Dynamics of Land Use and Land Cover Changes in Harare, Zimbabwe: A Case Study on the Linkage between Drivers and the Axis of Urban Expansion

With increasing population growth, the Harare Metropolitan Province has experienced accelerated land use and land cover (LULC) changes, influencing the city’s growth. This study aims to assess spatiotemporal urban LULC changes, the axis, and patterns of growth as well as drivers influencing urban growth over the past three decades in the Harare Metropolitan Province. The analysis was based on remotely sensed Landsat Thematic Mapper and Operational Land Imager data from 1984–2018, GIS application, and binary logistic regression. Supervised image classification using support vector machines was performed on Landsat 5 TM and Landsat 8 OLI data combined with the soil adjusted vegetation index, enhanced built-up and bareness index and modified difference water index. Statistical modelling was performed using binary logistic regression to identify the influence of the slope and the distance proximity characters as independent variables on urban growth. The overall mapping accuracy for all time periods was over 85%. Built-up areas extended from 279.5 km2 (1984) to 445 km2 (2018) with high-density residential areas growing dramatically from 51.2 km2 (1984) to 218.4 km2 (2018). The results suggest that urban growth was influenced mainly by the presence and density of road networks

Valuing Natural Space and Landscape Fragmentation in Richmond, VA

Hedonic pricing methods and GIS (Geographic Information Systems) were used to evaluate relationships between sale price of single family homes and landscape fragmentation and natural land cover. Spatial regression analyses found that sale prices increase as landscapes become less fragmented and the amount of natural land cover around a home increases. The projected growth in population and employment in the Richmond, Virginia region and subsequent increases in land development and landscape fragmentation presents a challenge to sustaining intact healthy ecosystems in the Richmond region. Spatial regression analyses helped illuminate how land cover patterns influence sale prices and landscape patterns that are economically and ecologically advantageous

Monitoring urban growth and land use land cover change in Al Ain, UAE using remote sensing and GIS techniques

Urbanization and industrialization cause a serious land degradation problem, including an increased pressure on natural resources such as deforestation, rise in temperature and management of water resources. The Urban Heat Island (UHI) effects of urbanization are widely acknowledged. Increase of impervious surface is a surrogate measure of urbanization and their effects on local hydrology is well reported in literature. This study investigates the spatial-temporal dynamics of land use and land cover changes in Al Ain, UAE, from 2006 to 2016. The Landsat images of two different periods, i.e., Landsat ETM of 2006 and Landsat 8 for 2016 were acquired from earth explorer site. Semi-supervised known as the hybrid classification method was used for image classification. The change detection was carried out through post-classification techniques. The study area was categorized into five major classes. These are agriculture, gardens, urban, sandy areas and mixed urban/sandy areas. It was observed that agricultural and urban land increases from 42,560 ha to 45,950 ha (8%) and 8150 ha to 9105 ha (12%), respectively. Consequently, the natural sandy area was reduced. It was also found that the urban area was expanded dramatically in the west and southwest directions. The outcomes of this study would help concerning authorities for a sustainable land and water resources management in the Al Ain region

Evolution of small reservoirs in Burkina Faso

Small reservoirs (SRs) are important infrastructures for providing water for a wide range of activities in Burkina Faso and other semiarid environments. In recent years, SRs have become even more important, considering the effects of climate change and variability such as erratic rainfall patterns, recurrent droughts and floods, delays in the onset of the rains (Laux et al. 2008), increased incidence of in-season dry spells (Lacombe et al. 2012), and high evapotranspiration rates. SRs provide vulnerable rural communities with water for multiple purposes, including domestic and agricultural uses (McCartney et al. 2012; Venot et al. 2012). However, a number of external factors are negatively influencing the sustainable uses of SRs. Rapid population growth (Zuberi and Thomas 2012) and its attendant human-induced activities are a threat to the quality of water in SRs, as are agricultural extensification and intensification around SRs, including the increased use of inorganic fertilizers

A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional Neural Networks

Analyzing land use and land cover (LULC) using remote sensing (RS) imagery is essential for many environmental and social applications. The increase in availability of RS data has led to the development of new techniques for digital pattern classification. Very recently, deep learning (DL) models have emerged as a powerful solution to approach many machine learning (ML) problems. In particular, convolutional neural networks (CNNs) are currently the state of the art for many image classification tasks. While there exist several promising proposals on the application of CNNs to LULC classification, the validation framework proposed for the comparison of different methods could be improved with the use of a standard validation procedure for ML based on cross-validation and its subsequent statistical analysis. In this paper, we propose a general CNN, with a fixed architecture and parametrization, to achieve high accuracy on LULC classification over RS data from different sources such as radar and hyperspectral. We also present a methodology to perform a rigorous experimental comparison between our proposed DL method and other ML algorithms such as support vector machines, random forests, and k-nearest-neighbors. The analysis carried out demonstrates that the CNN outperforms the rest of techniques, achieving a high level of performance for all the datasets studied, regardless of their different characteristics.Ministerio de Economía y Competitividad TIN2014-55894-C2-1-RMinisterio de Economía y Competitividad TIN2017-88209-C2-2-

Effects of a large irrigation reservoir on aquatic and riparian plants: a history of survival and loss

