Preliminary evaluation of the relationships between SPOT-1 HRV data and forest stand parameters

Abstract

Image data recorded from SPOT-1 were correlated with five forest stand parameters: mean percentage canopy cover, tree density, mean tree diameter at breast height (DBH), mean tree height and sub-compartment age. With the exception of percentage canopy cover, the correlation coefficients for the near-infrared waveband (S3) were all significant at the 99 per cent confidence level. The correlation coefficients for the red (S2) and green (S1) wavebands were lower and this may be due to the low dynamic range of the data for forest canopies in these wavebands.     

Satellite remote sensing of rangelands in Botswana I. Landsat MSS and herbaceous vegetation

Field measurements of the cover and biomass of live and dead herbaceous vegetation, the cover of trees and shrubs and the area of bare ground were made for rangelands in three study sites in eastern Botswana between September 1983 and April 1984. The sites were selected to be representative of Terminalia sericea, Cotophospermum mopane and Combretum apiculatum-Acacia nigrescens woodland savannas, which, taken together, occupy a large part of eastern and northern Botswana. Mean herbaceous biomass varied from 0 to 563?kg ha^?1, cover from 0 to 21 per cent and bare ground from 57 to 85 per cent. The mean tree canopy cover in each site was approximately 30 per cent, with a range of 0-50. Landsat miiltispectral scanner (MSS) data were obtained for May, August, November 1983, January and March-April 1984. Nine MSS pixels were registered with 20 sample plots in each site and the ratios of mean band-7 to band-5 digital numbers were calculated. The variation in these ratios between the three sites and four dates on which the data were acquired was analyzed with respect to the field measurements.

The results indicate that the biomass and cover of live herbaceous vegetation and the bare ground individually account for quite small, but significant proportions of the variation in band ratio for all four observation dates taken together. However, when factors that specified site and date were included in the multiple regression models, 75·7, 77·9 and 64·1 per cent of the variation in herb biomass, cover and bare ground respectively were accounted for. Multitemporal integration of the band ratios accounted for 70·3 per cent of the variation in the end-of-season biomass of herbaceous vegetation, without the need to use a site factor. These highly significant relationships were achieved without including measurements of the canopy cover of trees in the models. Analysis of the individual site data revealed some negative relationships between band ratios and both biomass and Cover of herbaceous vegetation in one site, which seem to be a result of a strong negative relationship between the cover of herbaceous vegetation and trees in this vegetation type.

It was concluded that predictive equations could be constructed which enable cover and biomass of herbaceous vegetation and the area of bare ground to be estimated from Landsat MSS band-7/band-5 ratios, but only if the relationship is applied to sites having the same type of vegetation as that for which the equations were derived. Stratification of the scene using vegetation maps is therefore an important requirement for the application of remote sensing by Landsat MSS to the monitoring of the rangelands in Botswana     

Review of: “ Integrative Approaches in Remote Sensing.” Edited by N. LONGDON (Noordwijk,The Netherlands: ESTEC,ESA SP-214. Proceedings of an EARSeL/ESA symposium, 8-11 April 1984.) [Pp. 375.] Price Ff 170.

Three AVHRR-LAC data sets acquired in September 1990 and January 1991 were used to map the forest resources of Madagascar. The island was partitioned into four strata to include: (1) the western hardwoods, (2) the central grasslands, (3) the eastern rainforest, and (4) spiny forest. Each stratum was classified separately using AVHRR-LAC data in conjunction with 1984-1988 Landsat-MSS photoproducts. The results of AVHRR classification indicate that approximately 11 per cent of the island is covered by forest. Approximately 1 per cent of the island was obscured by clouds and could not be enumerated. Estimates of forest area, by stratum, follow: western hardwoods, 6697 km²; central grasslands, 2830km²; eastern rainforest 34167km²; and spiny forest, 17 224 km². The total forest area on the 587041km² island is estimated to be 60918km². The AVHRR forest map was compared to a mid 1970s land cover map which was developed using Landsat-MSS photoproducts. The average class agreement between the mid 1970s ground reference map and the 1990 AVHRR-LAC map was 78-2 per cent, the overall accuracy was 81-1 per cent. Areas identified as forest on the ground reference map on the 1990 AVHRR map agreed only 62 per cent of the time, however, that figure confounds AVHRR misclassifi-cation error with actual forest loss over the decade. Much of the per-pixel disagreement between the ground reference and AVHRR maps involved areas identified as forest in the 1970s and as nonforest in 1990. These results demonstrate that one kilometre spatial resolution satellite data may be used to provide a reconnaissance level survey of the forest resources of a region or subcontinent when used in conjunction with fine resolution data.     

