Land-cover classification in a moist tropical region of Brazil with Landsat TM imagery

This research aims to improve land-cover classification accuracy in a moist tropical region in Brazil by examining the use of different remote sensing-derived variables and classification algorithms. Different scenarios based on Landsat Thematic Mapper (TM) spectral data and derived vegetation indices and textural images, and different classification algorithms - maximum likelihood classification (MLC), artificial neural network (ANN), classification tree analysis (CTA), and object-based classification (OBC), were explored. The results indicated that a combination of vegetation indices as extra bands into Landsat TM multispectral bands did not improve the overall classification performance, but the combination of textural images was valuable for improving vegetation classification accuracy. In particular, the combination of both vegetation indices and textural images into TM multispectral bands improved overall classification accuracy by 5.6% and kappa coefficient by 6.25%. Comparison of the different classification algorithms indicated that CTA and ANN have poor classification performance in this research, but OBC improved primary forest and pasture classification accuracies. This research indicates that use of textural images or use of OBC are especially valuable for improving the vegetation classes such as upland and liana forest classes having complex stand structures and having relatively large patch sizes.     

Mapping leaf area index over a mixed natural forest area in the flooding season using ground-based measurements and Landsat TM imagery

Leaf area index (LAI) is an important structural parameter in terrestrial ecosystem modelling and management. Therefore, it is necessary to conduct an investigation on using moderate-resolution satellite imagery to estimate and map LAI in mixed natural forests in southeastern USA. In this study, along with ground-measured LAI and Landsat TM imagery, the potential of Landsat 5 TM data for estimating LAI in a mixed natural forest ecosystem in southeastern USA was investigated and a modelling method for mapping LAI in a flooding season was developed. To do so, first, 70 ground-based LAI measurements were collected on 8 April 2008 and again on 1 August 2008 and 30 July 2009; TM data were calibrated to ground surface reflectance. Then univariate correlation and multivariate regression analyses were conducted between the LAI measurement and 13 spectral variables, including seven spectral vegetation indices (VIs) and six single TM bands. Finally, April 08 and August 08 LAI maps were made by using TM image data, a multivariate regression model and relationships between April 08 and August 08 LAI measurements. The experimental results indicate that Landsat TM imagery could be used for mapping LAI in a mixed natural forest ecosystem in southeastern USA. Furthermore, TM4 and TM3 single bands (R ² > 0.45) and the soil adjusted vegetation index, transformed soil adjusted vegetation index and non-linear vegetation index (R ² > 0.64) have produced the highest and second highest correlation with ground-measured LAI. A better modelling result (R ²?=?0.78, accuracy?=?73%, root mean square error (RMSE)?=?0.66) of the 10-predictor multiple regression model was obtained for estimating and mapping April 08 LAI from TM data. With a linear model and a power model, August 08 LAI maps were successfully produced from the April 08 LAI map (accuracy?=?79%, RMSE?=?0.57), although only 58–65% of total variance could be accounted for by the linear and non-linear models.     

Mapping of boreal vegetation of a temperate mountain in China by multitemporal Landsat TM imagery

The Changbai Mountain Natural Reserve (2000 km 2 ), north-east China, is a very important ecosystem representing the temperate biosphere. The cover types were derived by using multitemporal Landsat TM imagery, which was modified with DEM data on the relationship between vegetation distribution and elevation. It was classified into 20 groups by supervised classification. By comparing the results of the classification of different band combinations, bands 4 and 5 of an image from 18 July 1997 and band 3 of an image from 22 October 1997 were used to make a false colour image for the final output, a vegetation map, which showed the best in terms of classification accuracy. The overall accuracy by individual images was less than 70%, while that of the multitemporal classification was higher than 80%. Further, on the basis of the relationship of vegetation distribution and elevation, the accuracy of multitemporal classification was raised from 85.8 to 89.5% by using DEM. Bands 4 and 5 showed a high ability for discriminating cover types. Images acquired in late spring and mid-summer were recognized better than other seasons for cover type identification. NDVI and band ratio of B4/B3 proved useful for cover type discrimination, but were not superior to the original spectral bands. Other band ratios like B5/B4 and B7/B5 were less important for improving classification accuracy. The changes of spectral reflectance and NDVI with season were also analysed with 10 images ranging from 1984 to 1997. Seperability of images in terms of classification accuracy was high in late spring and summer, and decreased towards winter. There were five vegetation zones on the mountain, from the base to the peak: deciduous forest zone, mixed forest zone, conifer forest zone, birch forest zone and tundra zone. Spruce-fir conifer dominated forest was the most dominant vegetation (33%), followed by mixed forest (26%), Korean pine forest (8%) and mountain birch forest (5%).     

