Processing of soil survey data

This thesis focuses on processing soil survey data into user-specific information. Within this process four steps are distinguished: collection, storage, analysis and presentation. A review of each step is given, and detailed research on important aspects of the steps are presented.Observation density, type of soil attributes and selection of observation sites are important aspects in the collection of soil data. The effect of observation density on the accuracy of spatial predictions was investigated in an acid sulphate soil area in Indonesia. It was found that a similar accuracy could be obtained with a marked reduction in observation density.Most attributes collected in soil survey are on an ordinal measurement scale. Commonly used statistics, such as mean, standard deviation and semivariance, and most spatial interpolation techniques are not permissible for this type of data. Ordinal data from a soil survey in Costa Rica are used to illustrate processing possibilities. For instance, the spatial-difference-probability function was proposed for describing the spatial structure of ordinal data.Over the past twenty years the storage of soil survey data in information systems has been receiving much attention. Digital storage is essential for rapid analysis of data. The soil information system of The Netherlands is described.Seven main categories of soil data analysis can be distinguished. Examples of some categories are presented. The differences between interpreted soil maps on scales of 1 : 10 000, 1 : 25 000 and 1 : 50 000 for predicting moisture deficits and changes in crop yield were investigated. No differences in quality were found between the three maps when predicting average values for an area. The best predictions for point locations, however, were obtained with the 1 : 10 000 map.Also a comparison was made between a thematic map produced by spatial prediction from point data (kriging) and one derived from a general-purpose soil map. The thematic map contain attributes that are important for water movement in the soil. No significant difference in purity was found between the two maps. When combining soil data with other spatial data a vector to raster conversion of the soil map is often necessary. Several sheets of the soil map of The Netherlands 1 : 50 000 of different complexity were investigated for the magnitude of the rasterizing error. The regression equations determined related map complexity to rasterizing error. The rasterizing error of a complex map may be as high as 20% for a raster cell size of 4 mm * 4 mm.Two display methods are introduced for the presentation of uncertainty in soil data. The first method yields an isoline map with empirical confidence limits based on the use of kriging and associated estimated kriging variance. The second method yields a map showing the probability that a certain threshold value is exceeded. When presenting soil data in the form of a map, the complexity of the map pattern has an important influence on its readability. Six complexity measures for maps were compared. The fragmentation index was selected as the best measure for evaluating map complexity

Surface hydrologic modeling and GIS: a case study of the Kaimai Hydropower project catchment, New Zealand

