Accuracy assessment and validation of remotely sensed and other spatial information

2001 ◽  
Vol 10 (4) ◽  
pp. 321 ◽  
Author(s):  
Russell G. Congalton
Keyword(s):  
Data Quality ◽  
Fire Management ◽  
Spatial Information ◽  
Accuracy Assessment ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Error Matrix ◽  
Remotely Sensed Imagery ◽  
Ecological Principles ◽  
In Fire

This paper was presented at the conference ‘Integrating spatial technologies and ecological principles for a new age in fire management’, Boise, Idaho, USA, June 1999 Today, validation or accuracy assessment is an integral component of most mapping projects incorporating remotely sensed data. Other spatial information may not be so stringently evaluated, but at least requires meta-data that documents how the information was generated. This emphasis on data quality was not always the case. In the 1970s only a few brave scientists and researchers dared ask the question, ‘How good is this map derived from Landsat MSS imagery?’ In the 1980s, the use of the error matrix became a common tool for representing the accuracy of individual map categories. By the 1990s, most maps derived from remotely sensed imagery were required to meet some minimum accuracy standard. A similar progression can be outlined for other spatial information. However, this progression is about 5 years behind the validation of remotely sensed data. This paper presents a series of steps moving towards better assessment and validation of spatial information and asks the reader to evaluate where they are in this series and to move forward.

scholarly journals Distribution Statistics Preserving Post-Processing Method With Plot Level Uncertainty Analysis for Remotely Sensed Data-Based Forest Inventory Predictions

2018 ◽  
Vol 10 (11) ◽  
pp. 1677
Author(s):  
Virpi Junttila ◽  
Tuomo Kauranne
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Linear Models ◽  
Accuracy Assessment ◽  
Remotely Sensed ◽  
Processing Method ◽  
Post Processing ◽  
Remotely Sensed Data ◽  
Training Set ◽  
Study Sites

Remotely sensed data-based models used in operational forest inventory usually give precise and accurate predictions on average, but they often suffer from systematic under- or over-estimation of extreme attribute values resulting in too narrow or skewed attribute distributions. We use a post-processing method based on the statistics of a proper, representative training set to correct the predictions and their probability intervals, attaining corrected predictions that reproduce the statistics of the whole population. Performance of the method is validated with three forest attributes from seven study sites in Finland with training set sizes from 50 to over 400 field plots. The results are compared to those of the uncorrected predictions given by linear models using airborne laser scanning data. The post-processing method improves the accuracy assessment linear fit between the predictions and the reference set by 35.4–51.8% and the distribution fit by 44.5–95.0%. The prediction root mean square error declines on the average by 6.3%. The systematic under- and over-estimation are reduced consistently with all training set sizes. The level of uncertainty is maintained well as the probability intervals cover the real uncertainty while keeping the average probability interval width similar to the one in uncorrected predictions.

Changing fuel management strategies - The challenge of meeting new information and analysis needs

2001 ◽  
Vol 10 (4) ◽  
pp. 267 ◽  
Author(s):  
Susan G. Conard ◽  
Timothy Hartzell ◽  
Michael W. Hilbruner ◽  
G. Thomas Zimmerman
Keyword(s):  
Fire Management ◽  
Wildland Fire ◽  
Fire Suppression ◽  
Fire Hazard ◽  
Management Strategies ◽  
Scientific Basis ◽  
Fuel Management ◽  
Burned Areas ◽  
Ecological Principles ◽  
In Fire