A case study on the determination of fog optical depth and liquid water path using AVHRR data and relations to fog liquid water content and horizontal visibility

An improved direct method is presented for the calculation of fog optical depth and liquid water path from fog top albedo using AVHRR channel 1. Fog top albedo is determined by means of a simple radiative transfer model under special consideration of radiometric corrections. By the conversion of channel I digital counts into a reflectance factor, a correction has to be performed, due to the degradation of channel 1 radiometer with time. By omitting atmospheric and anisotropy corrections, modal uncertainties in the calculations of fog top albedo in the order of ±25 per cent and ± 5 per cent were obtained. Assuming a homogeneous droplet distribution within fog layers at scan lime, a horizontal visibility was calculated from fog optical depth and fog geometrical thickness, whereas fog liquid water content was derived from liquid water path and fog thickness. Ground truth from high resolution transmiss-ometer observations showed an encouraging accuracy with a mean deviation between the observed and calculated visibility of about 56 m and a mean error in the determination of fog liquid water content of about 0·025 gm^?3.     

Satellite remote sensing of rangelands in Botswana II. NOAA AVHRR and herbaceous vegetation

NOAA-7 Advanced Very High Resolution Radiometer (AVHRR) global-area coverage (GAC) data for the visible and near-infrared bands were used to investigate the relationship between the normalized difference vegetation index (NDVI) and the herbaceous vegetation in three representative rangeland types in eastern Botswana. Regressions between Landsat MSS band-7/band-5 ratios and field measurements of the cover of the live parts of herbaceous plants, above-ground biomass of live herbaceous plants and bare ground were used in conjunction with MSS data in order to interpolate the field data to 144 km² areas for comparison with blocks of nine AVHRR GAC pixels. NOAA NDVI data were formed into 10-day composites in order to remove cloud cover and extreme off-nadir viewing angles. Both individual NDVI composite data and multitemporal integrations throughout the period May 1983-April 1984 were compared with the field data.

In multiple linear regressions, the cover and biomass of live herbaceous plants and bare ground measurements accounted for 42, 56 and 19 per cent respectively of the variation in NDVI. When factors were included in I he regression models to specify the site and date of acquisition of the data, between 93 and 99 per cent of the variation in NDVI was accounted for. The total herbaceous biomass at the end of the season was positively related to integrated NDVI, up lo the maximum biomass observed in a 12km × 12km area (590kgha^?1)- These results give a different regression of herbaceous biomass values on integrated AVHRR NDVI to that reported by Tucker et at. (1985 b) for Senegalese grasslands. The effect of the higher cover of the tree canopy in Botswana on this relationship and on the detection of forage available to livestock is discussed.     

A verification of the 'triangle' method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index (NDVI) and surface e

An inversion procedure is presented for estimating surface soil water content (as surface moisture availability, Mo ), fractional vegetation cover ( Fr ), and the instantaneous surface energy fluxes, using remote multispectral measurements made from an aircraft. The remotely derived values of these fluxes and the soil water content are compared with field measurements from two intensive field measurement programs FIFE and MONSOON '90. The measurements from the NS001 multispectral radiometer were reduced to fractional vegetation cover, surface soil water content (surface moisture availability), and turbulent energy fluxes, with the application of a soil vegetation atmosphere transfer (SVAT) model. A further step in the inversion process involved 'stretching' the SVAT results between pre-determined boundaries of the distribution of normalized difference vegetation index (NDVI) and surface radiant temperature ( To ). Comparisons with measurements at a number of sites from two field experiments show standard errors, between derived and measured fluxes, generally between 25 and 55Wm-2, or about 10-30 per cent of the magnitude of the fluxes and for surface moisture availability of 16 per cent.     

Vegetation spatial variability and its effect on vegetation indices

Abstract

Landsat MSS data were used to simulate low resolution satellite data, such as NOAA AVHRR, to quantify the fractional vegetation cover within a pixel and relate the fractional cover to the normalized difference vegetation index (NDVI) and the simple ratio (SR). The MSS data were converted to radiances from which the NDVI and SR values for the simulated pixels were determined. Each simulated pixel was divided into clusters using an unsupervised classification programme. Spatial and spectral analysis provided a means of combining clusters representing similar surface characteristics into vegetated and non-vegetated areas. Analysis showed an average error of 12·7 per cent in determining these areas. NDVI values less than 0·3 represented fractional vegetated areas of 5 per cent or less, while a value of 0·7 or higher represented fractional vegetated areas greater than 80 per cent. Regression analysis showed a strong linear relation between fractional vegetation area and the NDVI and SR values; correlation values were 0·89 and 0·95 respectively. The range of NDVI values calculated from the MSS data agrees well with field studies.     