Dammed rivers have unnatural stream flows, disrupted sediment dynamics, and rearranged geomorphologic settings. Consequently, fluvial biota experiences disturbed functioning in the novel ecosystems. The case study is the large irrigation reservoir Alqueva in Guadiana River, Southern Iberia. The study area was divided into three zones: upstream and downstream of the dam and reservoir. For each zone, species composition and land use and land cover (LULC) were compared before and after the Alqueva Dam implementation. Data consist of aquatic and riparian flora composition obtained from 46 surveys and the area (%) of 12 classes of LULC obtained in 90 riverine sampling units through the analysis of historical and contemporary imagery. There was an overall decrease of several endemic species and on the riparian shrublands and aquatic stands, although di erences in the proportion of functional groups were not significant. Nevertheless, compositional diversity shows a significant decline in the upstream zone while landscape diversity shows an accentuated reduction in the reservoir area and downstream of the dam, which is likely related to the loss of the rocky habitats of the ‘old’ Guadiana River and the homogenization of the riverscape due to the irrigation intensification. The mitigation of these critical changes should be site-specific and should rely on the knowledge of the interactions between surrounding lands, ecological, biogeomorphologic, and hydrological components of the fluvial ecosystemsinfo:eu-repo/semantics/publishedVersio

Spatiotemporal analyses of soil moisture from point to footprint scale in two different hydroclimatic regions

This paper presents time stability analyses of soil moisture at different spatial measurement support scales (point scale and airborne remote sensing (RS) footprint scale 800 m × 800 m) in two different hydroclimatic regions. The data used in the analyses consist of in situ and passive microwave remotely sensed soil moisture data from the Southern Great Plains Hydrology Experiments 1997 and 1999 (SGP97 and SGP99) conducted in the Little Washita (LW) watershed, Oklahoma, and the Soil Moisture Experiments 2002 and 2005 (SMEX02 and SMEX05) in the Walnut Creek (WC) watershed, Iowa. Results show that in both the regions soil properties (i.e., percent silt, percent sand, and soil texture) and topography (elevation and slope) are significant physical controls jointly affecting the spatiotemporal evolution and time stability of soil moisture at both point and footprint scales. In Iowa, using point‐scale soil moisture measurements, the WC11 field was found to be more time stable (TS) than the WC12 field. The common TS points using data across the 3 year period (2002–2005) were mostly located at moderate to high elevations in both the fields. Furthermore, the soil texture at these locations consists of either loam or clay loam soil. Drainage features and cropping practices also affected the field‐scale soil moisture variability in the WC fields. In Oklahoma, the field having a flat topography (LW21) showed the worst TS features compared to the fields having gently rolling topography (LW03 and LW13). The LW13 field (silt loam) exhibited better time stability than the LW03 field (sandy loam) and the LW21 field (silt loam). At the RS footprint scale, in Iowa, the analysis of variance (ANOVA) tests show that the percent clay and percent sand are better able to discern the TS features of the footprints compared to the soil texture. The best soil indicator of soil moisture time stability is the loam soil texture. Furthermore, the hilltops (slope ∼0%–0.45%) exhibited the best TS characteristics in Iowa. On the other hand, in Oklahoma, ANOVA results show that the footprints with sandy loam and loam soil texture are better indicators of the time stability phenomena. In terms of the hillslope position, footprints with mild slope (0.93%–1.85%) are the best indicators of TS footprints. Also, at both point and footprint scales in both the regions, land use–land cover type does not influence soil moisture time stability

Factors limiting sand dune restoration in Northwest Beach, Point Pelee National Park, Canada

Known as home to rare species of flora and fauna, and their critical habitats, Northwest beach of Point Pelee National Park has undergone significant ecological and infrastructural changes in the past decades. A number of important management challenges have emerged, including conservation of endangered Five-lined Skink (Plestiodon fasciatus) which inhabit the extensive dune system within the park. This research investigates key factors for sand dune ecosystem restoration in Northwest beach of Point Pelee with particular attention to the conservation of Skink habitat. Random stratified sampling method was used to collect sand and vegetation samples from the disturbed and natural areas. Sand samples were also collected from the sand piles, which is a part of dune restoration process initiated by the Parks Canada. Three aspects were considered: grain size distribution of dune sediments, vegetation assemblage and character of the dune associated species, land use and land cover change. Grain size distribution indicated that samples from most of the sand piles contained some amounts of clay/silt and pebble sized grains making it unfavourable for wind action, resulting in no significant contribution to dune formation. Most of the sand samples collected along the foredunes and water edge were appropriate for sediment transport. Shannon and Simpson’s Diversity Index was calculated as 1.48 and 0.67 for natural area as compared to 0.71 and 0.35 for the disturbed area, which indicate unfavourable species diversity for dune restoration in disturbed areas. The research also focused on the spatial and temporal changes in land use and land cover in NW beach area of Point Pelee using aerial photos for 1959, 1977, 2006 and 2015. Different time series of the aerial photos were chosen based on their availability. The Ecological land classification system for Southern Ontario were used to classify the aerial photos for land use and land cover (LULC). LULC classes included Shoreline vegetation, Deciduous thicket, Sand Barren and Dune Type, and Infrastructures (includes Transportation and services) for the entire Northwest Beach area. Segmentation and classification tools was used to classify four different time series of aerial photos. Grain size distribution and vegetation assemblage for dune associated species were calculated to determine the factors limiting habitat restoration process. Based on the results alternate management strategies for dune restoration in Point Pelee were recommended. The study offers key insights on the importance of timely detection, analysis and visualisation of dynamic changes for habitat restoration and maintaining ecological integrity of the Northwest beach area of Point Pelee