The recent trend towards spatial hydrologic modeling is due to advances in the field of geographic information systems (GIS). Spatially distributed models take into account the spatial variability within the catchment and allow users to define parameters for each sub-watershed or river section depending on the availability of the data. A study was conducted at the Kaimai Hydropower catchment, Tauranga, to identify the potential advantages and disadvantages of spatial hydrologic modeling for use in surface runoff estimation while emphasizing the importance of GIS data quality and discussing the latest developments in the field of GIS. This study has developed a number of techniques to improve the usefulness of GIS for surface hydrologic modeling. A black box runoff model was also developed in order to evaluate the effectiveness of spatial surface hydrologic model as an inflow prediction tool. The Kaimai Hydropower scheme is a small storage-constrained scheme and it is very important that such schemes optimize their available water resources in the present competitive electricity marketing environment. River inflow forecasts are an important part of optimizing hydropower schemes. A black box type inflow prediction model was developed and used as an input to the Kaimai Hydropower scheduling software (HYMAX). HYMAX results indicated a 7% improvement in the operation of the hydropower scheme when HYMAX results were compared with the control room operation. It is also important for the management of any hydropower scheme to see any impact of landuse change on their river resources. The Kaimai Hydropower catchment has undergone a landuse change from native bush to pinus radiata in several parts of the catchment since 1982. The analysis of electrical power output as a proxy for catchment water yield could not detect any reduction in annual or seasonal water yield. A slight increase in the water yield in winter and spring seasons after landuse change is attributed to the incremental nature of the landuse change. The use of GIS in the field of hydrology contains many hidden errors and the user must be aware of the quality of the data before its use. Different types of GIS errors such as generalization, rasterizing error, sink artifacts (and their impacts on the surface hydrologic modeling) were studied and solutions proposed. The digital elevation model is the basic digital data set to be used in surface hydrologic modeling using GIS. A technique was developed to build a hydrologically sound digital elevation model, and this was then applied to develop a surface runoff model using the curve number (CN) approach of excess rainfall estimation within the GIS framework. The unit hydrograph was used to translate the time distribution of excess rainfall into a runoff hydrograph, and routed at the watershed outlet using the Muskingum method. However, the CN method failed in the study area because the region has a high infiltration rate coupled with deep percolation through joints and cracks. The subsurface geology, rather than land surface characteristics, were the dominant factor here, so surface classification methods such as CN could not support predicting quickflow volumes. A map-based surface water flow simulation model, which is based on Geographic Information System (GIS) and object oriented programming (OOP), was evaluated after making necessary changes in its original code and applied to the study area to see its applicability as an inflow prediction tool. The selection of this model was based on its strength in addressing GIS and hydrologic modeling, as an integrated field in the area of runoff prediction involving time series data. The model gave a good match when compared with both observed and black box predicted inflows. It proved to be a good strategic management tool for planning purposes, but at present has limited use as an operational tool because of greater computational requirements involved. However, the map-based model is a good addition in the field of integrated spatial hydrologic modeling using GIS. It also solves many of the basic problems such as feature oriented map operations, dynamic segmentation of an arc, and spatial time series database development, which until recently were not possible in a GIS environment. This study shows the effective use of black box and GIS techniques by applying them to the study area, and demonstrates the integrated surface hydrologic modeling using ARC/INFO GIS and OOP in an ARCVIEW GIS environment. The importance of the GIS data quality for hydrologic applications is explained by studying the different types of GIS errors, and techniques were developed to handle the errors to improve the quality of the digital data. This study has also addressed new developments in surface hydrologic modeling within a GIS framework; and hopefully some of the solutions presented here will be of value to future work in spatial hydrology and related fields

Global irrigated area mapping: Overview and recommendations

Mapping / Data collection / Data storage and retrieval / Water harvesting / Irrigated sites / Climate / Satellite surveys / Evaporation / Food production / Sustainability / Soil water / Models

VGC 2023 - Unveiling the dynamic Earth with digital methods: 5th Virtual Geoscience Conference: Book of Abstracts

Conference proceedings of the 5th Virtual Geoscience Conference, 21-22 September 2023, held in Dresden. The VGC is a multidisciplinary forum for researchers in geoscience, geomatics and related disciplines to share their latest developments and applications.:Short Courses 9 Workshops Stream 1 10 Workshop Stream 2 11 Workshop Stream 3 12 Session 1 – Point Cloud Processing: Workflows, Geometry & Semantics 14 Session 2 – Visualisation, communication & Teaching 27 Session 3 – Applying Machine Learning in Geosciences 36 Session 4 – Digital Outcrop Characterisation & Analysis 49 Session 5 – Airborne & Remote Mapping 58 Session 6 – Recent Developments in Geomorphic Process and Hazard Monitoring 69 Session 7 – Applications in Hydrology & Ecology 82 Poster Contributions 9

Predicting Soil Organic Carbon and Nitrogen Content Using Airborne Laser Scanning in the Taita Hills, Kenya

