Examining the satellite-detected urban land use spatial patterns using multidimensional fractal dimension indices

2013-2014 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

A multiple scale, function, and type approach to determine and improve Green Infrastructure of urban watersheds

Green Infrastructure (GI) connects different types of green features via various scales, thereby supporting urban biodiversity and service provision. This study presents a methodology capable of identifying multiple functions to assess GI in less-developed countries, where such methodologies are lacking. GI was assessed based on a high-resolution land use classification using both landscape metrics and spatial data within an urbanized region of San José, Costa Rica, at different scales (watershed, neighbourhood, object). Results showed highly fragmented green spaces (often <10 ha), typically unable to support high levels of biodiversity, along with a low amount of green space per inhabitant (<7.4 m²) within the watershed. Substantially higher tree cover (x6) and tree density (x5) were found in the greenest neighbourhood in comparison to the least green neighbourhood. Potential areas for new GI in the form of green roofs (4.03 ha), permeable pavement (27.3), and potential retention areas (85.3) were determined. Several green spaces (n = 11) were identified as promising GI sites with the potential to increase provision (18.6 m²/inhabitant). The adopted methodology demonstrates the potential of GI for increasing recreational green space access, runoff reduction, and flood retentions while supporting biodiversity, validating its utility in guiding decision-making and policy generation

Not just a pretty picture: Mapping Leaf Area Index at 10 m resolution using Sentinel-2

Achieving the Global Climate Observing System goal of 10 m resolution leaf area index (LAI) maps is critical for applications related to climate adaptation, sustainable agriculture, and ecosystem monitoring. Five strategies for producing 10 m LAI maps from Sentinel-2 (S2) imagery are evaluated: i. bi-cubic interpolation of 20 m resolution S2 LAI maps from the Simplified Level 2 Prototype Processor Version 1 (SL2PV1) as currently performed by the Sentinel Applications Platform (SNAP), ii. applying SL2PV1 to S2 reflectance bands spatially downscaled to 10 m using bi-cubic interpolation (BICUBIC), iii. Applying SL2PV1 to S2 reflectance bands spatially downscaled to 10 m using Area to Point Regression Kriging (ATPRK), iv. using a recalibrated version of SL2PV1 (SL2PV2) requiring only three S2 10m bands, and iv) a novel use of the previously developed Active Learning Regularization (ALR) approach to locally approximate the SL2PV1 algorithm using only 10 m bands. Algorithms were assessed in terms of per-pixel accuracy and spatial metrics when comparing 10 m LAI maps produced using either actual S2 imagery or S2 imagery synthesized from airborne hyperspectral imagery to reference 10 m LAI maps traceable to in-situ fiducial reference measurements at 10 sites across the continental US. ATPRK and ALR algorithms had the lowest precision error of ~0.15 LAI, compared to 0.19 LAI for SNAP and BICUBIC and 0.35 LAI for SL2PV2, and ranked highest in terms of local correlation and Structural Similarity Index measure as well as qualitative agreement with reference maps. SL2PV2 LAI showed evidence of saturation over forests related to decreased sensitivity of input visible reflectance. All algorithms had a similar uncertainty of ~0.55 LAI compared to traceable reference maps, due to the trade-off between bias and precision. However, ATPRK and ALR uncertainty reduced to 0.11 LAI and 0.16 LAI, respectively, when compared to reference maps that ignored canopy clumping. These results suggest that both ATPRK and ALR are suitable for producing 10 m S2 LAI maps assuming bias due to local clumping can be corrected in the underlying SL2PV1 algorithm

Developing a Biophilic City through Natural Land Transformation Analysis and Geodesign: The case of Purbachal New Town, Bangladesh

Dhaka, the capital city of Bangladesh is one of the fastest-growing metropolitan regions in the world. To solve the ever-increasing need for housing and to reduce the pressure of the population from the capital city, the Purbachal satellite city was planned. It is the biggest planned township in Bangladesh with an area of over 25 square kilometers. Purbachal is situated at the confluence of the Shitalakhya and Balu rivers. Historically a low-lying wetland, Purbachal has gone through a rapid transformation in past decades. The water bodies around the area have been filled in to create new developable land. Through remote sensing and GIS analysis, this study investigates the transformation of wetland areas in Purbachal New Town. The study Investigates whether the new developments in the Purbachal New Town followed a natural topography or it was drastically modified from its natural conditions. The study also investigates how these changes in the inherent topographical nature of the area can influence the future of the city. The goal of the study is to explore the complex interrelation of different factors responsible for the growth of a city. The main aim is to formulate a realistic city planning process to synthesize systems city approach with the concept of Biophilic design to create spaces where people will be able to live in harmony with nature

Optimizing Spatial Resolution of Imagery for Urban Form Detection—The Cases of France and Vietnam

The multitude of satellite data products available offers a large choice for urban studies. Urban space is known for its high heterogeneity in structure, shape and materials. To approach this heterogeneity, finding the optimal spatial resolution (OSR) is needed for urban form detection from remote sensing imagery. By applying the local variance method to our datasets (pan-sharpened images), we can identify OSR at two levels of observation: individual urban elements and urban districts in two agglomerations in West Europe (Strasbourg, France) and in Southeast Asia (Da Nang, Vietnam). The OSR corresponds to the minimal variance of largest number of spectral bands. We carry out three categories of interval values of spatial resolutions for identifying OSR: from 0.8 m to 3 m for isolated objects, from 6 m to 8 m for vegetation area and equal or higher than 20 m for urban district. At the urban district level, according to spatial patterns, form, size and material of elements, we propose the range of OSR between 30 m and 40 m for detecting administrative districts, new residential districts and residential discontinuous districts. The detection of industrial districts refers to a coarser OSR from 50 m to 60 m. The residential continuous dense districts effectively need a finer OSR of between 20 m and 30 m for their optimal identification. We also use fractal dimensions to identify the threshold of homogeneity/heterogeneity of urban structure at urban district level. It seems therefore that our approaches are robust and transferable to different urban contexts