is tested in the Ebro Basin. Qualitative (binary information based on media records) and quantitative (crop yields) data of drought events and impacts spanning a period of 12 years are used as a benchmark in the analysis. Results show that early signs of drought impacts can be detected up to 6 months before impacts are reported in newspapers, with the best correlation–anticipation relationships for the standard precipitation index (SPI), the normalised difference vegetation index (NDVI) and evapotranspiration (ET). Soil moisture (SM) and land surface temperature (LST) offer also good anticipation but with weaker correlations, while gross primary production (GPP) presents moderate positive correlations only for some of the rain-fed areas. Although classical hydrological information from water levels and water flows provided better anticipation than remote sensing indicators in most of the areas, correlations were found to be weaker. The indicators show a consistent behaviour with respect to the different levels of crop yield in rain-fed areas among the analysed years, with SPI, NDVI and ET providing again the stronger correlations. Overall, the results confirm remote sensing products' ability to anticipate reported drought impacts and therefore appear as a useful source of information to support drought management decisions.

LAI Retrieval Based on PROSPECT-SAILH Model from Multi-Angular Data of WiDAS Imaging System

2012 ◽  
Vol 518-523 ◽  
pp. 5697-5703
Author(s):  
Zhao Yan Liu ◽  
Ling Ling Ma ◽  
Ling Li Tang ◽  
Yong Gang Qian
Keyword(s):  
Remote Sensing ◽  
Imaging System ◽  
Remote Sensing Data ◽  
Canopy Reflectance ◽  
Area Index ◽  
Sensing Data ◽  
Spectral Bands ◽  
Sensing Technology ◽  
Leaf Optical Properties ◽  
Angular Data

The aim of this study is to assess the capability of estimating Leaf Area Index (LAI) from high spatial resolution multi-angular Vis-NIR remote sensing data of WiDAS (Wide-Angle Infrared Dual-mode Line/Area Array Scanner) imaging system by inverting the coupled radiative transfer models PROSPECT-SAILH. Based on simulations from SAILH canopy reflectance model and PROSPECT leaf optical properties model, a Look-up Table (LUT) which describes the relationship between multi-angular canopy reflectance and LAI has been produced. Then the LAI can be retrieved from LUT by directly matching canopy reflectance of six view directions and four spectral bands with LAI. The inversion results are validated by field data, and by comparing the retrieval results of single-angular remote sensing data with multi-angular remote sensing data, we can found that the view angle takes the obvious impact on the LAI retrieval of single-angular data and that high accurate LAI can be obtained from the high resolution multi-angular remote sensing technology.

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