The global tree carrying capacity (keynote)

editorial reviewe

Updating soil information with digital soil mapping

De Bodemkaart van Nederland, schaal 1:50.000, is de belangrijkste bron van bodeminformatie in Nederland. Deze kaart raakt echter in gebieden met veengronden verouderd. Door intensief gebruik van deze gronden verdwijnt het veen. Actualisatie van de bodemkaart is daarom noodzakelijk. Bas Kempen promoveerde op zijn onderzoek hiernaar

Uso y cobertura del suelo en las islas macaronésicas de Portugal y España: nuevos métodos para cuantificar y visualizar información de patrones espaciales

Tesis de la Universidad Complutense de Madrid, Facultad de Geografía e Historia, Departamento de Geografía Humana, leída el 23/11/2016The aim of this research is to propose novel methods for quantifying and visualizing geographical information, in order to aid the spatial planning decision-making process when addressing land use and land cover patterns. In doing so, several modeling and geographic visualization methods are developed and demonstrated by using the Macaronesian islands of Portugal and Spain as study areas. Macaronesia is a biogeographical region consisting of several archipelagos in the Atlantic Ocean belonging to three countries: Portugal, Spain, and Cape Verde. This research encompasses three archipelagos: the Azores, Madeira, and the Canary Islands. From these three archipelagos, the four most densely populated islands were further selected for the land use and land cover assessments: São Miguel, Madeira, Tenerife, and Gran Canaria. A common feature of the Macaronesian islands is that, ever since European colonization in the fifteenth century, up until the mid-twentieth century, anthropogenic land change was predominately attributable to agricultural activities consuming forests and natural areas. In the mid-twentieth century, owing to profound social and economic changes, the tertiary sector started its rise in becoming the main economic sector. Because the secondary sector in this region has always been minor, this substantial shift to the tertiary sector would dictate a progressive abandonment of the primary sector. Hence, agricultural areas started to recede. As a result, the last decades of the twentieth century were marked by a significant shift in land use dynamics. Agricultural activities ceased to be the main driving force of land change and were replaced by a rampant increase of the artificial surfaces, mainly on the southern coastal areas, where tourism-related and real estate pressure constitute a major impact on the landscape. A direct consequence of this pressure was the drastic transformation across the islands’ leeward coastal landscapes...El objetivo principal de esta investigación es proponer nuevos métodos para cuantificar y visualizar información geográfica, con el fin de facilitar el proceso de toma de decisiones en relación a los patrones de uso y ocupación del suelo. De este modo, se desarrollan y aplican varios métodos de modelación y visualización geográfica, utilizando las islas macaronésicas de Portugal y España como áreas de estudio. La Macaronesia es una región biogeográfica que integra varios archipiélagos en el Océano Atlántico pertenecientes a tres países: Portugal, España y Cabo Verde. Esta investigación abarca tres archipiélagos: Azores, Madeira y Canarias. Para una evaluación detallada de uso y cobertura del suelo se seleccionaron las cuatro islas más densamente pobladas: San Miguel, Madeira, Tenerife y Gran Canaria. Una característica común a las islas macaronésicas es que, desde de la colonización en el siglo XV hasta mediados del siglo XX, el cambio antropogénico del suelo se debió principalmente a las actividades agrícolas, que ocuparon bosques y áreas naturales. A mediados del siglo XX, debido a profundos cambios sociales y económicos, el sector terciario empezó su ascenso para convertirse en el principal sector económico. Debido a que el sector secundario en esta región siempre ha tenido una importancia menor, este proceso de terciarización de la economía supuso un progresivo abandono del sector primario. Por lo tanto, las áreas agrícolas comenzaron a experimentar un claro retroceso. Como resultado de este proceso, las últimas décadas del siglo XX se caracterizaron por un cambio significativo en las dinámicas de uso y cobertura del suelo. Las actividades agrícolas dejaron de ser la principal fuerza impulsora en el cambio de lo suelo y fueron reemplazadas por el aumento desenfrenado de las superficies artificiales, principalmente en las zonas costeras del sur, donde el turismo y la especulación inmobiliaria ejercen una gran presión sobre el paisaje. Consecuencia directa de esta presión fueron las drásticas transformaciones de los paisajes costeros de las islas...Esta investigação tem como principal objectivo propor novos métodos para quantificar e visualizar informação geográfica, de modo a auxiliar o processo de tomada de decisão quando seja necessário analisar padrões de uso e ocupação do solo. Ao longo da investigação são apresentados vários métodos de modelação e visualização geográfica, usando como área de estudo as ilhas da Macaronésia pertencentes a Portugal e Espanha. A Macaronésia é uma região biogeográfica no Oceano Atlântico constituída por vários arquipélagos pertencentes a três países: Portugal, Espanha e Cabo Verde. Este trabalho de investigação abrange três arquipélagos: os Açores, a Madeira e as Ilhas Canárias. Para uma avaliação mais detalhada quanto ao uso e ocupação do solo, foram seleccionadas as quatro ilhas mais densamente povoadas: São Miguel, Madeira, Gran Canaria e Tenerife. Uma característica comum às ilhas da Macaronésia reside na particularidade de, desde a sua colonização no século XV, até meados do século XX, as alterações antropogénicas do solo terem estado predominantemente associadas às actividades agrícolas que consumiram extensas áreas de floresta e espaços naturais. Em meados do século XX, devido a profundas alterações sociais e económicas, o sector terciário iniciou a sua ascensão para se tornar o principal sector económico. Uma vez que, nesta região, o sector secundário foi sempre pouco significativo, a terciarização da actividade económica ditou um progressivo abandono do sector primário. Deste modo, as áreas agrícolas começaram a recuar. Como resultado deste processo, as últimas décadas do século XX foram marcadas por uma mudança significativa na dinâmica de uso e ocupação do solo nas ilhas desta região. As actividades agrícolas deixaram de ser a principal força motriz para as alterações no uso do solo, sendo substituídas pelo aumento galopante das superfícies artificiais, principalmente nas áreas costeiras do sul, onde as actividades relacionadas com o turismo e a especulação imobiliária causaram um grande impacto na paisagem, e contribuiram para a transformação drástica do litoral sotavento das ilhas...Depto. de GeografíaFac. de Geografía e HistoriaTRUEunpu

