The big brother : monitoring urban growth and change for sustainable infrastructure provision

Monitoring urban growth and land-use change is an important issue for sustainable infrastructure planning. Rapid urban development, sprawl and increasing population pressure, particularly in developing nations, are resulting in deterioration of infrastructure facilities, loss of productive agricultural lands and open spaces, pollution, health hazards and micro-climatic changes. In addressing these issues effectively, it is crucial to collect up-to-date and accurate data and monitor the changing environment at regular intervals. This chapter discusses the role of geospatial technologies for mapping and monitoring the changing environment and urban structure, where such technologies are highly useful for sustainable infrastructure planning and provision.

Motivation, development and validation of a new spectral greenness index : a spectral dimension related to foliage projective cover

A method is presented for the development of a regional Landsat-5 Thematic Mapper (TM) and Landsat-7 Enhanced Thematic Mapper plus (ETM+) spectral greenness index, coherent with a six-dimensional index set, based on a single ETM+ spectral image of a reference landscape. The first three indices of the set are determined by a polar transformation of the first three principal components of the reference image and relate to scene brightness, percent foliage projective cover (FPC) and water related features. The remaining three principal components, of diminishing significance with respect to the reference image, complete the set. The reference landscape, a 2200 km2 area containing a mix of cattle pasture, native woodland and forest, is located near Injune in South East Queensland, Australia. The indices developed from the reference image were tested using TM spectral images from 19 regionally dispersed areas in Queensland, representative of dissimilar landscapes containing woody vegetation ranging from tall closed forest to low open woodland. Examples of image transformations and two-dimensional feature space plots are used to demonstrate image interpretations related to the first three indices. Coherent, sensible, interpretations of landscape features in images composed of the first three indices can be made in terms of brightness (red), foliage cover (green) and water (blue). A limited comparison is made with similar existing indices. The proposed greenness index was found to be very strongly related to FPC and insensitive to smoke. A novel Bayesian, bounded space, modelling method, was used to validate the greenness index as a good predictor of FPC. Airborne LiDAR (Light Detection and Ranging) estimates of FPC along transects of the 19 sites provided the training and validation data. Other spectral indices from the set were found to be useful as model covariates that could improve FPC predictions. They act to adjust the greenness/FPC relationship to suit different spectral backgrounds. The inclusion of an external meteorological covariate showed that further improvements to regional-scale predictions of FPC could be gained over those based on spectral indices alone.

Land use effects on soil carbon fractions in the southeastern United States. II. changes in soil carbon fractions along a forest to pasture chronosequence

Since land use change can have significant impacts on regional biogeochemistry, we investigated how conversion of forest and cultivation to pasture impact soil C and N cycling. In addition to examining total soil C, we isolated soil physiochemical C fractions in order to understand the mechanisms by which soil C is sequestered or lost. Total soil C did not change significantly over time following conversion from forest, though coarse (250-2,000 mum) particulate organic matter C increased by a factor of 6 immediately after conversion. Aggregate mean weight diameter was reduced by about 50% after conversion, but values were like those under forest after 8 years under pasture. Samples collected from a long-term pasture that was converted from annual cultivation more than 50 years ago revealed that some soil physical properties negatively impacted by cultivation were very slow to recover. Finally, our results indicate that soil macroaggregates turn over more rapidly under pasture than under forest and are less efficient at stabilizing soil C, whereas microaggregates from pasture soils stabilize a larger concentration of C than forest microaggregates. Since conversion from forest to pasture has a minimal impact on total soil C content in the Piedmont region of Virginia, United States, a simple C stock accounting system could use the same base soil C stock value for either type of land use. However, since the effects of forest to pasture conversion are a function of grassland management following conversion, assessments of C sequestration rates require activity data on the extent of various grassland management practices.

Urban growth monitoring techniques for sustainable development

This paper discusses the role of advance techniques for monitoring urban growth and change for sustainable development of urban environment. It also presents results of a case study involving satellite data for land use/land cover classification of Lucknow city using IRS-1C multi-spectral features. Two classification algorithms have been used in the study. Experiments were conducted to see the level of improvement in digital classification of urban environment using Artificial Neural Network (ANN) technique.

