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The prediction performances of geostatistical approach for mapping quantitative variables of Derivation of land cover change data and their assimilation in ecosystem models

Research project T4/DD/31 (Research action T4)

Persons :

  • Prof. dr.  LAMBIN Eric - Université Catholique de Louvain (UCL)
    Financed belgian partner
    Duration: 15/12/1998-15/12/2000

Description :

Which methodological developments are required to allow for a better integration of remote sensing data in ecosystem models in order to better address key issues on land-cover changes and their impacts?
The general objective of the research is to develop new methodologies, and to advance and refine existing methodologies to allow for: (i) a more realistic description of long-term processes of land-cover changes, based on a variety of data sources, and (ii) a better integration of remote sensing data into ecosystem models in order to better address key issues on land-cover changes and their impacts.

The proposal is divided into four modules:

Module 1. Characterisation from space of subtle changes in land-cover:
A new approach based on the integration of remotely-sensed and ground-measured information will be developed to characterise subtle changes in land-cover. The methodology will be process-predicated in the sense that changes within land-cover types, or mosaics thereof, will be quantified in a continuum using models that correlate numeric indicators of these processes with spectral change response indices. Change quantification will be calibrated for natural variability via controlled experiments and will be specific, not at the more traditional global or regional scale, but at the local natural community level.

Module 2: Detection of land-cover change trajectories with long temporal series of data from a variety of sources
The objectives of this module are:
(i) to design data aggregation techniques to join in a single, homogeneous time series data coming from a variety of sources (e.g. historical maps, aerial photographs, satellite remote sensing data from different sensors), by addressing the issues of level of spatial generalisation of the land-cover information, and
(ii) to design change detection techniques to deal with such long temporal series of land-cover data (e.g. 50 years or more) which display complex trajectories of change (i.e. non-linear, reversible fluctuations as opposed to secular, monotonic trends).

Module 3: Validation and calibration of remote sensing-based land-cover change data with field measurements
The objective of this module is to design an approach to validate land-cover change data, using field measurements. An appropriate data aggregation method and integrated sampling scheme will be developed to relate point-level biophysical attribute data to area-level remotely sensed data. A Monte Carlo simulation of the process elements will be used to model the impact of the sampling scheme and data aggregation method on the relative accuracy of the estimated attribute value. For this, different sampling schemes (design and intensity) and aggregation methods will be tested on the simulated data sets. In case the simulated attribute values are based on indirect measurements, new techniques will also be developed to determine a method-specific calibration factor.

Module 4: Assimilation of remote sensing-based land-cover change data in ecosystem models
This module will test the sensitivity of two types of ecosystem models to the type, quality and format of land-cover change data used as input. The models are:
(i) a Soil-Vegetation-Atmosphere Transfer model (SVAT) coupled with a meso-scale climate model, and
(ii) a model for regional scale emissions from biomass burning. The sensitivity analyses will be performed by varying the following attributes of the input land-cover change data: the continuous versus categorical representation of land-cover change data; the representation of the fine-scale spatial variability of the land-cover change data; the level of spatial aggregation of the land-cover change data.

The remote sensing methods that will be developed in this research will advance the understanding of spatially heterogeneous biophysical processes at a scale consistent with the size of homogeneous landscape elements. This research proposal is closely integrated in the research agenda of the IGBP programme, and in particular of its Core Projects LUCC, GCTE and BACH. It is also related to the forthcoming ESA "Earth Explorer" Mission called "Land-Surface Processes and Interactions Mission" (LSPIM) which addresses the issue of assimilation of remote sensing-based data into ecosystem models to better understand crucial biophysical processes.
vegetation will be compared with those of classical calibration techniques that ignore the space-time dependence of observations.

Documentation :

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