Ancillary Geographical Data Research Articles

Estimating neighborhood-level population characteristics from parcel data

ABSTRACT This paper investigates how ancillary geographic data – in particular, information on land or assessor parcels – might be used to improve estimates for small area populations and population characteristics. It seeks to determine whether parcel land use codes can be used to reliably replicate population and housing distributions within small (subcounty) areas and whether other parcel attributes – in addition to land use – exhibit any explanatory power in replicating population and housing characteristics within these same places. The basis for this paper was Professor Barbara Buttenfield’s service on the Census Scientific Advisory Committee, in which her working group explored the utility of administrative source data as an alternative or complement to federal survey data. This analysis highlights some of the benefits and complications of the incorporation of parcel data into geodemographic estimation, and the findings demonstrate that such a use is problematic but encouraging.

Distribution patterns of macrophytes in shallow lakes of the lower Paraná River floodplain: Associations with environmental conditions

Abstract Macrophytes play key functional and structural roles in floodplain shallow lakes (SL), determining the maintenance of ecosystem functions and sustaining several productive activities. We assessed the relationship between macrophyte community structure (composition, species abundance, and plant diversity) and environmental features (SL dynamics, connectivity, morphometry, flood frequency, and water and sediment physical and chemical variables) in the Lower Paraná River floodplain. Our main hypothesis was that SL macrophyte distribution depends on direct and indirect environmental controls operating at multiple scales. The SL dynamics during the April–November 2017 Paraná River pulse were described according to their coverage changes (vegetation, and turbid or clear open water), which were assessed with supervised classifications on Landsat 8‐OLI scenes. Twenty‐five SL of the Lower Paraná River floodplain were selected to represent all the SL dynamics identified. Ten SL were permanently connected and 15 were disconnected from watercourses. Water and sediment physical and chemical characteristics, macrophyte assemblage composition, and plant species abundance were surveyed at each site in the 2018 summer. Also, SL morphometric parameters and flood frequency were estimated from ancillary geographic data. The direct controlling factors of macrophyte assemblages were water ammonium and water nitrate concentrations, turbidity, and flood frequency. No clear effects of environmental conditions on macrophyte diversity were found. The connectivity of SL with watercourses affected their water and sediment physical and chemical features. Also, SL dynamics during the 2017 pulse were related to flood frequency. As connectivity to watercourses affects SL chemical and physical variables, and SL dynamics were associated with flood frequency, connectivity and SL dynamics indirectly influenced macrophyte assemblage composition. This study improved our understanding of macrophyte distribution patterns in the study area, which was related to several environmental variables operating at different scales. Our results highlight the importance of the variability of hydrological connectivity in promoting environmental heterogeneity in river floodplain systems, which is central to their functioning, and the high biodiversity that characterises these environments.

Assessing Potential Bioenergy Production on Urban Marginal Land in 20 Major Cities of China by the Use of Multi-View High-Resolution Remote Sensing Data

For the purpose of bioenergy production, biomass cropping on marginal land is an appropriate method. Less consideration has been given to estimating the marginal land in cities at a fine spatial resolution, especially in China. Marginal land within cities has great potential for bioenergy production. Therefore, in this research, the urban marginal land of 20 representative cities of China was estimated by using detailed land-cover and 3D building morphology information derived from Ziyuan-3 high-resolution remote sensing imagery, and ancillary geographical data, including land use, soil type, and digital elevation model data. We then classified the urban marginal land into “vacant land” and “land between buildings”, and further revealed its landscape patterns. Our results showed that: (1) the suitable marginal land area ranged from 17.78 ± 1.66 km2 to 353.48 ± 54.19 km2 among the 20 cities; (2) it was estimated that bioethanol production on marginal land could amount to 0.005–0.13 mT, corresponding to bioenergy of 2.1 × 1013–4.0 × 1014 J for one city; (3) from the landscape viewpoint, the marginal landscape pattern tended to be more fragmented in more developed cities. Our results will help urban planners to reclaim unused urban land and develop distributed bioenergy projects at the city scale.

Land Cover Classification in an Ecuadorian Mountain Geosystem Using a Random Forest Classifier, Spectral Vegetation Indices, and Ancillary Geographic Data

