Guide Remote Sensing Applications for the Urban Environment

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The correspondence between lighted area and urban land use can be quantified using Landsat imagery. Satellite image of Philadelphia, Pennsylvania. Urban areas generally have higher solar radiation absorption and a greater thermal capacity and conductivity because of being covered with buildings, roads and other impervious surfaces.

Urban areas tend to experience a relatively higher temperature compared to the surrounding rural areas. Higher urban temperatures generally result in higher ozone levels due to an increased ground-level ozone production. Higher urban temperatures also mean increased energy use, mostly due to a greater demand for air conditioning. As power plants burn more fossil fuels, they drive up both pollution level and energy costs.

To better understand the impact of urban development on land surface temperatures, we can observe the thermal signature of each land cover type and overlay a land surface temperature image with a land use and land cover map in the same year. An easy method to detect urban areas and boundaries is using satellite data: night-time lights is the only globally consistent source, though it is not without problems.

Natural Resource Management. Limitations of remote sensing in urban environments The application of remote sensing to specific problems has proceeded more rapidly in agriculture, geology, cartography, meteorology, and natural resource management than in urban analysis and research.

How can we recognise urban areas? City lights on the globe. This image was created from a mosaic of night-time images. These are discussed below:. Airborne remote sensing era: The airborne remote sensing era evolved during the first and the Second World War Avery and Berlin, , Colwell, During this time remote sensing was mainly used for the purposes of surveying, reconnaissance, mapping, and military surveillance.

Spy satellite remote sensing era: During the peak of the cold war, spy satellites such as Corona Dwayne et al. Data was collected, almost exclusively, for military purposes. The data was not digital, but was produced as hard copies. However, the spin-off of the remote sensing developed for military purposes during the above 3 eras spilled over to mapping and slowly into environmental and natural resources applications.

This was an era when data started being available in digital format and were analyzed using exclusive computer hardware and software. This was also an era when global coverage became realistic and environmental applications practical. The Landsat-6 failed during launch. These satellites have high resolution nominal 2. At this resolution, only Landsat is currently gathering data with global wall to wall coverage.

This is, by far, the most significant era that kick started truly wide environmental application of remote sensing data locally and globally. Applications of sensor data have become wide spread and applications have multiplied. Institutions and individuals who never used remote sensing have begun to take an interest in remote sensing. New Millennium era: The new millennium era Bailey et al. These are basically satellites and sensors for the next generation.

These include Earth Observing-1 carrying the first spaceborne hyperspectral data. Private industry era: The private industry era began at the end of the last millennium and beginning of this millennium see Stoney, This era consists of a number of innovations. Second, a revolutionary means of data collection. This is typified by Rapideye satellite constellation of 5 satellites, having almost daily coverage of any spot on earth at 6. Third, is the introduction of micro satellites, some under disaster monitoring constellation DMC , which are designed and launched by surrey satellite technology Ltd.

A state-of-art of satellite sensors widely used in environmental applications and natural resources management are given in Table 1. These sensors provide data in a wide range of scales or pixel resolutions , radiometry, band numbers, and band widths and provides distinct advantage of consistency of data, synoptic coverage, global reach, cost per unit area, repeatability, precision, and accuracy. Added to this is the long-time series of archives and pathfinder datasets e.

Much of this data is also free and accessible online. Many applications e. This can be maximized by using data from multiple sensors Table 1. However, since data from these sensors are acquired in multiple resolution spatial, spectral, radiometric , multiple bandwidth, and in varying conditions, they need to be harmonized and synthesized before being used Thenkabail et al. This will help normalize for sensor characteristics such as pixel sizes, radiometry, spectral domain, and time of acquisitions, as well as for scales. Also, inter-sensor relationships Thenkabail, will help establish seamless monitoring of phenomenon across landscape.

The majority of remote sensing work has been focused on natural environments over the past decades. Applying remote sensing technology to urban areas is relatively new. With the advent of high resolution imagery and more capable techniques, urban remote sensing is rapidly gaining interest in the remote sensing community. Driven by technology advances and societal needs, remote sensing of urban areas has increasing become a new arena of geospatial technology and has applications in all socioeconomic sectors Weng and Quattrochi, Urban landscapes are typically a complex combination of buildings, roads, parking lots, sidewalks, garden, cemetery, soil, water, and so on.