Reducing greenhouse gas emissions and increasing carbon sequestration is critical for climate change mitigation. With the emergence of carbon markets and the development of compensatory mechanisms such as Reducing Emissions from Deforestation and Degradation in Developing Countries (REDD+), there is much interest in measurement and monitoring of soil organic carbon (SOC). Detailed information on the distribution of SOC and other soil attributes, such as nitrogen (N), across the landscape is necessary in order to locate areas where carbon stocks can be increased and loss of soil carbon slowed down. SOC has large spatial variability, which often demands intensive sampling in the field. Airborne laser scanning (ALS) provides very accurate information about the topography and vegetation of the measured area, and hence, possible means for improving soil properties maps. In this thesis, the aim was to study the feasibility of ALS and free of cost ancillary data for predicting SOC and N in a tropical study area. The study area is located in the Taita Hills, in South-Eastern Kenya, and has highly fluctuating topography ranging between 930–2187 m. Land cover in the Taita Hills is very heterogeneous and consists of forest, woodlands, agroforestry and croplands. The field data consisted of SOC and N measurements for 150 sample plots (0.1 ha). The soil samples along with several other soil and vegetation attributes were collected in 2013. ALS (Optech ALTM 3100, mean return density 11.4 m-1) data was acquired in February 2013. ALS data was pre-processed by classifying ground, low- and high vegetation, buildings and power wires. ALS point cloud was used to calculate two types of predictors for SOC and N: 1) topographical variables based on the high resolution digital terrain model (DTM) and 2) ALS metrics describing the vertical distribution and cover of vegetation. The ancillary datasets included spectral predictors based on Landsat 7 ETM+ time series and soil grids for Africa at 250 m resolution. In total, over 500 potential predictors were calculated for the modelling. Random Forest model was constructed from the selected variables and model performance was analysed by comparing the predicted values to the field measurements. The best model for SOC had pseudo R2 of 0.66 and relative root mean square error (RRMSE) of 30.98 %. Best model for N had pseudo R2 of 0.43 and RRMSE of 32.14 %. Usage of Landsat time series as ancillary dataset improved the modelling results slightly. For SOC, the most important variables were tangential curvature, maximum intensity and Landsat band 2 (green). Finally, the best model was applied for mapping SOC and N in the study area. The results of this study are in line with other remote sensing studies modelling soil properties in Africa. The soil properties in the study area do not correlate strongly with present vegetation and topography leading to intermediate modelling results.Kasvihuonepäästöjen vähentäminen ilmakehästä on kriittistä ilmastonmuutoksen hillitsemisen kannalta. Hiilimarkkinoiden ja erilaisten korvausmekanismien kehittyminen on lisännyt kiinnostusta maaperässä olevan orgaanisen hiilen mittaamiseen ja monitoroimiseen. Yksityiskohtainen tieto maaperän ominaisuuksista, kuten orgaanisen hiilen ja typen alueellisesta jakaumasta, voi auttaa löytämään alueita, joissa hiilen osuutta voidaan kasvattaa tai sen vähentymistä voidaan hidastaa. Maaperän hiilen vaihtelevasta spatiaalisesta jakaumasta johtuen kalliita kenttämittauksia tarvitaan runsaasti. Lentolaserkeilaus tarjoaa tarkkaa tietoa kuvatun alueen topografiasta ja kasvillisuudesta, mikä voisi olla hyödyllistä maaperän karttojen laadun parantamisessa. Tämän tutkimuksen tavoitteena oli selvittää, miten lentolaserkeilaus ja vapaasti saatavilla oleva lisäaineisto soveltuvat maaperän orgaanisen hiilen ja typen pitoisuuksien ennustamiseen. Tutkimusalue on Taitavuorilla Kaakkois-Keniassa, jossa topografia on hyvin vaihtelevaa, korkeuden vaihdellessa 930 ja 2187 metrin välillä. Taitavuorten maanpeite on hyvin heterogeenistä ja koostuu metsistä, metsämaasta, peltometsäviljelysmaista ja viljelysmaista. Tutkimuksessa käytetty kenttäaineisto koostuu 150:sta maaperän hiili- ja typpimittauksista 0.1 hehtaarin kokoisilta koealoilta. Maaperän mittaukset suoritettiin vuonna 2013. Lentolaserkeilausaineisto (Optech ALTM 3100) kuvattiin helmikuussa 2013. Kuvattu lentolaserkeilausaineisto esikäsiteltiin luokittelemalla maaperä, matala ja korkea kasvillisuus, rakennukset ja voimalinjat. Lentolaserkeilausaineistoa käytettiin kahden tyyppisten muuttujien laskennassa: 1) topografiamuuttujat, jotka laskettiin erittäin korkearesoluutioisesta korkeusmallista ja 2) kasvillisuuden vertikaalisesta rakenteesta ja peitosta kertoviin muuttujiin. Lisäaineistona analyysissä oli mukana spektraalista tietoa sisältävä Landsat ETM+ aikasarja, sekä maaperäruudukot Afrikasta 250 m:n spatiaalisella resoluutiolla. Yhteensä noin 500 muuttujaa laskettiin mallinnusta varten. Random Forest -malli rakennettiin valituista muuttujista ja mallien suorituskykyä arvioitiin vertaamalla ennustettuja arvoja havaittuihin arvoihin. Parhaan maaperän hiilimallin valeselitysaste oli 0.66 ja suhteellinen keskivirhe 30.98 %. Parhaan typpimallin valeselitysaste oli 0.43 ja suhteellinen keskivirhe 32.14 %. Tärkeimmät muuttujat maaperän hiilen ennustamiseen olivat tangentiaalinen kaarevuus (tangential curvature), maksimi-intensiteetti (maximum intensity) ja Landsatin kanava 2 (vihreä aallonpituus). Landsat aineiston käyttö avustavana aineistona johti pieniin parannuksiin mallinnuksessa. Lopulta maaperän hiili- ja typpikartat ennustettiin käyttämällä parhaita löydettyjä malleja. Tämän tutkimuksen tulokset ovat linjassa muiden kaukokartoitusta hyödyntävien maaperän ominaisuuksia tutkivien tutkimuksien kanssa. Maaperän ominaisuudet eivät korreloineet voimakkaasti kasvillisuuden ja topografian kanssa, mikä johti keskinkertaisiin tuloksiin