We presented a methodology to accurately classify mountainous regions in the tropics. These landscapes are complex in terms of their geology, ecosystems, climate and land use. Obtaining accurate maps to assess land cover change is essential. The objectives of this study were to (1) map vegetation using the Random Forest Classifier (RFC), spectral vegetation index (SVI), and ancillar geographic data (2) identify important variables that help differentiate vegetation cover, and (3) assess the accuracy of the vegetation cover classification in hard-to-reach Ecuadorian mountain region. We used Landsat 7 ETM+ satellite images of the entire scene, a RFC algorithm, and stratified random sampling. The altitude and the two band enhanced vegetation index (EVI2) provide more information on vegetation cover than the traditional and often use normalized difference vegetation index (NDVI) in other settings. We classified the vegetation cover of mountainous areas within the 1016 km2 area of study, at 30 m spatial resolution, using RFC that yielded a land cover map with an overall accuracy of 95%. The user´s accuracy and the half-width of the confidence interval for 95% of the basic map units, forest (FOR), páramo (PAR), crop (CRO) and pasture (PAS) were 95.85% ± 2.86%, 97.64% ± 1.24%, 91.53% ± 3.35% and 82.82% ± 7.74%, respectively. The overall disagreement was 4.47%, which results from adding 0.43% of quantity disagreement and 4.04% of allocation disagreement. The methodological framework presented in this paper and the combined use of SVIs, ancillary geographic data, and the RFC allowed the accurate mapping of hard-to-reach mountain landscapes as well as uncovering the underlying factors that help differentiate vegetation cover in the Ecuadorian mountain geosystem.

Identification and quantification of drivers of forest degradation in tropical dry forests: A case study in Western Mexico

The intensity of forest degradation is linked to landowners’ decisions on management of their shifting cultivation systems. Understanding the processes involved in this land use type is therefore essential for the design of sustainable forest management practices. However, knowledge of the processes and patterns of forest transition that result from this practice is extremely limited. In this study, we used spatially-explicit binary logistic regression to study the proximate factors that relate to forest degradation by combining biophysical and socio-economic variables. Our study region is within the Ayuquila Basin, in Western Mexico, a typical fragmented tropical dry forest landscape dominated by shifting cultivation. Through a survey and semi-structured interviews with community leaders, we obtained data on the forest resources and on the uses that people make of them. Detailed forest cover maps for 2004 and 2010 were produced from high-resolution SPOT 5 data, and ancillary geographical data were used to extract spatial variables. The degree of social marginalization of each community and the ratio of forest area to population size were the main factors positively correlated with the probability of the occurrence of forest degradation. Livestock management and use of fence posts by the communities were also positively associated with forest degradation. Among biophysical factors, forest degradation is more likely to occur in flatter areas. We conclude that local drivers of forest degradation include both socioeconomic and physical variables and that both of these factors need to be addressed at the landscape level while developing measures for activities related to REDD+.

A high-resolution population grid map for Europe

Population figures are usually collected by national statistical institutes at small enumeration units (e.g. census tracts or building units). However, still for many countries in Europe, data are distributed at coarser geographical units like municipalities. This level of resolution is insufficient for analysis in many fields. In addition, the heterogeneity of the size of the geographical units causes great distortions in analysis, i.e. the Modifiable Areal Unit Problem (MAUP). Dasymetric mapping techniques have long been applied world-wide to derive finer (and MAUP-free) depictions of the population distribution. These techniques disaggregate population figures reported at coarse source zones into a finer set of zones using ancillary geographical data. Previous attempts to map the European population at high resolution have used CORINE Land Cover (CLC) as the main source of ancillary data. In this article, we test new geographical datasets to produce an updated and improved European population grid map. It is tested whether using more detailed ancillary data in the dasymetric mapping significantly yields higher accuracies. As final outcome of this cartographic exercise, a European population grid map for the reference year of 2006, with a spatial resolution of 100 × 100 meters, is presented and validated against reference data. Resident population reported at commune level, a refined version of CLC and information on the soil sealing degree are used as the main inputs to produce the final map.

The Dynamic Monitoring of Coal Mining Seeper Subsidence District Based on RS and GIS Technology — A Case Study of Huainan City

Using the 1987 TM and 2006 ETM+ remote sensing images and other related ancillary geographical data of Huainan city, combined with GIS Technology, the dynamic change of the coal mining subsidence of Huainan city was extracted. In order to unify the two period image data difference because of the different date, the research used modified normalized difference water index (MNDWI) to get the information of seeper subsidence in coal mining district. Using the change detection function of RS, Combined with the spatial analysis and statistics function of GIS, all the subsidence area of coal mining district and the seeper subsidence area were extracted. The research shows that the seeper subsidence extent is different because of the mining period, scale, method, groundwater level and groundwater extraction, surface coverage, roof’s management and other coal seam geological environments, especially dip angle of coal seam. The Xiejiaji and Bagongshan mine’s mining seeper subsidence are the serious due to its bigger dip angle of coal seam, the seeper subsidence percentage are 38.57%, 25.95% respectively, while Jiulonggang- Datong mining seeper subsidence is the least even if it’s the oldest and abandoned mine in Huainan.

Building population mapping with aerial imagery and GIS data

Geospatial distribution of population at a scale of individual buildings is needed for analysis of people's interaction with their local socio-economic and physical environments. High resolution aerial images are capable of capturing urban complexities and considered as a potential source for mapping urban features at this fine scale. This paper studies population mapping for individual buildings by using aerial imagery and other geographic data. Building footprints and heights are first determined from aerial images, digital terrain and surface models. City zoning maps allow the classification of the buildings as residential and non-residential. The use of additional ancillary geographic data further filters residential utility buildings out of the residential area and identifies houses and apartments. In the final step, census block population, which is publicly available from the U.S. Census, is disaggregated and mapped to individual residential buildings. This paper proposes a modified building population mapping model that takes into account the effects of different types of residential buildings. Detailed steps are described that lead to the identification of residential buildings from imagery and other GIS data layers. Estimated building populations are evaluated per census block with reference to the known census records. This paper presents and evaluates the results of building population mapping in areas of West Lafayette, Lafayette, and Wea Township, all in the state of Indiana, USA.