Abundance of Natural Riparian Forests and Tree Plantations in the Amudarya Delta of Uzbekistan and their impact on emissions of soil-borne greenhouse gases

Through a forest inventory in parts of the Amudarya river delta, Central Asia, we assessed the impact of ongoing forest degradation on the emissions of greenhouse gases (GHG) from soils. Interpretation of aerial photographs from 2001, combined with data on forest inventory in 1990 and field survey in 2003 provided comprehensive information about the extent and changes of the natural tugai riparian forests and tree plantations in the delta. The findings show an average annual deforestation rate of almost 1.3% and an even higher rate of land use change from tugai forests to land with only sparse tree cover. These annual rates of deforestation and forest degradation are higher than the global annual forest loss. By 2003, the tugai forest area had drastically decreased to about 60% compared to an inventory in 1990. Significant differences in soil GHG emissions between forest and agricultural land use underscore the impact of the ongoing land use change on the emission of soil-borne GHGs. The conversion of tugai forests into irrigated croplands will release 2.5 t CO2 equivalents per hectare per year due to elevated emissions of N2O and CH4. This demonstrates that the ongoing transformation of tugai forests into agricultural land-use systems did not only lead to a loss of biodiversity and of a unique ecosystem, but substantially impacts the biosphere-atmosphere exchange of GHG and soil C and N turnover processes.

Methane and nitrous oxide fluxes in annual and perennial land-use systems of the irrigated areas in the Aral Sea Basin

Land use and agricultural practices can result in important contributions to the global source strength of atmospheric nitrous oxide (N2O) and methane (CH4). However, knowledge of gas flux from irrigated agriculture is very limited. From April 2005 to October 2006, a study was conducted in the Aral Sea Basin, Uzbekistan, to quantify and compare emissions of N2O and CH4 in various annual and perennial land-use systems: irrigated cotton, winter wheat and rice crops, a poplar plantation and a natural Tugai (floodplain) forest. In the annual systems, average N2O emissions ranged from 10 to 150 μg N2O-N m−2 h−1 with highest N2O emissions in the cotton fields, covering a similar range of previous studies from irrigated cropping systems. Emission factors (uncorrected for background emission), used to determine the fertilizer-induced N2O emission as a percentage of N fertilizer applied, ranged from 0.2% to 2.6%. Seasonal variations in N2O emissions were principally controlled by fertilization and irrigation management. Pulses of N2O emissions occurred after concomitant N-fertilizer application and irrigation. The unfertilized poplar plantation showed high N2O emissions over the entire study period (30 μg N2O-N m−2 h−1), whereas only negligible fluxes of N2O (<2 μg N2O-N m−2 h−1) occurred in the Tugai. Significant CH4 fluxes only were determined from the flooded rice field: Fluxes were low with mean flux rates of 32 mg CH4 m−2 day−1 and a low seasonal total of 35.2 kg CH4 ha−1. The global warming potential (GWP) of the N2O and CH4 fluxes was highest under rice and cotton, with seasonal changes between 500 and 3000 kg CO2 eq. ha−1. The biennial cotton–wheat–rice crop rotation commonly practiced in the region would average a GWP of 2500 kg CO2 eq. ha−1 yr−1. The analyses point out opportunities for reducing the GWP of these irrigated agricultural systems by (i) optimization of fertilization and irrigation practices and (ii) conversion of annual cropping systems into perennial forest plantations, especially on less profitable, marginal lands.