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.     

Measurement of oak tree density with Landsat TM data for estimating biogenic isoprene emissions in Tennessee,USA

Isoprene emissions from oak trees in the eastern USA play an important role in tropospheric ozone pollution. Oak trees (Quercus) emit an order of magnitude more isoprene than most other emitting tree species, and are by far the largest source of biogenic isoprene in the eastern US. We used Landsat TM data to measure oak tree abundance near Oak Ridge, Tennessee, to estimate fluxes of isoprene. The Landsat classification was performed using multi-date data, supervised classification techniques, and an iterative approach. Training sites were selected based on transect data, and ten vegetation classes were mapped. A supervised classification algorithm called the Spectral Angle Mapper was used to classify the data. Empirical vegetation emission data were used to estimate the isoprene flux from each of the vegetation classes. The resultant isoprene flux maps were compared with concentrations measured in the field, and a good correspondence was observed. We also compare the Landsat classification with three other landcover schemes including the USGS's Global Landcover Classification, which is based on AVHRR data. Results from these landcover classifications are used as input for models that predict tropospheric ozone production and are used to investigate ozone control strategies.     

Comparison of linear and nonlinear spectral unmixing approaches: a case study with multispectral TM imagery

Spectral unmixing has been widely used by researchers in quantitative remote sensing due to the prevalence of mixed pixels in low- or middle-resolution images. In this article, six linear and nonlinear unmixing approaches – fully constrained least squares (FCLS), bilinear-Fan model (BFM), polynomial post-nonlinear model (PPNM), supervised fuzzy c-means (SFCM), Support Vector Machine (SVM), and artificial neural network (ANN) – are applied with multispectral Landsat Thematic Mapper (TM) data in order to systematically compare their performance under different scenarios. In addition, a strategy of band selection was proposed for solving the endmember variability issue. The unmixing results were analysed in terms of the overall performance, pure and mixed data set, sub-scenes with different mixture proportions by calculating the accuracy indices: root mean square error (RMSE) and the Pearson correlation coefficient (r). Nonlinear approaches can generate a closer abundance fraction map to reference, and have a higher overall accuracy than the linear approach. Nevertheless, the performance of nonlinear approaches differed dramatically with the increased proportion of mixed pixels in different study areas. SVM, SFCM, BFM, and PPNM depicted a scenario better when the proportion of mixed pixels was high, whereas ANN worked more effectively when processing large amounts of relatively pure pixels (or mixed pixels with large/extreme proportions). The linear approach, in contrast, performed more consistently for various areas. Overall, our study indicates that nonlinear approaches are more effective than the linear one, especially for a study area consisting of different small parcels. The performance of nonlinear approaches is more sensitive to the proportion change of mixed pixels in a study area. The linear approach, however, is more appropriate for a rough estimation, particularly with little prior knowledge of the study area.     

Detailed mapping of a salt farm from Landsat TM imagery using neural network and maximum likelihood classifiers: a comparison

Information on the size and distribution of various zones in a salt farm is critical to salt farm management and estimation of salt yield. The ability of neural network and maximum likelihood classifiers to classify spectrally uniform water bodies with a distinct boundary in a salt farm is comparatively studied in this paper for the Taibei Salt Field, Jiangsu Province, East China using Landsat Thematic Mapper (TM) data. In a pre‐run classification of general land covers, the salt farm was mapped 84% correctly using the neural network method, slightly higher than the 76% achieved with the maximum likelihood classifier. In another separate neural network classification the salt farm was mapped further into three zones of evaporation, condensation, and crystallization at a producer's accuracy of 76%, 84%, and 86%, respectively, with the optimum classification settings. Such a detailed classification was not possible with the maximum likelihood method. It is concluded that the neural network is superior to the maximum likelihood method for detailed mapping of the Taibei Salt Field where salty water bodies are spectrally uniform and spatially extensive on the image with clear‐cut boundaries among them.     

