Managing Tricky Decentralised Competencies: Case Study of a Participatory Modelling Experiment on Land Use in the Lake Guiers Area in Northern Senegal

Sustainability Science, Volume 4, Number 2, 2009, 243-261

Grégoire Leclerc, Alassane Bah, Bruno Barbier, Laurence Boutinot, Aurélie Botta, William’s Daré, Ibrahima Diop Gaye, Christine Fourage, Géraud Magrin and Mame Arame Soumare, et al.

“We describe an action-research project whose objective was to help stakeholders at different organisational levels achieve sustainable land management by developing mediation models and tools. We chose to test a specific approach called companion modelling in the framework of a multidisciplinary research partnership and a formal local partnership (a ‘users committee’) involving an array of stakeholders at different organisational levels. The study area covers 10,000 km2 of agro-pastoral land around Lake Guiers in northern Senegal. We conducted studies to update the knowledge base of the area and organised six field workshops that clearly revealed three important tool functions to support decision-making on land use at different scales, i.e. understanding maps, monitoring and evaluating land tenure, and foreseeing changes in land use. We found that a toolbox approach was the best way to implement the three functions and overcome the constraints faced by the research team and those linked to the timing of the project. Therefore, we produced five simple complementary tools aimed at various users: a farm-level optimisation model (for researchers and technical services), a database for land allocations and a discussion tool to assess the impact of land allocation decisions (for the rural council), a paper atlas (for local players) and a regional land use change simulation model (for regional and national planners). Participants were able to work with paper maps, to interpret computer-generated simulations of land use change and understand the strengths and limitations of each. Self-assessment of the research process emphasised the importance of the context and the critical role played by social capital at both the research and the field level, which, in turn, emphasised the need for major improvements in the design and implementation of a quality process for participatory modelling. It turns out that action-research may be an effective way to undertake sustainability science.”

Spatial Modeling and Variability Analysis for Modeling and Prediction of Soil and Crop Canopy Coverage Using Multispectral Imagery from an Airborne Remote Sensing System

Transactions of the American Society of Agricultural and Biological Engineers (ASABE), 53(4): 1321-1329. 2010

Y. Huang, Y. Lan, Y. Ge, W. C. Hoffmann, S. J. Thomson

“Spatial modeling and variability analysis of soil and crop canopy coverage has been accomplished using aerial multispectral images. Multispectral imagery was acquired using an MS-4100 multispectral camera at different flight altitudes over a 40 ha cotton field. After the acquired images were geo-registered and processed, spatial relationships between the aerial images and ground-based soil conductivity and NDVI (normalized difference vegetation index) measurements were estimated and compared using two spatial analysis approaches (model-driven spatial regression and data-driven geostatistics) and one non-spatial approach (multiple linear regression). Comparison of the three approaches indicated that OLS (ordinary least squares) solutions from multiple linear regression models performed worst in modeling ground-based soil conductivity and NDVI with high AIC (Akaike information criterion) (-668.3 to 2980) and BIC (Bayesian information criterion) (-642.4 to 3006) values. Spatial regression and geostatistics performed much better in modeling soil conductivity, with low AIC (2698 to 2820) and BIC (2732 to 2850) values. For modeling ground-based NDVI, the AIC and BIC values were -681.7 and -652.1, respectively, for spatial error regression and -679.8 and -646.5, respectively, for geostatistics, which were only moderate improvements over OLS (-668.3 and -642.4). Validation of the geostatistical models indicated that they could predict soil conductivity much better than the corresponding multiple linear regression models, with lower RMSE (root mean squared error) values (0.096 to 0.186, compared to 0.146 to 0.306). Results indicated that the aerial images could be used for spatial modeling and prediction, and they were informative for spatial prediction of ground soil and canopy coverage variability. The methods used for this study could help deliver baseline data for crop monitoring with remote sensing and establish a procedure for general crop management.”

Geostatistical Analysis on Human Impact Indexes for Land Use/Cover in Fujian Province and Fuzhou City

17th International Conference on Geoinformatics, 12-14 August 2009

Zhi-qiang Chen, Jian-fei Chen

“Land use/cover change (LUCC) is an important component of global change research. Based on Geostatistics and taking the TM images in 1985 and ASTER images in 2002 in Fujian Province, the TM images in 1988 and the ASTER images in 2004 in Fuzhou City as the data sources, the present paper built the human impact indexes, compiled the human impact indexes maps and the human impact indexes change maps, calculated the parameters of semivariograms. The results showed that the human impact indexes and change had directionalities, the trends of main land use/cover types, all types and change types were approximately NE-SW in Fujian Province and NWSE in Fuzhou City using Standard Deviational Ellipse, the trends were consistent with the topographies respectively. The structural variance/sill ratio of human impact indexes was 88.96% in Fujian Province, 81.05% in Fuzhou City, the nugget/sill ratio was 11.04% and 18.95% respectively suggesting the intrinsic factors which were mainly nature factors such as topography were the dominant composition in the land use/cover pattern. The structural variance/sill ratio of human impact indexes change was 42.19% in Fujian Province, 42.17% in Fuzhou City, the nugget/sill ratio was 57.81% and 57.83% respectively suggesting the intrinsic and extrinsic factors influenced the change of land use/cover simultaneously. The structural variance/sill ratio of human impact indexes and change in Fuzhou City were smaller than the ones in Fujian Province respectively which showed scale had important impact on the human impact indexes and change.”

OGC and Smart Ocean Sensor Consortium (SOSC) to Collaborate

The Open Geospatial Consortium (OGC®) and the Smart Ocean Sensor Consortium (SOSC) have signed a Memorandum of Understanding (MoU) to advance sensor observing systems for the oceans community.

Under the agreement the two organizations will participate in joint outreach and marketing activities to raise awareness and interest in smart sensor systems and Sensor Web Enablement. The organizations’ first cooperative activity will focus on the Monterey Bay Research Institute’s (MBARI) PUCK protocol for hydrographic sensor configuration. The PUCK protocol specification has been submitted to the OGC as a candidate standard.

“This agreement is a natural outcome of the active role that the ocean observing community has played in the OGC in recent years,” explained Mark Reichardt, President and CEO of the OGC. “We look forward to an ongoing dialog with the ocean sensor manufacturers and users represented by the SOSC as they tackle a range of ocean-related interoperability issues in activities such as climate research, meteorology, disaster management, resource management, and navigation.”

According to Neil Cater, Chair of the SOSC, “Collecting ocean data can be expensive and challenging. Smart ocean sensors will reduce cost and effort and offer greater value to the end user. The agreement between OGC and the Smart Ocean Sensors Consortium is an important step in establishing a new class of interoperable plug and work sensors.”

[Source: OGC press release]