Each of the urban component surfaces exhibits a unique radiative, thermal, moisture, and aerodynamic properties, and relates to their surrounding site environment to create the spatial complexity of ecological systems Oke To understand the dynamics of patterns and processes and their interactions in heterogeneous landscapes such as urban areas, one must be able to quantify accurately the spatial pattern of the landscape and its temporal changes Wu et al.

In order to do so, it is necessary: 1 to have a standardized method to define theses component surfaces, and 2 to detect and map them in repetitive and consistent ways, so that a global model of urban morphology may be developed, and monitoring and modeling their changes over time be possible Ridd Remote sensing technology has been widely applied in urban land use, land cover classification, and change detection. The mixed pixel problem is resulted from the fact that the scale of observation i. Ridd proposed a major conceptual model for remote sensing analysis of urban landscapes, i.

It assumes that land cover in urban environments is a linear combination of three components, namely, vegetation, impervious surface, and soil. Ridd believed that this model can be applied to spatial-temporal analyses of urban morphology, biophysical, and human systems. While urban land use information may be more useful in socioeconomic and planning applications, biophysical information that can be directly derived from satellite data is more suitable for describing and quantifying urban structures and processes Ridd Linear spectral mixture analysis LSMA is another approach that can be used to handle the mixed pixel problem, besides the fuzzy classification.

Instead of using statistical methods, LSMA is based on physically deterministic modeling to unmix the signal measured at a given pixel into its component parts called endmembers Adams et al. Endmembers are recognizable surface materials that have homogenous spectral properties all over the image. LSMA assumes that the spectrum measured by a sensor is a linear combination of the spectra of all components within the pixel Boardman Because of its effectiveness in handling spectral mixture problem and ability to provide continuum-based biophysical variables, LSMA has been widely used in: 1 estimation of vegetation cover Asner and Lobell ; McGwire et al.

Advances in remote sensing applications for urban sustainability

However, with a few exceptions, these studies have focused on technical specifics and on the examination of the effectiveness of LSMA. Only a few studies have explicitly adopted the V-I-S model as the conceptual model to explain urban land cover patterns Phinn et al. LULC classes were formed by using LSMA derived fractions with a hybrid procedure that combined maximum-likelihood and decision-tree algorithms Weng et al. Impervious surfaces are anthropogenic features through which water cannot infiltrate into the soil, such as roads, driveways, sidewalks, parking lots, rooftops, and so on.

In recent years, impervious surface has emerged not only as an indicator of the degree of urbanization, but also a major indicator of urban environmental quality Arnold and Gibbons, Various digital remote sensing approaches have been developed to estimate and map impervious surfaces, including mainly: image classification, multiple regression, sub-pixel classification, artificial neural network, classification and regression tree algorithm, and so on.

Through review of basic concepts and methodologies, analysis of case studies, and examination of methods for applying up-to-date techniques to impervious surface estimation and mapping, this book may serve undergraduate and graduate students as a textbook, or be used as a reference book for professionals, researchers, and alike in the academics, government, industries, and beyond.

The root-mean-square-error of the impervious surface map with the ANN model was Since launch of the first Earth Resources Technology Satellite, ERTS-1 , scientist have used remotely-sensed data from different sensors to characterize, map, analyze and model the state of the land surface and surface processes. With the help of new algorithms, new hydrological information were extracted from remotely-sensed data and used in hydrological and environmental modeling. These new information and hydrological parameters have increased our understanding of the different hydrological processes by helping in quantifying the rate and amount of water and energy fluxes in the environment.

The ability of these sensors in providing various spatiotemporal scales data has also increased our capability in looking into one of the challenges of environmental modeling, mismatch between scales of environmental process and available data. The role of remote sensing in understanding hydrological processes and fluxes across different spatial and temporal scales can be tremendous, if appropriate spatial and temporal resolution remotely-sensed data are available under ranges of bands.

With the availability of large volumes of remotely-sensed data, geographical information system GIS tools to manipulate, process, store and retrieve such data and efficient computing system, the application of remote sensing to water resources has been increasing in recent years. The application of remote sensing in water resources research and management mainly lies in one of the three categories: mapping of watersheds and features, indirect hydrological parameter estimation and direct estimation of hydrological variables.