Across-path DN gradient in Landsat TM imagery of Amazonian forests: A challenge for image interpretation and mosaicking

An analysis of tropical rain forest covering Amazonian lowlands has highlighted a systematic across-path, east–west radiometric gradient within Landsat TM imagery. Visual assessment of 45 and quantitative analysis of 20 Amazonian Landsat-4 and -5 TM scenes show that the gradient is band dependent and pronounced in visible light bands 1 to 3 but significant also in IR bands 4 to 7. The results show that the scan line location of a pixel explains a considerable amount of the DN variation of forests in the width of the entire scene (B1: 70%, B2: 52%, B3: 44%, B4: 34%, B5: 46%, B7: 39%). In digital numbers, the difference between east and west side of a scene may be small (9, 4, 3, 10, 9, 3, respectively) but these differences become significant if the images are to be mosaicked, or the data are used for mapping relatively subtle differences of natural forests. Apparently, the gradient is a result of at least three factors: 1) shadows caused by the undulating terrain, 2) anisotropic reflectance of the varying surfaces, and 3) atmospheric scattering. The phenomenon becomes more significant when the sun is high and the scanning line is close to solar azimuth direction—a condition more easily encountered in lower latitudes of the earth.     

On the estimation of biomass of submerged vegetation using Landsat thematic mapper (TM) imagery: A case study of the Honghu Lake,PR China

The visible and near infrared bands of the Landsat thematic mapper (TM) were used in an empirical assessment of submerged vegetation biomass in Honghu Lake in the middle reaches of the Yangtse River, in the People's Republic of China. The method used here was based on eigenvector rotation of the four bands to enhance submerged vegetation biomass variations. Field measurement of spectral reflectance of submerged vegetation was taken for various biomass and vegetation types to determine the possibility of estimating submerged vegetation biomass using remote sensing. The locations of sample points were determined by global positioning system (GPS) and field biomass was obtained at the same time as the TM image. Regression analyses were performed between the principal components and biomass, and a marked linear relationship between submerged vegetation biomass and first two principal components (PC) was revealed. This was used to determine the total biomass of submerged vegetation.     

Landsat TM analysis of fracture patterns: a case study from the Coastal Cordillera of northern Chile

Cretaceous rocks on the continental margin of northern Chile record a complex geodynamic evolution. Cycles of transtensional and transpressional deformation and of extrusive and intrusive magmatism are linked to the development of crustal-scale lineaments. The Landsat Thematic Mapper is used here as a tool to define these structural features. Geocorrected data were digitally enhanced and lineaments plotted directly from a hard copy image, thereby excluding artificial or non-geological features that might degrade the subsequent structural analysis. The lineaments were then digitized and analysed using a Weighted Moving Average (WMA) technique to suppress noise and to enhance azimuthal variation. Statistical analysis of the data reveals three lineament populations. The first is a set of NNE-trending lineaments that belong to the margin-parallel, sinistral Atacama Fault System. The second is a series of NW-trending lineaments with a similar orientation to large-scale structures identified across the South American continental plate. The third is a widely spaced set of NE-trending lineaments. The key result of this study is that lineaments identified from remotely sensed data may have orientation patterns that differ considerably from those identified by traditional geological mapping and that full structural analysis of structurally complex crustal regions will likely be incomplete without a comprehensive analysis of remotely sensed data. Although the NW-trending structures are numerically dominant on the Landsat TM image, they are seldom recorded at map scale and are under-represented on published geological maps. Of the 275 faults marked on the published geological map sheets, 89 are N to NNE-trending and only 88 are NW-trending. By contrast, of 841 lineaments identified from the satellite image, 455 are NW-trending and 178 are N- to NNE-trending. The lack of prior recognition of the NW-trending structures means that their importance has been underestimated in reconstructions of the geodynamic evolution of the region. In addition, as major ore deposits in the region are frequently located at intersections between two fracture systems, the recognition here of the NW-trending set of structures should illuminate future mineral exploration programmes.     

Understanding variation in trophic status of lakes on the Boreal Plain: A 20 year retrospective using Landsat TM imagery

There is a critical need for spatially and temporally extensive information on the trophic status of lakes to assist in scientifically sound forest management decisions. To meet this need, this study examined the utility of Landsat TM imagery in deriving indicators of trophic status in remote and relatively undisturbed lakes on the Boreal Plain of northern Alberta. Based on data collected during a survey of lakes in 2001, normalized exoatmospheric reflectance values of the red band explained 68, 82, and 47% of the variance in chlorophyll a, turbidity, and Secchi disk depth, respectively. To understand the natural variation in trophic status in the lakes, we applied the linear regression equations to images collected during late summer (i.e., August) for 18 out of 20 years from 1984 to 2003 and performed a two-way analysis of variance to decompose the total variation into space, time, and space × time interaction factors. We found that temporal factors accounted for 10% and spatial factors accounted for 50% of the total variation in trophic status, with a minimum of 10 years and 20 lakes needed to reach stability in the contributions of these factors. This study suggests that regional factors that are external to the lake explained the majority (60%) of the variation in trophic status of the lakes.     