The estimation of the surface moisture of a vegetated soil using aerial infrared photography

The bidirectional reflectance of near infrared wavelengths of electromagnetic radiation from a vegetation canopy is primarily determined by the relative area and reflectance of the canopy and canopy dependent components: leaves, non-green vegetation, soil and shadow. It has been shown that when the percentage cover of leaves and non-green vegetation are both known and constant and the effect of shadow is minimal, then the near infrared bidirectional reflectance from the-canopy is negatively related to surface soil moisture.

This study was based on the above observation to estimate surface soil moisture of a vegetated soil from remotely sensed measurements of near infrared bidirectional reflectance.

The near infrared bidirectional reflectance, surface soil moisture and vegetation cover were measured at 10 heathland sites on 18 dates. The surface soil moisture was significantly related (at better than the 1 per cent level) to the Y axis intercept, when near infrared bidirectional reflectance (Y) was regressed against the percentage cover of green vegetation (X). This relationship between soil moisture and canopy reflectance was then used to enable the surface soil moisture of vegetated heathland soil to be estimated by means of five flights of black and white infrared aerial photography. It proved possible to estimate the surface soil moisture of the vegetated soil with an accuracy of ±18·4 percent at the 95 percent confidence limits. Possible improvements to the technique are discussed.     

Estimating spatial variations in soil organic carbon using satellite hyperspectral data and map algebra

This study evaluated the effectiveness of using Hyperion hyperspectral data in improving existing remote-sensing methodologies for estimating soil organic carbon (SOC) content on farmland. The study area is Big Creek Watershed in Southern Illinois, USA. Several data-mining techniques were tested to calibrate and validate models that could be used for predicting SOC content using Hyperion bands as predictors. A combined model of stepwise regression followed by a five hidden nodes artificial neural network was selected as the best model, with a calibration coefficient of determination (R ²) of 78.9% and a root mean square error (RMSE) of 3.3 tonnes per hectare (t ha^?1). The validation RMSE, however, was found to be 11.3 t ha^?1. Map algebra was implemented to extrapolate this model and produce a SOC map for the watershed. Hyperspectral data improved marginally the predictability of SOC compared to multispectral data under natural field conditions. They could not capture small annual variations in SOC, but could measure decadal variations with moderate error. Satellite-based hyperspectral data combined with map algebra can measure total SOC pools in various ecosystem or soil types to within a few per cent error.     

Using short-term MODIS time-series to quantify tree cover in a highly heterogeneous African savanna

Reliable mapping of tree cover and tree-cover change at regional, continental, and global scales is critical for understanding key aspects of ecosystem structure and function. In savannas, which are characterized by a variable mixture of trees and grasses, mapping tree cover can be especially challenging due to the highly heterogeneous nature of these ecosystems. Our objective in this article was to develop improved tools for large-scale classification of savanna tree cover in grass-dominated savanna ecosystems that vary substantially in woody cover over fine spatial scales. We used multispectral, low-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery to identify the bands and metrics that are best suited to quantify woody cover in an area of the Serengeti National Park, Tanzania. We first used 1-m resolution panchromatic IKONOS data to quantify tree cover for February 2010 in an area of highly variable tree cover. We then upscaled the classification to MODIS (250 m) resolution. We used a 2 year time series (IKONOS date ± 1 year) of MODIS 16 day composites to identify suitable metrics for quantifying tree cover at low resolution, and calculated and compared the explanatory power of three different variable classes for four MODIS bands using Lasso regression: longitudinal summary statistics for individual spectral bands (e.g. mean and standard deviation), Fourier harmonics, and normalized difference vegetation index (NDVI) green-up metrics. Longitudinal summary statistics showed better explanatory power (R ² = 73% for calibration data; R ² = 61% for validation data) than Fourier or green-up metrics. The mid-infrared, near-infrared, and NDVI bands were all important predictors of tree cover. Mean values for the time series were more important than other metrics, suggesting that multispectral data may be more valuable than within-band seasonal variation obtained from time series data for mapping tree cover. Our best model improved substantially over the MODIS Vegetation Continuous Fields product, often used for quantifying tree cover in savanna systems. Quantifying tree cover at coarse spatial resolution using remote-sensing approaches is challenging due to the low amount and high heterogeneity of tree cover in many savanna systems, and our results suggest that products that work well at global scales may be inadequate for low-tree-cover systems such as the Serengeti. We show here that, even in situations where tree cover is low (<10%) and varies considerably across space, satisfactory predictive power is possible when broad spectral data can be obtained even at coarse spatial resolution.     