This paper was presented at the conference ‘Integrating spatial technologies and ecological principles for a new age in fire management’, Boise, Idaho, USA, June 1999 ‘The earth, born in fire, baptized by lightning since before life"s beginning, has been and is a fire planet.’ E.V. Komarek Attitudes and policies concerning wildland fire, fire use, and fire management have changed greatly since early European settlers arrived in North America. Active suppression of wildfires accelerated early in the 20th Century, and areas burned dropped dramatically. In recent years, burned areas and cost of fires have begun to increase, in part due to fuel buildups resulting from fire suppression. The importance of fire as an ecosystem process is also being increasingly recognized. These factors are leading to changes in Federal agency fire and fuels management policies, including increased emphasis on use of prescribed fire and other treatments to reduce fuel loads and fire hazard. Changing fire management strategies have highlighted the need for better information and improved risk analysis techniques for setting regional and national priorities, and for monitoring and evaluating the ecological, economic, and social effects and tradeoffs of fuel management treatments and wildfires. The US Department of Interior and USDA Forest Service began the Joint Fire Science Program in 1998 to provide a sound scientific basis for implementing and evaluating fuel management activities. Development of remote sensing and GIS tools will play a key role in enabling land managers to evaluate hazards, monitor changes, and reduce risks to the environment and the public from wildland fires.

A primer on mapping vegetation using remote sensing

2001 ◽  
Vol 10 (4) ◽  
pp. 277 ◽  
Author(s):  
Tom Bobbe ◽  
Henry Lachowski ◽  
Paul Maus ◽  
Jerry Greer ◽  
Chuck Dull
Keyword(s):  
Remote Sensing ◽  
Remotely Sensed ◽  
Vegetation Mapping ◽  
Project Planning ◽  
Gis Mapping ◽  
Remotely Sensed Data ◽  
Geospatial Information ◽  
Ecological Principles ◽  
Vegetation Maps ◽  
Key Steps

This paper was presented at the conference ‘Integrating spatial technologies and ecological principles for a new age in fire management’, Boise, Idaho, USA, June 1999 The use of information based upon remotely sensed data is a central factor in our 21st Century society. Scientists in land management agencies especially require accurate and current geospatial information to effectively implement ecosystem management. The increasing need to collect data across diverse landscapes, scales, and ownerships has resulted in a wider application of remote sensing, Geographic Information Systems (GIS) and associated geospatial technologies for natural resource applications. This paper summarizes the use of digital remotely sensed data for vegetation mapping. Key steps in preparing vegetation maps are described. These steps include defining project requirements and classification schemes, use of reference data, classification procedures, and assessing accuracy. The role of field personnel and inventory data is described. Case studies and applications of vegetation mapping on national forest land are also included. remote sensing, GIS, mapping, geospatial, project planning.

scholarly journals Unsupervised classification of saturated areas using a time series of remotely sensed images

2007 ◽  
Vol 4 (3) ◽  
pp. 1663-1696 ◽  
Author(s):  
D. A. DeAlwis ◽  
Z. M. Easton ◽  
H. E. Dahlke ◽  
W. D. Philpot ◽  
T. S. Steenhuis
Keyword(s):  
Water Content ◽  
Management Practices ◽  
Spatial Information ◽  
New York State ◽  
Soil Surface ◽  
Resource Planning ◽  
Remotely Sensed ◽  
Leaf Water Content ◽  
Remotely Sensed Data ◽  
Remotely Sensed Images

Abstract. The spatial distribution of saturated areas is an important consideration in numerous applications, such as water resource planning or sighting of management practices. However, in humid well vegetated climates where runoff is produced by saturation excess processes on hydrologically active areas (HAA) the delineation of these areas can be difficult and time consuming. Much of the non-point source pollution in these watersheds originates from these HAAs. Thus, a technique that can simply and reliably predict these areas would be a powerful tool for scientists and watershed managers tasked with implementing practices to improve water quality. Remotely sensed data is a source of spatial information and could be used to identify HAAs, should a proper technique be developed. The objective of this study is to develop a methodology to determine the spatial variability of saturated areas using a temporal sequence of remotely sensed images. The Normalized Difference Water Index (NDWI) was derived from medium resolution LANDSAT 7 ETM+ imagery collected over seven months in the Town Brook watershed in the Catskill Mountains of New York State and used to characterize the areas that were susceptible to saturation. We found that within a single landcover type, saturated areas were characterized by the soil surface water content when the vegetation was dormant and leaf water content of vegetation during the growing season. The resulting HAA map agreed well with both observed and spatially distributed computer simulated saturated areas. This methodology appears promising for delineating saturated areas in the landscape.