Different sensors aboard airplane or satellites have been used extensively in providing imaging, photographing and mapping information for different purposes. Mapping of wetlands, floodplains, disaster areas, coastal shores, river banks, snow pack, fire damage, drainage basins and others that show the areal extent of a given land feature distinct from others due to the difference in the spectral signature fall under this category. Different studies have used aerial photos, satellite images, lidar and radar data to map and visualize land surfaces for planning, resource mapping, hazard assessment and emergency operations.

Landsat images were used to capture the expansion of a hydrologically-closed lake, Devils Lake, North Dakota. Results of the study are presented below. The lake surface area of the chain of Devils Lakes excluding Stump Lake mapped from Landsat for and is indicated in Figure 3. Between and , the surface area of the lake was lowest in and highest in Figures 3.

The lake surface area from Landsat images was classified and only lake areas were computed for each respective year. As depicted from the images, the areal increase of the lake is greater in recent years. The white line in Figure 3 marks the lake boundary line in Figure 4 , lake surface area by and year without Stump Lake derived from Landsat images, shows that each meter rise of lake inundates a progressively greater area resulting in increased flood volume.

It is shown that a 1. This indicates the severity of the flooding problem at higher lake levels attributed partly due to the flat topography of the contributing watershed. The majority of remote sensing contribution in water resources management falls under the indirect hydrological parameter estimation category. The most common area of contribution in this category is the use of classification algorithms to generate land cover classes. Using the unique spectral signature of land surfaces, mainly in the visible, infrared and thermal spectra, land cover classes are estimated from such data.

The use of surface temperature, normalized difference vegetation index NDVI and unsupervised classification algorithm is demonstrated in mapping land-cover for the Heart River sub-basin, Missouri River basin in North Dakota. The unsupervised classifier yielded 30 spectral classes. Scattergram of the scaled Normalized Difference Vegetation Index NDVI s versus scaled surface radiant temperature T s were used to find instances of strong correlation between them and the land-cover data of the sub-basin.

The spectral signatures of all these classes were used to determine the mean radiance for each band. Using the mean signature values, additional layers and vegetation indices were derived. Percent vegetation cover for Econlockhatchee River sub-basin Econ sub-basin in Florida a b Surfaces that impede the natural infiltration of water and enhance surface runoff are classified as impervious surfaces.

Associated with urbanization and construction of pavements, roads and buildings, impervious surfaces play an important role in surface runoff and the transport of contaminants. Remote sensing has been used as an effective technique to map impervious surfaces using spectral characteristics of surfaces Melesse b , Melesse and Wang, Ridd and Owen et al. In surface runoff estimation, impervious surfaces are classified as hydraulically connected and those that are not. The hydraulically connected surfaces such as parking lots and roads are connected to the drainage system where runoff from such surfaces leads to the drainage network.

Those surfaces such as roof-tops are classified as hydraulically not connected. Rain water from roof tops can fall into the pervious surface area such as grass hence termed as disconnected. The resulting storm runoff from such surfaces can be lower than the hydraulically connected impervious surface areas. Topography plays an important role in the distribution and flux of water and energy within the natural landscape. Surface runoff, evaporation and infiltration are hydrologic processes that take place at the ground-atmosphere interface.

Quantitative assessment of these processes depends on topographic configuration of the landscape, which is one of several controlling boundary conditions. Wetness index WI provides a description of the spatial distribution of the soil moisture using topographic information. WI is computed as,. As specific drainage area increases and gradient decreases, WI and soil moisture content increase. Wetness index takes into account both a local slope geometry and site location in the landscape, combining data on gradient and specific drainage area. This can lead to higher correlations of soil moisture with WI , hence evapotranspiration, than with specific drainage area and gradient.

Wetness index controls flow accumulation, soil moisture, distribution of saturation zones, depth of water table, evapotranspiration, thickness of soil horizons, organic matter, pH, silt and sand content, plant cover distribution Kulagina et al. A study to understand the relationship between latent heat flux and microtopography 1m resolution of a wheat field was studied using remote sensing-based latent heat flux.

The latent heat flux estimation technique is based on the surface energy balance approach using remotely-sensed data Bastiaanssen, The detailed procedure on estimating grid-based latent heat flux for the study area is shown in Melesse and Nangia and Oberg and Melesse The relationship between WI Figure 8 and latent heat flux for a wheat field is strong, since the topography influences the flux and distribution of water.