Urban green space dynamics and socio-environmental inequity: multi-resolution and spatiotemporal data analysis of Kumasi,Ghana

Urban green spaces (UGS) are crucial for urban sustainability and resilience to environmental vulnerabilities but are often relegated in cities in the global south. This article analysed the spatio-temporal change, composition, extent, and distributional inequities associated with UGS in Kumasi, Ghana. Spatial techniques and Gini index were combined in the assessment. Kumasi UGS coverage is currently 33% but declined fourfold faster in recent years (2009–2014) than previously (1986–2002). The overall accuracy of the change maps: 1986–2014 and 2009–2014 were, respectively, 0.96 ± 0.02 and 0.97 ± 0.02. The Shannon entropy for built-up sprawl in 1986 and 2014 were 0.80 and 0.99, respectively. The UGS area per capita for 2009 (R² = 0.50, p = 0.049) and 2014 (R² = 0.53, p = 0.0398) were moderately correlated with socioeconomic conditions of sub-metropolises. The Gini coefficient for both vegetation and tree cover was 0.26. UGS cover is plummeting and somewhat unevenly distributed across Kumasi. Strategic planning for UGS can ensure ample availability, equity in access, and resilience to climate-related vulnerabilities.     

Estimating stem volume by tree crown area and tree shadow area extracted from pan‐sharpened Quickbird imagery in open Crimean juniper forests

Although open forests represent approximately 30% of the world's forest resources, there is a clear lack of reliable inventory data to allow sustainable management of this valuable resource from semi‐arid areas. This paper demonstrates that the low ground cover of open forest offers a unique opportunity for deriving single tree attributes from high‐resolution satellite imagery, allowing reliable biomass estimation. More particularly, this study investigates the relationship between field‐measured stem volume and tree attributes, including tree crown area and tree shadow area, measured from pan‐sharpened Quickbird imagery with a 0.61 m resolution in a sparse Crimean juniper (Juniperus excelsa M.Bieb.) forest in south‐western Turkey. First tree shadows and crowns were identified and delineated as individual polygons. Both visual delineation and computer‐aided automatic classification methods were tested. After delineation, stem volume as a function of these image‐measured attributes was modelled using linear regression. The statistical analyses indicated that stem volume was correlated with both shadow area and crown area. The best model for stem volume using shadow area resulted in an adjusted R ² = 0.67, with a root mean square error (RMSE) of 12.5%. The model for stem volume using crown area resulted in an adjusted R ² = 0.51, with a RMSE of 15.2%. The results showed that pan‐sharpened Quickbird imagery is suitable for estimating stem volume and may be useful in reducing the time required for obtaining inventory data in open Crimean juniper forests and other similar open forests.     

Classification trees: an alternative to traditional land cover classifiers

Classification trees are a powerful alternative to more traditional approaches of land cover classification. Trees provide a hierarchical and nonlinear classification method and are suited to handling non-parametric training data as well as categorical or missing data. By revealing the predictive hierarchical structure of the independent variables, the tree allows for great flexibility in data analysis and interpretation. In this Letter, we compare a tree' s performance to that of a maximum likelihood classifier using a 1° by 1° global data sel. The tree's accuracy in classifying a validation dala set is comparable to that when using maximum likelihood (82 per cent). The tree also may be used to reduce the dimensionality of data sets and to find those metrics that are most useful for discriminating among cover types.     

