Assessing the accuracy of the MODIS LAI 1-km product in southeastern United States loblolly pine plantations: Accounting for measurement variance from ground to satellite

Leaf area index (LAI), defined here as one-half of the total leaf area per unit ground surface area (Chen, 1996), has been estimated at a global scale from spectral data processed from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard two NASA EOS-AM spacecraft, Terra (launched in 1999) and Aqua (launched in 2002). The MOD15A2 LAI product is a 1 km global data product composited over an 8-day period and is derived from a three-dimensional radiative transfer model driven by an atmosphere corrected surface reflectance product (MOD09), a land cover product (MOD12) and ancillary information on surface characteristics. The United States Environmental Protection Agency (US EPA) initiated validation research (2002) in the evergreen needle leaf biome, as defined in the MOD12 classification, in a regional study located in the southeastern United States. The validation effort was prompted by the potential use of MODIS LAI inputs into atmospheric deposition and biogenic emission models developed within the US EPA Office of Research and Development. The MODIS LAI validation process involves the creation of a high spatial resolution LAI surface map, which when scaled to the MOD15A2 resolution (1 km) allowed for comparison and analysis with the 1 km MODIS LAI product. Creation of this LAI surface map involved: (1) the collection of in situ LAI measurements via indirect optical measurements, (2) the correlation of land cover specific LAI estimates with spectral values retrieved from high resolution imagery (20 m--30 m), and (3) the aggregation of these 30 m cells to 1 km spatial resolution, matching the resolution of the MODIS product and enabling a comparison of the two LAI values (Morisette et al. 2006). This research assessed the uncertainty associated with the creation of the high-resolution LAI reference map, specifically addressing uncertainty in the indirect in situ optical measurements of LAI and the uncertainty in the land cover classification process. Also addressed was the influence of vegetative understory on satellite-derived vegetation indices from the IKONOS sensor

The use of remote sensing to evaluate and detect desert regions

Die Fernerkundung spielt eine signifikante Rolle bei der Bereitstellung von aktuellen Daten zur Schätzung von empirischen Indizes bei Untersuchungen der Umwelt, insbesondere in Trockengebieten. Spektral- und thermische Kanäle in Satellitenbildern werden auch zur Berechnung von Indizes verwendet, um natürliche Phänomene in Trockengebieten – wie etwa Bodendegradation und Desertifikation – aufzuspüren, zu bestimmen und zu evaluieren. In dieser Arbeit wurden zur Identifikation von Desertifikation in der Kashan-Qom Region im Zentraliran fünf Desertifikationsindikatoren verwendet: Vegetation, Oberflächentemperatur, Erosion, Trockenheit und Überflutungen. Diese Indikatoren wurden dargestellt mit Hilfe von: Vegetationsindex (VCI), Temperaturindex (TCI), Revidierte Universelle Bodenverlustgleichung (RUSLE), standardisierter Niederschlagsindex (SPI) und Abfluss. Multispektrale Bilder des MODIS Satelliten wurden für die Berechnung von VCI und TCI herangezogen. Des Weiteren wurden RUSLE, SPI und Abfluss bestimmt. Schließlich wurden mehrere Desertifikationskarten anhand von zwei Modellen – einem konventionellen Modell und einem unscharfen Modell – erstellt. Die Ergebnisse der Modelle wurden mit Hilfe von Feldproben und der Erstellung einer Fehlermatrix analysiert. Im unscharfen Modell wurde ein regelbasiertes System aufgrund von Expertenwissen und einer induktiven datengetriebenen Methode erstellt. Obwohl das unscharfe Modell weniger genau als die konventionelle Methode ist, zeigt es die Unbestimmtheit in den Desertifikationsklassen der erstellten Karten.Remote sensing plays a significant role in providing up-to-date data for the estimating of empirical indices in studying the environment, especially in drylands. The spectral and thermal bands in satellite images are also applied to calculate the indices to detect, identify, and evaluate the natural phenomena in drylands such as land degradation and desertification. In this project, for the identification of desertification in the Kashan-Qom region in Central Iran, five main indicators of desertification are used as follows: vegetation, land surface temperature, erosion, drought, and flooding; therefore, these indices are selected as Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Revised Universal Soil Loss Equation (RUSLE), and Standardized Precipitation Index (SPI), and runoff (Q), respectively. The multi-spectral satellite images of MODIS are used for the calculation of remotely sensed indices such as Vegetation Condition Index (VCI) and Temperature Condition Index (TCI). Furthermore, the ancillary data-based indices, Revised Universal Soil Loss Equation (RUSLE), and Standardized Precipitation Index (SPI), and runoff (Q), are also estimated. Then several desertification maps are produced in two models: conventional method and fuzzy model. The result of each model is also evaluated, that is, the results are assessed by the supplying of field sampling as ground truth references and the defining of error matrix. In the fuzzy modelling, a rule-based system is built by expert knowledge and data-induction method. According to the obtained results, even though the accuracy of the fuzzy model is lower than the conventional method, the fuzzy model represents the uncertainty in the classes of resulted desertification by providing a map for each class

The Issue of Uncertainty Propagation in Spatial Decision Making

Abstract GISs give users facilities to integrate and analyze data from different sources with different scale, accuracy, resolution and quality of the original data which are the key aspects of GIS functionality, but it does raise the question as to what effects the combination of different levels of data uncertainty has on both the output maps and on the data derived from spatial query and analysis. In this paper, in addition to provide an overview of uncertainty propagation assessment in overlay analysis, an experiment using Monte Carlo simulation method has been performed and then the results were analyzed. Two polygons whose vertices have been perturbed by changing their coordinates randomly using Monte Carlo simulation method are overlaid so that their intersection defines the third polygons set which in turn were statistically analyzed using a developed program and some GPS data. Two mainly recommended indicators, i.e., area and perimeter of polygon, were used and ended up with consequence that the indices of these polygons whose vertices had error in position emerged less than those whose vertices were accurate.