Grids with higher values of WI are areas receiving most of the flow higher flow accumulation and lower gradient. These areas have higher soil moisture, hence higher rate of evaporation, than areas with lower values of WI. It is also indicated that when water is a limiting factor, plants on higher WI areas grow well with good canopy cover than plants in other zones of the field. This increases the transpiration vegetation latent heat of the crops. Figure 9 shows the scattergram of WI vs.

The latent heat seems to increase at higher rate at lower values of WI than at higher values of WI. This is attributed to the limited available water for evaporation proportional to WI. With the advent of grid-based remotely-sensed rainfall data, the application of crop water balance models for crop monitoring and yield forecasting has gained increased acceptance by various international, national and local organizations around the world.

Soil water is a key state variable in hydrological modeling and determines the partitioning of rainfall into runoff and deep percolation, and also controls the rate of evapotranspiration ET. Although the estimation of actual evapotranspiration ET a is the ultimate goal of many researchers for hydrological and agronomical applications, it is often difficult to quantify and requires expensive instrumentation.

However, hydrological modeling techniques are used to estimate ET a. The two basic modeling techniques to estimate ET a are based on either energy balance e.

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For monitoring large areas using remotely sensed data, the water balance approach provides an operational advantage in terms of data availability. While the energy balance models are mainly driven by the thermal data, the water balance models are driven by rainfall. Naturally, cloud cover is an issue to provide daily estimates of ETa on rain-fed agriculture from the energy balance models. On the other hand, availability of satellite-derived rainfall data at various temporal and spatial scales makes operational estimation of ET a using a water balance model a relatively easy task for various decision makers in agriculture and natural resources.

The most widely used water balance technique for operational use is the FAO water balance algorithm that produces the crop water requirement satisfaction index WRSI , which is also known as the crop specific drought index CSDI. A value of indicates all the crop demand has been met while values less than 50 generally indicates a severe water shortage that could lead to complete failure of the crop Smith Values between 50 and will indicate different degrees of crop stress and yield reductions from shortage of adequate supply. FAO studies Doorenbos and Pruitt have shown that WRSI can be related to crop production using a linear yield-reduction function specific to a crop.

Meyer et al. Furthermore, Senay and Verdin enhanced the geospatial model by introducing the concept of maximum allowable depletion MAD and soil water stress factor from irrigation engineering for better estimation of ETa as a function of soil water content. The seasonal crop water requirement satisfaction index for a crop is based on the water supply and demand that a crop experiences during a growing season.

ETc is calculated from the product of the Penman-Monteith reference evapotranspiration ET o using the standardized FAO equation that uses short grass as the reference crop Allen et al. Crop coefficients K c , piecewise linear weighting functions, have traditionally been used to adjust for type and growth stage of the crop:. The key difference between ET c and ET a is that ET a depends on soil moisture that is calculated on a daily basis to provide the daily soil stress correction factor, Ks Senay and Verdin Whenever the soil water content is above the maximum allowable depletion MAD level, which varies by vegetation type, the ET a will balance the ET c resulting in no net water stress.

However, when the available soil water falls below the MAD level, the ET a will be lower than ET c , in proportion to the remaining soil water content. Runoff and deep drainage out of the root zone are assumed to occur in excess of field capacity. The soil water content is obtained through a simple mass balance equation where the level of soil water is monitored in a root-zone soil layer defined by the water holding capacity WHC of the soil and the crop root depth, i.

The key input data to the water balance model are precipitation, potential evapotranspiration PET , and soil water holding capacity, and crop coefficient. While the key crop coefficient values are obtained from the FAO publication Allen, , the other three main datasets are spatially distributed are described in brief below. Precipitation is the single most important input of the model. NASA generates daily rainfall data at 25 km resolution from the Tropical Rainfall Mapping Mission TRMM; satellite systems that covers the globe between 60 degrees latitude north and south of the equator.

Another important model parameter for the crop water balance model is the soil water holding capacity. Daily and dekadal day model outputs from the crop water balance are posted in a website Africa, Central America, Afghanistan. Similar products are available for western and eastern Africa, central America, Haiti and Dominican Republic and Afghanistan.