Proceedings of the 35th International Workshop on Statistical Modelling : July 20- 24, 2020 Bilbao, Basque Country, Spain

466 p.The InternationalWorkshop on Statistical Modelling (IWSM) is a reference workshop in promoting statistical modelling, applications of Statistics for researchers, academics and industrialist in a broad sense. Unfortunately, the global COVID-19 pandemic has not allowed holding the 35th edition of the IWSM in Bilbao in July 2020. Despite the situation and following the spirit of the Workshop and the Statistical Modelling Society, we are delighted to bring you the proceedings book of extended abstracts

LU(S)TI in the global South: an empirical analysis of land use and socio-economic transport interaction in Tanzania using mobile network data

The majority of rural-urban migration is filtered through slums: informally established, unplanned, and unrecognised by the government, scientists have a minimal understand- ing of the 200,000 that exist worldwide, never mind enough insight into the millions of individuals living there. This limited understanding often coincides with a more general absence of data in traditional urban planning approaches, leading to most cities seeing development, positive or otherwise, preceding planning. Wesolowski and Eagle (2010) highlighted the key need to use models of human mobility to help guide effective spatial planning policies. Previous research has shown that thinking about the built environment alone cannot account for individual differences in behaviour, and that we must also consider factors such as socio-economic circumstance and context (which are far more likely to contain explanatory value than the geographies of points of interest, such as home and work locations of individuals alone). However, this remains a very difficult topic to study. Emerging economies are often characterised by institutions struggling to keep even demographic data streams up to date. Combined with ineffective data collection strategies, it is often realistic to expect stakeholders to retain an overview of the dynamics of urban systems. This gap causes many issues, but particularly in East Africa: expense and logistics restrict the ability to deploy sensor technologies; fast-changing environments reduce the utility of traditional household and census surveying; and even when raw data exists there are distinct skill gaps for data analysis. To address this, this thesis extends nascent work, and systematically investigates the use of Call Detail Records (CDR) and Mobile Financial Service (MFS) transaction logs to model mobility, demographics, land use and their interplay. Data used was automatically generated as part of day-to-day operations of a major Tanzanian Mobile Network Operator. As part of this thesis, three empirical analyses are carried out to test the boundaries of inferring activity-based land use, predicting cell tower coverage level socio-economic levels and generating mobility metrics in the form of Origin-Destination matrices and synthetic daily activity plans for the Tanzanian port city of Dar Es Salaam. Further, shortcomings of CDR and MFS data, and ways to overcome these, are identified. Empirical chapters form the basis for the identification of factors from the spatial dimension focused on assessing the impact of the built environment, socio-economic circum- stance and mobility behaviour allowing for the extension of traditional land use-transport interaction (LUTI) models, through the inclusion of socio-economic characteristics. This culminates in a new empirical LU(S)TI analysis for a sub-Saharan context. The metropolitan area of the port city of Dar es Salaam, Tanzania, is a pertinent case study area as it is facing similar challenges to many other fast-growing metropolitan areas in emerging economies globally

Using hydrological models and digital soil mapping for the assessment and management of catchments: A case study of the Nyangores and Ruiru catchments in Kenya (East Africa)