Did Brisbane grow smartly? Drivers of city growth 1991-2001 and lessons for current policies

Urban areas are growing unsustainably around the world; however, the growth patterns and their associated drivers vary between contexts. As a result, research has highlighted the need to adopt case study based approaches to stimulate the development of new theoretic understandings. Using land-cover data sets derived from Landsat images (30 m × 30 m), this research identifies both patterns and drivers of urban growth in a period (1991-2001) when a number of policy acts were enacted aimed at fostering smart growth in Brisbane, Australia. A linear multiple regression model was estimated using the proportion of lands that were converted from non-built-up (1991) to built-up usage (2001) within a suburb as a dependent variable to identify significant drivers of land-cover changes. In addition, the hot spot analysis was conducted to identify spatial biases of land-cover changes, if any. Results show that the built-up areas increased by 1.34% every year. About 19.56% of the non-built-up lands in 1991 were converted into built-up lands in 2001. This conversion pattern was significantly biased in the northernmost and southernmost suburbs in the city. This is due to the fact that, as evident from the regression analysis, these suburbs experienced a higher rate of population growth, and had the availability of habitable green field sites in relatively flat lands. The above findings suggest that the policy interventions undertaken between the periods were not as effective in promoting sustainable changes in the environment as they were aimed for.

Extinction risk in cloud forest fragments under climate change and habitat loss

Aim: To quantify the consequences of major threats to biodiversity, such as climate and land-use change, it is important to use explicit measures of species persistence, such as extinction risk. The extinction risk of metapopulations can be approximated through simple models, providing a regional snapshot of the extinction probability of a species. We evaluated the extinction risk of three species under different climate change scenarios in three different regions of the Mexican cloud forest, a highly fragmented habitat that is particularly vulnerable to climate change. Location: Cloud forests in Mexico. Methods: Using Maxent, we estimated the potential distribution of cloud forest for three different time horizons (2030, 2050 and 2080) and their overlap with protected areas. Then, we calculated the extinction risk of three contrasting vertebrate species for two scenarios: (1) climate change only (all suitable areas of cloud forest through time) and (2) climate and land-use change (only suitable areas within a currently protected area), using an explicit patch-occupancy approximation model and calculating the joint probability of all populations becoming extinct when the number of remaining patches was less than five. Results: Our results show that the extent of environmentally suitable areas for cloud forest in Mexico will sharply decline in the next 70 years. We discovered that if all habitat outside protected areas is transformed, then only species with small area requirements are likely to persist. With habitat loss through climate change only, high dispersal rates are sufficient for persistence, but this requires protection of all remaining cloud forest areas. Main conclusions: Even if high dispersal rates mitigate the extinction risk of species due to climate change, the synergistic impacts of changing climate and land use further threaten the persistence of species with higher area requirements. Our approach for assessing the impacts of threats on biodiversity is particularly useful when there is little time or data for detailed population viability analyses. © 2013 John Wiley & Sons Ltd.

Climate change and forests in India

Forests play a critical role in addressing climate change concerns in the broader context of global change and sustainable development. Forests are linked to climate change in three ways. i) Forests are a source of greenhouse gas (GHG) emissions: ii) Forests offer mitigation opportunities to stabilise GHG concentrations: iii) Forests are impacted by climate change. This paper reviews studies related to climate change and forests in India: first, the studies estimating carbon inventory for the Indian land use change and forestry sector (LUCF), then the different models and mitigation potential estimates for the LUCF sector in India. Finally it reviews the studies on the impact of climate change on forest ecosystems in India, identifying the implications for net primary productivity and bio-diversity. The paper highlights data, modelling and research gaps relevant to the GHG inventory, mitigation potential and vulnerability and impact assessments for the forest sector in India.