Improved Land Cover Mapping using Random Forests Combined with Landsat Thematic Mapper Imagery and Ancillary Geographic Data

Large area land-cover mapping involving large volumes of data is becoming more common in remote sensing applications. Thus, there is a pressing need for increased automation in the land-cover mapping process. The main objective of this research was to compare the performance of three machine learning algorithms (MLAS) for mapping wetlands in the Sanjiang Plain combined Landsat TM imagery with ancillary geographical data. Three MLAS included random forest (RF), classification and regression tree (CART), and maximum likelihood classification (MLC). Comparisons were based on several criteria: overall accuracy, sensitivity to data set size, and noise. Our results indicated that first, the random forest and CART approach can achieve substantial improvements in accuracy over the traditional MLC method. Random forest produced the highest overall accuracy (91.3 percent) the kappa coefficient 0.8943, with marsh class accuracies ranging from 77.4 percent to 90.0 percent. Secondly, the random forest method was least sensitive to reduction in training sample size, and it was most resistant to the presence of noise compared to CART and MLC. The comparison between three MLAS revealed that the random forest approach was most resistant to training data deficiencies while improved land-cover map accuracy in marsh area.

Integrating TM and ancillary geographical data with classification trees for land cover classification of marsh area

The main objective of this research is to determine the capacity of land cover classification combining spectral and textural features of Landsat TM imagery with ancillary geographical data in wetlands of the Sanjiang Plain, Heilongjiang Province, China. Semi-variograms and Z-test value were calculated to assess the separability of grey-level co-occurrence texture measures to maximize the difference between land cover types. The degree of spatial autocorrelation showed that window sizes of 3×3 pixels and 11×11 pixels were most appropriate for Landsat TM image texture calculations. The texture analysis showed that co-occurrence entropy, dissimilarity, and variance texture measures, derived from the Landsat TM spectrum bands and vegetation indices provided the most significant statistical differentiation between land cover types. Subsequently, a Classification and Regression Tree (CART) algorithm was applied to three different combinations of predictors: 1) TM imagery alone (TM-only); 2) TM imagery plus image texture (TM+TXT model); and 3) all predictors including TM imagery, image texture and additional ancillary GIS information (TM+TXT+GIS model). Compared with traditional Maximum Likelihood Classification (MLC) supervised classification, three classification trees predictive models reduced the overall error rate significantly. Image texture measures and ancillary geographical variables depressed the speckle noise effectively and reduced classification error rate of marsh obviously. For classification trees model making use of all available predictors, omission error rate was 12.90% and commission error rate was 10.99% for marsh. The developed method is portable, relatively easy to implement and should be applicable in other settings and over larger extents.

Land Cover and Forest Formation Distributions for St. Kitts, Nevis, St. Eustatius, Grenada and Barbados from Decision Tree Classification of Cloud-Cleared Satellite Imagery

Satellite image-based mapping of tropical forests is vital to conservation planning. Standard methods for automated image classification, however, limit classification detail in complex tropical landscapes. In this study, we test an approach to Landsat image interpretation on four islands of the Lesser Antilles, including Grenada and St. Kitts, Nevis and St. Eustatius, testing a more detailed classification than earlier work in the latter three islands. Secondly, we estimate the extents of land cover and protected forest by formation for five islands and ask how land cover has changed over the second half of the 20th century. The image interpretation approach combines image mosaics and ancillary geographic data, classifying the resulting set of raster data with decision tree software. Cloud-free image mosaics for one or two seasons were created by applying regression tree normalization to scene dates that could fill cloudy areas in a base scene. Such mosaics are also known as cloud-filled, cloud-minimized or cloud-cleared imagery, mosaics, or composites. The approach accurately distinguished several classes that more standard methods would confuse; the seamless mosaics aided reference data collection; and the multiseason imagery allowed us to separate drought deciduous forests and woodlands from semi-deciduous ones. Cultivated land areas declined 60 to 100 percent from about 1945 to 2000 on several islands. Meanwhile, forest cover has increased 50 to 950%. This trend will likely continue where sugar cane cultivation has dominated. Like the island of Puerto Rico, most higher-elevation forest formations are protected in formal or informal reserves. Also similarly, lowland forests, which are drier forest types on these islands, are not well represented in reserves. Former cultivated lands in lowland areas could provide lands for new reserves of drier forest types. The land-use history of these islands may provide insight for planners in countries currently considering lowland forest clearing for agriculture.