Investigating habitat association of breeding birds using public domain satellite imagery and land cover data.

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesTwenty-five years after the implementation of the Birds Directive in 1979, Europe‟s farmland bird species and long-distance migrants continue to decrease at an alarming rate. Farmland supports more bird species of conservation concern than any other habitat in Europe. Therefore, it is imperative to understand farmland species‟ relationship with their habitats. Bird conservation requires spatial information; this understanding not only serves as a check on the individual species‟ populations, but also as a measure of the overall health of the ecosystem as birds are good indicators of the state of the environment. The target species in this study is the corn bunting Miliaria calandra, a bird whose numbers in northern and central Europe have declined sharply since the mid-1970s. This study utilizes public domain data, namely Landsat imagery and CORINE land cover, along with the corn bunting‟s presence-absence data, to create a predictive distribution map of the species based on habitat preference. Each public domain dataset was preprocessed to extract predictor variables. Predictive models were built in R using logistic regression.(...

Incorporating anthropogenic influences into fire probability models : effects of human activity and climate change on fire activity in California

The costly interactions between humans and wildfires throughout California demonstrate the need to understand the relationships between them, especially in the face of a changing climate and expanding human communities. Although a number of statistical and process-based wildfire models exist for California, there is enormous uncertainty about the location and number of future fires, with previously published estimates of increases ranging from nine to fifty-three percent by the end of the century. Our goal is to assess the role of climate and anthropogenic influences on the state's fire regimes from 1975 to 2050. We develop an empirical model that integrates estimates of biophysical indicators relevant to plant communities and anthropogenic influences at each forecast time step. Historically, we find that anthropogenic influences account for up to fifty percent of explanatory power in the model. We also find that the total area burned is likely to increase, with burned area expected to increase by 2.2 and 5.0 percent by 2050 under climatic bookends (PCM and GFDL climate models, respectively). Our two climate models show considerable agreement, but due to potential shifts in rainfall patterns, substantial uncertainty remains for the semiarid inland deserts and coastal areas of the south. Given the strength of human-related variables in some regions, however, it is clear that comprehensive projections of future fire activity should include both anthropogenic and biophysical influences. Previous findings of substantially increased numbers of fires and burned area for California may be tied to omitted variable bias from the exclusion of human influences. The omission of anthropogenic variables in our model would overstate the importance of climatic ones by at least 24%. As such, the failure to include anthropogenic effects in many models likely overstates the response of wildfire to climatic change

