P.A.J. van Oort

Improving land cover change estimates by accounting for classification errors

In monitoring land cover change by overlay of two maps from different dates, the rate of change is frequently overestimated. This is due to three sources of uncertainty: (1) semantic differences in class definitions between two maps, (2) positional errors and (3) classification errors. In this study, four methods are proposed that use the Bayes theorem to update prior estimates of land cover change with information on the probabilities with which land cover classes are mistaken for each other. The methods were illustrated for two case studies. In the first case study, the real change was 1.4%, and by overlay of the two maps, 7.4% was predicted. The estimates by the four methods were 6.3%, 15.3%, 6.7% and 1.6%. In the second study, these percentages were 48% and 36%, and with our four methods: 39.2%, 54.1%, 50.8% and 53.0%. Two of the methods account for the correlation in classification accuracy between maps of two dates. Where this correlation was high (study area 1), the methods that accounted for corre...In monitoring land cover change by overlay of two maps from different dates, the rate of change is frequently overestimated. This is due to three sources of uncertainty: (1) semantic differences in class definitions between two maps, (2) positional errors and (3) classification errors. In this study, four methods are proposed that use the Bayes theorem to update prior estimates of land cover change with information on the probabilities with which land cover classes are mistaken for each other. The methods were illustrated for two case studies. In the first case study, the real change was 1.4%, and by overlay of the two maps, 7.4% was predicted. The estimates by the four methods were 6.3%, 15.3%, 6.7% and 1.6%. In the second study, these percentages were 48% and 36%, and with our four methods: 39.2%, 54.1%, 50.8% and 53.0%. Two of the methods account for the correlation in classification accuracy between maps of two dates. Where this correlation was high (study area 1), the methods that accounted for correlation yielded change estimates closer to the real change than the methods that did not account for this correlation.

Using quadtree segmentation to support error modelling in categorical raster data

This paper explores the use of quadtree segmentation of a land-cover map to improve error modelling by (1) accounting for variation in classification accuracy among differently sized homogeneous map regions and (2) improving the statistical properties of map realizations generated by sequential indicator simulation (SIS). The latter was accomplished by locating the first simulation nodes—which affect many subsequent local simulations—within the largest quadtree leaves. These represent the largest homogeneous and hypothetically most accurately classified areas on the map. A case study showed that, indeed, the overall accuracy of the land-cover map increased with the quadtree leaf size, ranging from 67% for single cells in heterogeneous areas to 98% for homogeneous blocks of 256 cells. A map of the prior probability of each land-cover class was prepared on the basis of quadtree level-specific confusion matrices. Next, two unconditional SIS algorithms were used to generate sets of 50 realizations of the map, thereby accounting for spatial continuity of the residuals between indicator-transformed reference data and the priors. The proposed quadtree-guided SIS outperformed the more common multiple-steps method as judged by the reproduction of target proportions of map categories, class-specific accuracy levels and variogram reproduction.

Do users ignore spatial data quality? A decision-theoretic perspective

Risk analysis (RA) has been proposed as a means of assessing fitness for use of spatial data but is only rarely adopted. The proposal is that better decisions can be made by accounting for risks due to errors in spatial data. Why is RA so rarely adopted? Most geographical information science (GISc) literature stresses educational and technical constraints. In this article we propose, based on decision theory, a number of hypotheses for why the user would be more or less willing to spend resources on RA. The hypotheses were tested with a questionnaire, which showed that the willingness to spend resources on RA depends on the presence of feedback mechanisms in the decision-making process, on how much is at stake, and to a minor extent on how well the decision-making process can be modeled.

Geoportals: An Internet Marketing Perspective

A geoportal is a web site that presents an entry point to geo-products (including geo-data) on the web. Despite their importance in (spatial) data infrastructures, literature suggest stagnating or even declining trends in visitor numbers. In this paper relevant ideas and techniques for improving performance are derived from internet marketing literature. We tested the extent to which these ideas are already applied in practice through a survey among 48 geoportals worldwide. Results show in many cases positive correlation with trends in visitor numbers. The ideas can be useful for geoportal managers developing their marketing strategy.

Phenology, sterility and inheritance of two environment genic male sterile (EGMS) lines for hybrid rice

BackgroundThere is still limited quantitative understanding of how environmental factors affect sterility of Environment-conditioned genic male sterility (EGMS) lines. A model was developed for this purpose and tested based on experimental data from Ndiaye (Senegal) in 2013-2015. For the two EGMS lines tested here, it was not clear if one or more recessive gene(s) were causing male sterility. This was tested by studying sterility segregation of the F2 populations. ResultsDaylength (photoperiod) and minimum temperatures during the period from panicle initiation to flowering had significant effects on male sterility. Results clearly showed that only one recessive gene was involved in causing male sterility. The model was applied to determine the set of sowing dates of two different EGMS lines such that both would flower at the same time the pollen would be completely sterile. In the same time the local popular variety (Sahel 108, the male pollen donor) being sufficiently fertile to produce the hybrid seeds. The model was applied to investigate the viability of the two line breeding system in the same location with climate change (+2oC) and in two other potential locations: in M’Be in Ivory Coast and in the Nile delta in Egypt.ConclusionsApart from giving new insights in the relation between environment and EGMS, this study shows that these insights can be used to assess safe sowing windows and assess the suitability of sterility and fertility period of different environments for a two line hybrid rice production system.

Detection and Risk for Digging Activities around Underground Cables and Pipelines: Implications for Spatial Data Quality

Digging activities are considered the largest cause of damage to underground cables and pipelines. Contractors can reduce the risk through detection, which will cost time and thus money. In the Netherlands, maps are the prime source of information on the location of cables/pipelines and detection time strongly depends on whether maps indicate the presence of cables and pipelines. Poor quality maps can contribute to increased risk or higher risk avoidance costs. The objective of this article is to present a model for calculating the trade-off between detection costs and risk in case of a hit and for calculating implications of over- and incompleteness of maps. The model aims to find the optimal detection time at which the sum of detection cost and risk is at its minimum. A case-study showed that it is possible to parameterise the model with data collected from contractors through a questionnaire. The case-study provides a numerical example of calculation of the trade-off between risk and detection costs and provides an example of calculation of costs of incompleteness. We conclude that the model contributes valuable new insight. However, more and location specific data are needed to enable operational use of the model.

Mapping abiotic stresses for rice in Africa: Drought, cold, iron toxicity, salinity and sodicity

Highlights • Hotspots of drought, cold, iron toxicity salinity/sodicity stress occurrence for rice in Africa.• Maps for targeted distribution of tolerant varieties.• Drought most important stress (33% of rice area) then iron toxicity (12%).• Risk of cold/salinity/sodicity in 7–2% of Africa’s rice area.