VegMachine - putting pastoralists in the picture

Australia’s rangelands are the extensive arid and semi-arid grazing lands that cover approximately 70% of the Australian continent. They are characterised by low and generally variable rainfall, low productivity and a sparse population. They support a number of industries including mining and tourism, but pastoralism is the primary land use. In some areas, the rangelands have a history of biological decline (Noble 1997), with erosion, loss of perennial native grasses and incursion of woody vegetation commonly reported in the scientific and lay literature. Despite our historic awareness of these trends, the establishment of systems to measure and monitor degradation, has presented numerous problems. The size and accessibility of Australia’s rangeland often mitigates development of extensive monitoring programs. So, too, securing on-going commitment from Government agencies to fund rangeland monitoring activities have led to either abandonment or a scaled-down approach in some instances (Graetz et al. 1986; Holm 1993). While a multiplicity of monitoring schemes have been developed for landholders at the property scale, and some have received promising initial uptake, relatively few have been maintained for more than a few years on any property without at least some agency support (Pickup et al. 1998). But, ironically, such property level monitoring tools can contribute significantly to local decisions about stock, infrastructure and sustainability. Research in recent decades has shown the value of satellites for monitoring change in rangelands (Wallace et al. 2004), especially in terms of tree and ground cover. While steadily improving, use of satellite data as a monitoring tool has been limited by the cost of the imagery, and the equipment and expertise needed to extract useful information from it. A project now under way in the northern rangelands of Australia is attempting to circumvent many of the problems through a monitoring system that allows property managers to use long-term satellite image sequences to quickly and inexpensively track changes in land cover on their properties

Burnt area mapping in insular Southeast Asia using medium resolution satellite imagery

Burnt area mapping in humid tropical insular Southeast Asia using medium resolution (250-500m) satellite imagery is characterized by persisting cloud cover, wide range of land cover types, vast amount of wetland areas and highly varying fire regimes. The objective of this study was to deepen understanding of three major aspects affecting the implementation and limits of medium resolution burnt area mapping in insular Southeast Asia: 1) fire-induced spectral changes, 2) most suitable multitemporal compositing methods and 3) burn scars patterns and size distribution. The results revealed a high variation in fire-induced spectral changes depending on the pre-fire greenness of burnt area. It was concluded that this variation needs to be taken into account in change detection based burnt area mapping algorithms in order to maximize the potential of medium resolution satellite data. Minimum near infrared (MODIS band 2, 0.86μm) compositing method was found to be the most suitable for burnt area mapping purposes using Moderate Resolution Imaging Spectroradiometer (MODIS) data. In general, medium resolution burnt area mapping was found to be usable in the wetlands of insular Southeast Asia, whereas in other areas the usability was seriously jeopardized by the small size of burn scars. The suitability of medium resolution data for burnt area mapping in wetlands is important since recently Southeast Asian wetlands have become a major point of interest in many fields of science due to yearly occurring wild fires that not only degrade these unique ecosystems but also create regional haze problem and release globally significant amounts of carbon into the atmosphere due to burning peat. Finally, super-resolution MODIS images were tested but the test failed to improve the detection of small scars. Therefore, super-resolution technique was not considered to be applicable to regional level burnt area mapping in insular Southeast Asia.

Livelihood, land use and environment interactions in the highlands of East Africa

This study aims at improving understanding of the interactions of livelihoods and the environment focusing on both socio-economic and biodiversity implications of land use change in the context of population pressure, global and local markets, climate change, cultural and regional historical factors in the highlands of East Africa. The study is based on three components (1) two extensive livelihood surveys, one on Mt. Kilimanjaro in Tanzania and the other in the Taita Hills of Kenya, (2) a land use change study of the southern slopes of Mt. Kilimanjaro focusing on land use trends between 1960s and 1980s and 1980s and 2000 and (3) a bird diversity study focusing on the potential impacts of the future land use change on birds in the main land use types on the slopes and the adjacent plains of Mt. Kilimanjaro. In addition, information on the highlands in Embu and the adjacent lowlands in Mbeere of Kenya are added to the discussion. Some general patterns of livelihood, land use and environment interactions can be found in the three sites. However, the linkages are very complex. Various external factors at different times in history have influenced most of the major turning points. Farmers continually make small adaptations to their farming practices, but the locally conceived alternatives are too few. Farmers lack specific information and knowledge on the most suitable crops, market opportunities and the quality requirements for growing the crops for markets. Population growth emerges as the most forceful driver of land use and environmental change. The higher altitudes have become extremely crowded with population densities in some areas higher than typical urban population densities. Natural vegetation has almost totally been replaced by farmland. Decreasing farm size due to population pressure is currently threatening the viability of whole farming systems. In addition, capital-poor intensification has lead to soil fertility depletion. Agricultural expansion to the agriculturally marginal lowlands has created a new and distinct group of farmers struggling constantly with climate variability causing frequent crop failures. Extensification to the fragile drylands is the major cause of fragmentation and loss of wildlife habitat. The linkages between livelihoods, land use and the environment generally point to degradation of the environment leading to reduced environmental services and ecosystem functions. There is no indication that the system is self-regulating in this respect. Positive interventions will be needed to maintain ecosystem integrity.