Multitemporal remote sensing of landscape dynamics and pattern change: describing natural and anthropogenic trends

2008 ◽  
Vol 32 (5) ◽  
pp. 503-528 ◽  
Author(s):  
Steve N. Gillanders ◽  
Nicholas C. Coops ◽  
Michael A. Wulder ◽  
Sarah E. Gergel ◽  
Trisalyn Nelson
Keyword(s):  
Land Cover ◽  
Change Detection ◽  
Landscape Pattern ◽  
Pattern Analysis ◽  
Spatial Information ◽  
Remotely Sensed ◽  
Spatial Pattern Analysis ◽  
Remotely Sensed Data ◽  
Landscape Pattern Analysis ◽  
Landscape Pattern Indices

Science and reporting information needs for monitoring dynamics in land cover over time have prompted research, and made operational, a wide variety of change detection methods utilizing multiple dates of remotely sensed data. Change detection procedures based upon spectral values are common; however, landscape pattern analysis approaches which utilize spatial information inherent within imagery present opportunities for the generation of unique and ecologically important information. While the use of two images may provide the means to identify change, the use of more than two images for long-term monitoring affords the ability to identify a greater range of processes of landscape change, including rates and dynamics. The main objective of this review is to investigate and summarize the methods and applications of land cover spatial pattern analysis using three or more image dates. The potential and the limitations of landscape pattern indices are identified and discussed to inform application recommendations. The second objective of this review is to make recommendations, including appropriate landscape pattern indices, for the application of landscape pattern analysis of a long time series of remotely sensed data to a case study involving the mountain pine beetle in British Columbia, Canada. The review concludes with recommendations for future research.

scholarly journals Fusing High-Spatial-Resolution Remotely Sensed Imagery and OpenStreetMap Data for Land Cover Classification Over Urban Areas

2019 ◽  
Vol 11 (1) ◽  
pp. 88 ◽  
Author(s):  
Nianxue Luo ◽  
Taili Wan ◽  
Huaixu Hao ◽  
Qikai Lu
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Urban Areas ◽  
Spatial Information ◽  
Land Cover Classification ◽  
Remotely Sensed ◽  
Geographical Information ◽  
Remotely Sensed Imagery ◽  
Training Samples ◽  
Comparison Results

Land cover classification of urban areas is critical for understanding the urban environment. High-resolution remotely sensed imagery provides abundant, detailed spatial information for urban classification. In the meantime, OpenStreetMap (OSM) data, as typical crowd-sourced geographical information, have been an emerging data source for obtaining urban information. In this context, a land cover classification method that fuses high-resolution remotely sensed imagery and OSM data is proposed. Training samples were generated by integrating the OSM data and multiple information indexes. OSM data, which contain class attributes and location information of urban objects, served as the labels of initial training samples. Multiple information indexes that reflect spectral and spatial characteristics of different classes were utilized to improve the training set. Morphological attribute profiles were used because the structural and contextual information of images was effective in distinguishing the classes with similar spectral characteristics. Moreover, a road superimposition strategy that considers road hierarchy was developed because OSM data provide road information with high completeness in the urban area. Experiments were conducted on the data captured over Wuhan city, and three state-of-the-art approaches were adopted for comparison. Results show that the proposed approach obtains satisfactory results and outperforms the other comparative approaches.

Integrating ecological principles in fire management: Impact of fire fighting chemicals on soil-plant systems from temperate humid zone

2006 ◽  
Vol 234 ◽  
pp. S265
Author(s):  
A. Couto-Vázquez ◽  
J. Mahía ◽  
M. Díaz-Raviña ◽  
T. Carballas ◽  
S.J. González-Prieto
Keyword(s):  
Fire Management ◽  
Fire Fighting ◽  
Humid Zone ◽  
Ecological Principles ◽  
In Fire
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