Human activities on land have a direct and cumulative impact on water and other natural resources within a catchment. This land-use change can have hydrological consequences on the local and regional scales. Sound catchment assessment is not only critical to understanding processes and functions but also important in identifying priority management areas. The overarching goal of this doctoral thesis was to design a methodological framework for catchment assessment (dependent upon data availability) and propose practical catchment management strategies for sustainable water resources management. The Nyangores and Ruiru reservoir catchments located in Kenya, East Africa were used as case studies. A properly calibrated Soil and Water Assessment Tool (SWAT) hydrologic model coupled with a generic land-use optimization tool (Constrained Multi-Objective Optimization of Land-use Allocation-CoMOLA) was applied to identify and quantify functional trade-offs between environmental sustainability and food production in the ‘data-available’ Nyangores catchment. This was determined using a four-dimension objective function defined as (i) minimizing sediment load, (ii) maximizing stream low flow and (iii and iv) maximizing the crop yields of maize and soybeans, respectively. Additionally, three different optimization scenarios, represented as i.) agroforestry (Scenario 1), ii.) agroforestry + conservation agriculture (Scenario 2) and iii.) conservation agriculture (Scenario 3), were compared. For the data-scarce Ruiru reservoir catchment, alternative methods using digital soil mapping of soil erosion proxies (aggregate stability using Mean Weight Diameter) and spatial-temporal soil loss analysis using empirical models (the Revised Universal Soil Loss Equation-RUSLE) were used. The lack of adequate data necessitated a data-collection phase which implemented the conditional Latin Hypercube Sampling. This sampling technique reduced the need for intensive soil sampling while still capturing spatial variability. The results revealed that for the Nyangores catchment, adoption of both agroforestry and conservation agriculture (Scenario 2) led to the smallest trade-off amongst the different objectives i.e. a 3.6% change in forests combined with 35% change in conservation agriculture resulted in the largest reduction in sediment loads (78%), increased low flow (+14%) and only slightly decreased crop yields (3.8% for both maize and soybeans). Therefore, the advanced use of hydrologic models with optimization tools allows for the simultaneous assessment of different outputs/objectives and is ideal for areas with adequate data to properly calibrate the model. For the Ruiru reservoir catchment, digital soil mapping (DSM) of aggregate stability revealed that susceptibility to erosion exists for cropland (food crops), tea and roadsides, which are mainly located in the eastern part of the catchment, as well as deforested areas on the western side. This validated that with limited soil samples and the use of computing power, machine learning and freely available covariates, DSM can effectively be applied in data-scarce areas. Moreover, uncertainty in the predictions can be incorporated using prediction intervals. The spatial-temporal analysis exhibited that bare land (which has the lowest areal proportion) was the largest contributor to erosion. Two peak soil loss periods corresponding to the two rainy periods of March–May and October–December were identified. Thus, yearly soil erosion risk maps misrepresent the true dimensions of soil loss with averages disguising areas of low and high potential. Also, a small portion of the catchment can be responsible for a large proportion of the total erosion. For both catchments, agroforestry (combining both the use of trees and conservation farming) is the most feasible catchment management strategy (CMS) for solving the major water quantity and quality problems. Finally, the key to thriving catchments aiming at both sustainability and resilience requires urgent collaborative action by all stakeholders. The necessary stakeholders in both Nyangores and Ruiru reservoir catchments must be involved in catchment assessment in order to identify the catchment problems, mitigation strategies/roles and responsibilities while keeping in mind that some risks need to be shared and negotiated, but so will the benefits.:TABLE OF CONTENTS DECLARATION OF CONFORMITY........................................................................ i DECLARATION OF INDEPENDENT WORK AND CONSENT ............................. ii LIST OF PAPERS ................................................................................................. iii ACKNOWLEDGEMENTS ..................................................................................... iv THESIS AT A GLANCE ......................................................................................... v SUMMARY ............................................................................................................ vi List of Figures......................................................................................................... x List of Tables........................................................................................................... x ABBREVIATION..................................................................................................... xi PART A: SYNTHESIS 1. INTRODUCTION ............................................................................................... 1 1.1 Catchment management ...................................................................................1 1.2 Tools to support catchment assessment and management ..............................4 1.3 Catchment management strategies (CMSs)......................................................9 1.4 Concept and research objectives.......................................................................11 2. MATERIAL AND METHODS................................................................................15 2.1. STUDY AREA ..................................................................................................15 2.1.1. Nyangores catchment ...................................................................................15 2.1.2. Ruiru reservoir catchment .............................................................................17 2.2. Using SWAT conceptual model and land-use optimization ..............................19 2.3. Using soil erosion proxies and empirical models ..............................................21 3. RESULTS AND DISCUSSION..............................................................................24 3.1. Assessing multi-metric calibration performance using the SWAT model...........25 3.2. Land-use optimization using SWAT-CoMOLA for the Nyangores catchment. ..26 3.3. Digital soil mapping of soil aggregate stability ..................................................28 3.4. Spatio-temporal analysis using the revised universal soil loss equation (RUSLE) 29 4. CRITICAL ASSESSMENT OF THE METHODS USED ......................................31 4.1. Assessing suitability of data for modelling and overcoming data challenges...31 4.2. Selecting catchment management strategies based on catchment assessment . 35 5. CONCLUSION AND RECOMMENDATIONS ....................................................36 6. REFERENCES ............................ .....................................................................38 PART B: PAPERS PAPER I .................................................................................................................47 PAPER II ................................................................................................................59 PAPER III ...............................................................................................................74 PAPER IV ...............................................................................................................8