Geospatial environmental data modelling applications using remote sensing, GIS and spatial statistics

This thesis presents novel modelling applications for environmental geospatial data using remote sensing, GIS and statistical modelling techniques. The studied themes can be classified into four main themes: (i) to develop advanced geospatial databases. Paper (I) demonstrates the creation of a geospatial database for the Glanville fritillary butterfly (Melitaea cinxia) in the Åland Islands, south-western Finland; (ii) to analyse species diversity and distribution using GIS techniques. Paper (II) presents a diversity and geographical distribution analysis for Scopulini moths at a world-wide scale; (iii) to study spatiotemporal forest cover change. Paper (III) presents a study of exotic and indigenous tree cover change detection in Taita Hills Kenya using airborne imagery and GIS analysis techniques; (iv) to explore predictive modelling techniques using geospatial data. In Paper (IV) human population occurrence and abundance in the Taita Hills highlands was predicted using the generalized additive modelling (GAM) technique. Paper (V) presents techniques to enhance fire prediction and burned area estimation at a regional scale in East Caprivi Namibia. Paper (VI) compares eight state-of-the-art predictive modelling methods to improve fire prediction, burned area estimation and fire risk mapping in East Caprivi Namibia. The results in Paper (I) showed that geospatial data can be managed effectively using advanced relational database management systems. Metapopulation data for Melitaea cinxia butterfly was successfully combined with GPS-delimited habitat patch information and climatic data. Using the geospatial database, spatial analyses were successfully conducted at habitat patch level or at more coarse analysis scales. Moreover, this study showed it appears evident that at a large-scale spatially correlated weather conditions are one of the primary causes of spatially correlated changes in Melitaea cinxia population sizes. In Paper (II) spatiotemporal characteristics of Socupulini moths description, diversity and distribution were analysed at a world-wide scale and for the first time GIS techniques were used for Scopulini moth geographical distribution analysis. This study revealed that Scopulini moths have a cosmopolitan distribution. The majority of the species have been described from the low latitudes, sub-Saharan Africa being the hot spot of species diversity. However, the taxonomical effort has been uneven among biogeographical regions. Paper III showed that forest cover change can be analysed in great detail using modern airborne imagery techniques and historical aerial photographs. However, when spatiotemporal forest cover change is studied care has to be taken in co-registration and image interpretation when historical black and white aerial photography is used. In Paper (IV) human population distribution and abundance could be modelled with fairly good results using geospatial predictors and non-Gaussian predictive modelling techniques. Moreover, land cover layer is not necessary needed as a predictor because first and second-order image texture measurements derived from satellite imagery had more power to explain the variation in dwelling unit occurrence and abundance. Paper V showed that generalized linear model (GLM) is a suitable technique for fire occurrence prediction and for burned area estimation. GLM based burned area estimations were found to be more superior than the existing MODIS burned area product (MCD45A1). However, spatial autocorrelation of fires has to be taken into account when using the GLM technique for fire occurrence prediction. Paper VI showed that novel statistical predictive modelling techniques can be used to improve fire prediction, burned area estimation and fire risk mapping at a regional scale. However, some noticeable variation between different predictive modelling techniques for fire occurrence prediction and burned area estimation existed.