Mapping and monitoring of vegetation using airborne laser scanning

In this thesis, the utility of airborne laser scanning (ALS) for monitoring vegetation of relevance for the environmental sector was investigated. The vegetation characteristics studied include measurements of biomass, biomass change and vegetation classification in the forest-tundra ecotone; afforestation of grasslands; and detection of windthrown trees. Prediction of tree biomass for mountain birch (Betula pubescens ssp. czerepanovii) using sparse (1.4 points/m²) and dense (6.1 points/m²) ALS data was compared for a site at the forest-tundra ecotone near Abisko in northern Sweden (Lat. 68° N, Long. 19° E). The predictions using the sparse ALS data provided almost as good results (RMSE 21.2%) as the results from the dense ALS data (18.7%) despite the large difference in point densities. A new algorithm was developed to compensate for uneven distribution of the laser points without decimating the data; use of this algorithm reduced the RMSE for biomass prediction from 19.9% to 18.7% for the dense ALS data. Additional information about vegetation height and density from ALS data improved a satellite data classification of alpine vegetation, in particular for the willow and mountain birch classes. Histogram matching was shown to be effective for relative calibration of metrics from two ALS acquisitions collected over the same area using different scanners and flight parameters. Thus the difference between histogram-matched ALS metrics from different data acquisitions can be used to locate areas with unusual development of the vegetation. The height of small trees (0.3–2.6 m tall) in former pasture land near the Remnings¬torp test site in southern Sweden (Lat. 58° N, Long. 13° E) could be measured with high precision (standard deviation 0.3 m) using high point density ALS data (54 points/m2). When classifying trees taller than 1 m into the two classes of changed and unchanged, the overall classification accuracy was 88%. A new method to automatically detect windthrown trees in forested areas was developed and evaluated at the Remningstorp test site. The overall detection rate was 38% on tree-level, but when aggregating to 40 m square grid cells, at least one windthrown tree was detected in 77% of the cells that according to field data contained windthrown trees. In summary, this thesis has shown the high potential for ALS to be a future tool to map and monitor vegetation for several applications of interest for the environmental sector

Examination of the Potential of Structure-from-Motion Photogrammetry and Terrestrial Laser Scanning for Rapid Nondestructive Field Measurement of Grass Biomass

Above ground biomass (AGB) is a parameter commonly used for assessment of grassland systems. While destructive sampling of AGB is highly accurate, it is time consuming and often precludes repeat temporal sampling or sampling in sensitive ecosystems. Consequently, a number of nondestructive techniques that relate grass structural properties to AGB have been developed. This study investigated the application of two recent technologies, Terrestrial Laser Scanning (TLS) and Structurefrom- Motion (SfM), in the development of rapid nondestructive AGB estimation of grassland plots. TLS and SfM volume metrics generated using a rasterized surface differencing method were linearly related to destructively measured total AGB and grass AGB excluding all litter, and results were compared to the conventional disc pasture meter. The linear models were assessed using a leave-one-out cross validation scheme. The disc pasture meter was found to be the least reliable method in assessing total AGB (r2 = 0.32, RMSELOOCV = 269 g/m2). SfM (r2 = 0.74, RMSELOOCV = 169 g/m2) outperformed TLS (r2 = 0.56, RMSELOOCV = 219 g/m2), though a much larger slope in SfM regressions suggests an increased sensitivity to error. Litter removal decreased the effectiveness of AGB estimation for both TLS (r2 = 0.49) and SfM (r2 = 0.51) but increased the fit of disc pasture meter estimations (r2 = 0.42), highlighting the complex relationship between litter accumulation and AGB. TLS and SfM derived volumes were shown to be insensitive to cell dimensions when calculating volume provided cell dimensions were large enough to ensure no empty cells occurred. Using observed ground surfaces in volumetric calculations rather than an estimated ground plane increased r2 to 0.63 for TLS and 0.77 for SfM. Though the disc pasture meter was found to be the most rapid of the three methods, TLS and SfM both out performed it and have clearly demonstrated their potential utility for AGB estimation of grass systems. Their ability to systematically collect measurements over larger spatial extents than those investigated here could greatly outpace the disc pasture meter’s predictive capabilities and speed