MODIS tree cover validation for the circumpolar taiga-tundra transition zone

A validation of the 2005 500 m MODIS vegetation continuous fields (VCF) tree cover product in the circumpolar taiga-tundra ecotone was performed using high resolution Quickbird imagery. Assessing the VCF's performance near the northern limits of the boreal forest can help quantify the accuracy of the product within this vegetation transition area. The circumpolar region was divided into 7 longitudinal zones and validation sites were selected in areas of varying tree cover where Quickbird imagery is available in Google Earth. Each site was linked to the corresponding VCF pixel and overlaid with a regular dot grid within the VCF pixel's boundary to estimate percent tree crown cover in the area. Percent tree crown cover was estimated using Quickbird imagery for 396 sites throughout the circumpolar region and related to the VCF's estimates of canopy cover for 2000-2005. Regression results of VCF inter-annual comparisons (2000-2005) and VCF-Quickbird image-interpreted estimates indicate that: (1) Pixel-level, inter-annual comparisons of VCF estimates of percent canopy cover were linearly related (mean R² = 0.77) and exhibited an average root mean square error (RMSE) of 10.1% and an average root mean square difference (RMSD) of 7.3%. (2) A comparison of image-interpreted percent tree crown cover estimates based on dot counts on Quickbird color images by two different interpreters were more variable (R² = 0.73, RMSE = 14.8%, RMSD = 18.7%) than VCF inter-annual comparisons. (3) Across the circumpolar boreal region, 2005 VCF-Quickbird comparisons were linearly related, with an R² = 0.57, a RMSE = 13.4% and a RMSD = 21.3%, with a tendency to over-estimate areas of low percent tree cover and anomalous VCF results in Scandinavia. The relationship of the VCF estimates and ground reference indicate to potential users that the VCF's tree cover values for individual pixels, particularly those below 20% tree cover, may not be precise enough to monitor 500 m pixel-level tree cover in the taiga-tundra transition zone.     

The development of image processing techniques to assess changes in green vegetation cover along a climatic gradient through Northern Territory,Australia

The green vegetation cover gradient in three regions referred to as subtropical, semi-arid and arid in Northern Territory was assessed in terms of spectral reflectance characteristics. Incremental vegetation cover over different background components (combined spectra) resulted in three vegetation types being defined, Type 1, darkening Type 2 partial darkening and Type 3, highly near-infrared (NIR) reflective. Spectral plots on field sites showed the subtropical region as being typified by high NIR reflectance, the semi-arid curves showed extensive darkening, while the arid region curves show less darkening due to a lower vegetation cover and concomitant high soil reflectance. Results of analysis of variance of regression using different combinations of single bands, band transforms and principal component data resulted in the single band, MSS-5 providing the most useful predictive data in the subtropical and semi-arid areas. Normalized albedo on MSS-5 proved most useful in arid areas. Transforms involving relationships between near-infrared and visible wavebands were not ranked as suitable predictive tools for the semi-arid region. Prior to classification, single bands and band transforms were ranked. Only those showing high significance levels in relation to green vegetation cover were used as input data. Results showed that classification maps using maximum likelihood classifiers can be uniformly applied throughout all three regions resulting in high levels of accuracy in terms of map areas and community types. The classification technique separates out darkening (Type 1) and NIR reflective (Type 3) classes with the threshold of separation for the two types standing at 45-50 per cent green vegetation cover.     

Use of LANDSAT-derived profile features for spring small-grains classification

A simple model that describes the temporal behaviour of greenness is used to extract features to separate small grains from non-small grains. These features have previously proved effective in separating corn and soybeans. An additional feature derived from the temporal behaviour of the ratio of greenness to brightness has been found that aids in separating crops from non-crops. Using a very limited training set of 20 pure pixels per class obtained from ground data, the Ho-Kashyap linear classifier is trained on these derived features. The percentage of correct classification for pure pixels is about 85 per cent based on twenty-three 5nm × 6nm segments in Minnesota, North Dakota, South Dakota and Montana. The accuracy drops to 75 per cent for all LANDSAT pixels. An error in spring grain proportion estimate for all pixels (ground data classifier) of — 6.96 ± 7.85 per cent is found using this linear classifier. However, if segments with low small-grain proportion and strip fallow fields are removed, the error drops to —4.90±6.52 per cent. A considerable fraction of the bias arises due to the presence of impure (more than one class) pixels.     

基于混合光谱分解的兰州城市热岛与下垫面空间关系分析

利用Landsat ETM+数据,采用混合像元线性光谱分解方法提取的城市植被覆盖度与不透水面表征城市下垫面,通过单窗算法反演地表真实温度,对兰州市中心城区的夏季城市热岛强度与城市下垫面的空间分布关系进行相关分析。结果显示,利用中等分辨率ETM+影像对兰州中心城区不透水面和植被盖度分布提取,其成本较低,精度令人满意;兰州城区植被覆盖、不透水面与热岛强度的分布呈空间正自相关,地表温度的空间依赖性极强,与植被盖度和不透水面在空间方向上的相关性差异较大。     