Enhanced processing of SPOT multispectral satellite imagery for environmental monitoring and modelling

The Taita Hills in southeastern Kenya form the northernmost part of Africa’s Eastern Arc Mountains, which have been identified by Conservation International as one of the top ten biodiversity hotspots on Earth. As with many areas of the developing world, over recent decades the Taita Hills have experienced significant population growth leading to associated major changes in land use and land cover (LULC), as well as escalating land degradation, particularly soil erosion. Multi-temporal medium resolution multispectral optical satellite data, such as imagery from the SPOT HRV, HRVIR, and HRG sensors, provides a valuable source of information for environmental monitoring and modelling at a landscape level at local and regional scales. However, utilization of multi-temporal SPOT data in quantitative remote sensing studies requires the removal of atmospheric effects and the derivation of surface reflectance factor. Furthermore, for areas of rugged terrain, such as the Taita Hills, topographic correction is necessary to derive comparable reflectance throughout a SPOT scene. Reliable monitoring of LULC change over time and modelling of land degradation and human population distribution and abundance are of crucial importance to sustainable development, natural resource management, biodiversity conservation, and understanding and mitigating climate change and its impacts. The main purpose of this thesis was to develop and validate enhanced processing of SPOT satellite imagery for use in environmental monitoring and modelling at a landscape level, in regions of the developing world with limited ancillary data availability. The Taita Hills formed the application study site, whilst the Helsinki metropolitan region was used as a control site for validation and assessment of the applied atmospheric correction techniques, where multiangular reflectance field measurements were taken and where horizontal visibility meteorological data concurrent with image acquisition were available. The proposed historical empirical line method (HELM) for absolute atmospheric correction was found to be the only applied technique that could derive surface reflectance factor within an RMSE of < 0.02 ps in the SPOT visible and near-infrared bands; an accuracy level identified as a benchmark for successful atmospheric correction. A multi-scale segmentation/object relationship modelling (MSS/ORM) approach was applied to map LULC in the Taita Hills from the multi-temporal SPOT imagery. This object-based procedure was shown to derive significant improvements over a uni-scale maximum-likelihood technique. The derived LULC data was used in combination with low cost GIS geospatial layers describing elevation, rainfall and soil type, to model degradation in the Taita Hills in the form of potential soil loss, utilizing the simple universal soil loss equation (USLE). Furthermore, human population distribution and abundance were modelled with satisfactory results using only SPOT and GIS derived data and non-Gaussian predictive modelling techniques. The SPOT derived LULC data was found to be unnecessary as a predictor because the first and second order image texture measurements had greater power to explain variation in dwelling unit occurrence and abundance. The ability of the procedures to be implemented locally in the developing world using low-cost or freely available data and software was considered. The techniques discussed in this thesis are considered equally applicable to other medium- and high-resolution optical satellite imagery, as well the utilized SPOT data.

Establishing turf grass increases soil greenhouse gas emissions in peri-urban environments

Urbanization is becoming increasingly important in terms of climate change and ecosystem functionality worldwide. We are only beginning to understand how the processes of urbanization influence ecosystem dynamics and how peri-urban environments contribute to climate change. Brisbane in South East Queensland (SEQ) currently has the most extensive urban sprawl of all Australian cities. This leads to substantial land use changes in urban and peri-urban environments and the subsequent gaseous emissions from soils are to date neglected for IPCC climate change estimations. This research examines how land use change effects methane (CH4) and nitrous oxide (N2O) fluxes from peri-urban soils and consequently influences the Global Warming Potential (GWP) of rural ecosystems in agricultural use undergoing urbanization. Therefore, manual and fully automated static chamber measurements determined soil gas fluxes over a full year and an intensive sampling campaign of 80 days after land use change. Turf grass, as the major peri-urban land cover, increased the GWP by 415 kg CO2-e ha 1 over the first 80 days after conversion from a well-established pasture. This results principally from increased daily average N2O emissions of 0.5 g N2O ha-1 d-1 from the pasture to 18.3 g N2O ha-1 d-1 from the turf grass due to fertilizer application during conversion. Compared to the native dry sclerophyll eucalypt forest, turf grass establishment increases the GWP by another 30 kg CO2-e ha 1. The results presented in this study clearly indicate the substantial impact of urbanization on soil-atmosphere gas exchange in form of non-CO2 greenhouse gas emissions particularly after turf grass establishment.