Applications of Remote Sensing and GIS in Wasteland mapping

Spatial distribution of maize crop water requirement satisfaction index WRSI for southern Africa as of the 2 nd dekad of March Normally, the region's crop growing season spans from September through April. The exact growing season depends on the geographic location.

Remote Sensing Sensors and Applications in Environmental Resources Mapping and Modelling

For example, for much of Zimbabwe, the growing season is from November to April. Onset of rains map is a surrogate for the start of season SOS of the crop growing period. The SOS is defined with a simple rainfall accounting criteria: a total of 25 mm rainfall received in one dekad followed by a total of 20 mm in the following two consecutive dekads. The SOS map by itself provides critical information on the performance of the season, especially if there is a significant delay in its establishment.

In addition, the SOS map is used to initialize the crop water balance model, i. The end of the growing season EOS is dependent on the location and it varies from a minimum of 9 dekads 3 months in arid and semi-arid regions to 18 dekads 6 months in mountainous and wetter regions. Figure 11 shows the soil water index SWI which shows the level of soil water in the root zone as defined by the soil water holding capacity WHC of the top 1 m of soil. The SWI image shows 4 broad classes for qualitative interpretations.

This is generally a trigger level for drought early warning. The image is interpreted along with a weekly forecast rainfall. If the 7-day forecast rainfall is not promising, the areas with lowest SWI category are expected to go into the crop wilting phase. This is considered critical and becomes a potential candidate for highlighting it as a drought polygon if the data is corroborated with field information. In this regard, a large area of southern Africa falls in this region by the 2 nd dekad of March, because of a dry spell in February and March.

Figure 12 shows the spatial distribution of maize crop water requirement satisfaction index WRSI for southern Africa as of the 2 nd dekad of March The extended WRSI is composed of two data sources: 1 observed demand and supply from the SOS till the 2 nd dekad March, and 2 extended demand and supply from the 2 nd dekad of March till the end of the growing season. The extended demand and supply is based on climatological rainfall and potential evapotranspiration data. The WRSI values are expressed as index from 0 to Regions showing WRSI values between the 50 and 95 are at different stages of yield reduction due to water shortage.

The exact yield reduction is determined by the prevailing management practices in the region. Thus, using historical data from an administrative district, it is possible to formulate a mathematical relationship between WRSI and yield, which would allow the possibility of using WRSI to forecast yield beginning the mid crop growing season. Although the product assumes a predominant crop type in the region, the results are also indicative of other cereal crops growing in the same region and season.

This is another way of looking at the model output to cancel out some of the potential wrong model assumptions such as the assumed length of the growing season. This relative description is corroborated by field reports in that there was a wide-spread yield reduction in much of the southern regions and an improved crop performance in Tanzania. The advances made in spaceborne remote sensing in the last 50 years, from sputnik 1 to Worldview-1, has been phenomenal.

Urban applications of satellite remote sensing and GIS analysis (English) | The World Bank

The present trends point to increasing several innovations. First, availability of data from multiple sensors with wide array of spatial, spectral, and radiometric characteristics. These data will be available from multiple sources. Second, significant advances have been made in harmonizing and synthesizing data from multiple sources that facilitates the use of data from these sensors of widely differing characteristics and sources.

We also expect vendors to market data from multiple sources by harmonizing and by adding value. Third, availability of data from a constellation such as from Rapideye at very high resolution of 6. This will certainly require innovations in data handling, storage, and backup. But for applications, a combination of very high spatial resolution and frequent coverage is very attractive. Fifth, for many environmental and natural resource applications global wall-to-wall coverage is essential and here satellites like Landsat will continue to play most important role.

Sixth, data availability in hyperspectral and hyperspatial sensors brings in new challenges in data mining, processing, backup, and retrieval. Seventh, the advances made in data synthesis, presentation, and accessibility through systems such as Google Earth will bring in new users and multiply applications of remote sensing in environmental sciences and natural resources management. The authors expect that the future needs of the spatial data will be met overwhelmingly by spaceborne remote sensing. National Center for Biotechnology Information , U. Journal List Sensors Basel v.

Sensors Basel. Published online Dec Assefa M. Thenkabail , 3 and Gabriel B. Senay 4. Prasad S. Gabriel B. Author information Article notes Copyright and License information Disclaimer. E-mail: ude. Received Nov 8; Accepted Nov This article has been cited by other articles in PMC.