Sub-pixel estimation of urban land cover components with linear mixture model analysis and Landsat Thematic Mapper imagery

We examine the utility of linear mixture modelling in the sub-pixel analysis of Landsat Enhanced Thematic Mapper (ETM) imagery to estimate the three key land cover components in an urban/suburban setting: impervious surface, managed/unmanaged lawn and tree cover. The relative effectiveness of two different endmember sets was also compared. The interior endmember set consisted of the median pixel value of the training pixels of each land cover and the exterior endmember set was the extreme pixel value. As a means of accuracy assessment, the resulting land cover estimates were compared with independent estimates obtained from the visual interpretation of digital orthophotography and classified IKONOS imagery. Impervious surface estimates from the Landsat ETM showed a high degree of similarity (RMS error (RMSE) within approximately ±10 to 15%) to that obtained using high spatial resolution digital orthophotography and IKONOS imagery. The partition of the vegetation component into tree vs grass cover was more problematic due to the greater spectral similarity between these land cover types with RMSE of approximately ±12 to 22%. The interior endmember set appeared to provide better differentiation between grass and urban tree cover than the exterior endmember set. The ability to separate the grass vs tree components in urban vegetation is of major importance to the study of the urban/suburban ecosystems as well as watershed assessment.     

Linear regression modelling for the estimation of oil palm age from Landsat TM

This paper investigates the accuracy with which the age since field planting of oil palm (Elaeis guineensis Jacq.) can be estimated from Landsat Thematic Mapper (TM) radiance at pixel and stand scales. The study site, a commercial plantation 30 km south-east of Kuala Lumpur in Selangor, Malaysia, consisted of even-aged blocks from 4 to 21 years old. Spectral data were the six reflective TM bands and three spectral indices. Nonlinear negative relationships between spectral variables and age are compared to published trends in leaf area, stem height and per cent canopy cover for oil palm and other tree plantations. Correlation coefficients between log age and log radiance are moderate and highly significant (p<0.01) for bands 2-5 and 7 (-0.214 to-0.776) at the pixel scale, and increase at the stand scale (r ²=0.985 for log band 5, p<0.01). Relationships are strongest for the mid-infrared bands, especially band 5 (r ²=0.585, p <0.01) and the infrared index (IRI), a normalized difference index of bands 4 and 5 (r ²= 0.48, p<0.01). Direct and inverse linear regression models for log age with log band and log age with IRI squared (IRIsq) were constructed at both scales. Equivalent age was estimated from the models using independent test sets for differing scales and degrees of aggregation of the age classes. Single age classes cannot be estimated accurately at the pixel or stand scales; the lowest RMS error was obtained from the direct model using all bands (RMS error=3.9 years at pixel scale, 2.7 at stand scale). A posteriori aggregation into generalized age classes (<5, 6-10, 11-15, 16-21 years) improved the RMS error but the results were still unacceptably high (2.2, 2.3, 2.7, 6.0 years respectively for direct model 3 using all bands). Acceptable RMS errors down to 0.58 years were obtained for models using IRIsq with generalized age classes developed and applied at the stand scale when variations in ground cover and other variables were averaged out. The spatial pattern of error in equivalent age deserves investigation for precision crop management.     

Applying a normalized ratio scale technique to assess influences of urban expansion on land surface temperature of the semi-arid city of Erbil

The difference between surface and air temperature within a city and its surrounding area is a result of variations in surface cover, thermal capacity, and 3-dimensional geometry. This research has examined and quantified the decreasing daytime land surface temperature (LST) in Erbil, Kurdistan region of Iraq, and the influence of rapid urban expansion on urban heat/cool island effect over a 20 year period. Land-use/land-cover change across this time period is also established using pixel samples. The current study proposes the application of the normalized ratio scale (NRS) to adjust the temperature of images acquired at different dates to the same range. Eleven satellite images acquired by Landsat 4, 5, 7, and 8 during the period 1992–2013 are used to retrieve LST. The results indicate that 55.3 km² of city land cover changed from bare soil to urban; consequently, the mean LST of the new urbanized area decreased by 2.28°C. The normalized difference vegetation index (NDVI) of Sami Abdul-Rahman (S.A.) Park increased from 0.09 ± 0.01 to 0.32 ± 0.11, resulting in a decrease of the mean LST by 7.29°C. This study shows that the NRS method is appropriate for detecting temperature trends from urbanization using remote-sensing data. It also highlights that urban expansion may lead to a decrease in daytime LST in drylands.