Land-cover classification in a moist tropical region of Brazil with Landsat Thematic Mapper imagery

This research aims to improve land-cover classification accuracy in a moist tropical region in Brazil by examining the use of different remote-sensing-derived variables and classification algorithms. Different scenarios based on Landsat Thematic Mapper (TM) spectral data and derived vegetation indices and textural images and different classification algorithms, maximum likelihood classification (MLC), artificial neural network (ANN), classification tree analysis (CTA) and object-based classification (OBC), were explored. The results indicate that a combination of vegetation indices as extra bands into Landsat TM multi-spectral bands did not improve the overall classification performance, but the combination of textural images was valuable for improving vegetation classification accuracy. In particular, the combination of both vegetation indices and textural images into TM multi-spectral bands improved the overall classification accuracy (OCA) by 5.6% and the overall kappa coefficient (OKC) by 6.25%. Comparison of the different classification algorithms indicated that CTA and ANN have poor classification performance in this research, but OBC improved primary forest and pasture classification accuracies. This research indicates that use of textural images or use of OBC are especially valuable for improving the vegetation classes such as upland and liana forest classes that have complex stand structures and large patch sizes.     

Mapping land cover change in a reindeer herding area of the Russian Arctic using Landsat TM and ETM+ imagery and indigenous knowledge

Much of Russia north of the treeline is grazed by reindeer, and this grazing has materially altered the vegetation cover in many places. Monitoring vegetation change in these remote but ecologically sensitive regions is an important task for which satellite remote sensing is well suited. Further difficulties are imposed by the highly dynamic nature of arctic phenology, and by the difficulty of obtaining accurate official data on land cover in arctic Russia even where such data exist. We have approached the problem in a novel fashion by combining a conventional multispectral analysis of satellite imagery with data on current and historical land use gathered by the techniques of social anthropology, using a study site in the Nenets Autonomous Okrug (NAO). A Landsat-7 ETM+ image from the year 2000 was used to generate a current land cover classification. A Landsat-5 TM image was used to generate a land-cover classification for 1988, taking due account of phenological differences and between the two dates. A cautious comparison of these two classifications, again taking account of possible effects of phenological differences, shows that much of the study area has already undergone a notable transformation to grass-dominated tundra, almost certainly as a result of heavy grazing by reindeer. The grazing pattern is quite heterogeneous, and may have reached unsustainable levels in some areas. Finally, we suggest that this situation is unlikely to be unique to our study area and may well be widespread throughout the Eurasian tundra zone, particularly in the west.     

Vegetation mapping of a tropical freshwater swamp in the Northern Territory,Australia: A comparison of aerial photography,Landsat TM and SPOT satellite imagery

The tropical wetland environments of northern Australia have ecological, social, cultural and economic values. Additionally, these areas are relatively pristine compared to the many other wetland environments in Australia, and around the world, that have been extensively altered by humans. However, as the remote northern coastline of Australia becomes more populated, environmental problems are beginning to emerge that highlight the need to manage the tropical wetland environments. Lack of information is currently considered to be a major factor restricting the effective management of many ecosystems and for the expansive wetlands of the Northern Territory, this is especially the case, as these areas are generally remote and inaccessible. Remote sensing is therefore an attractive technique for obtaining relevant information on variables such as land cover and vegetation status. In the current study, Landsat TM, SPOT (XS and PAN) and large-scale, true-colour aerial photography were evaluated for mapping the vegetation of a tropical freshwater swamp in Australia's Top End. Extensive ground truth data were obtained, using a helicopter survey method. Fourteen cover types were delineated from 1:15 000 air photos (enlarged to 1:5000 in an image processing system) using manual interpretation techniques, with 89% accuracy. This level of detail could not be extracted from any of the satellite image data sets, with only three broad land-cover types identified with accuracy above 80%. The Landsat TM and SPOT XS data provided similar results although superior accuracy was obtained from Landsat, where the additional spectral information appeared to compensate in part for the coarser spatial resolution. Two different classification algorithms produced similar results.     

Comparison of data gap-filling methods for Landsat ETM+ SLC-off imagery for monitoring forest degradation in a semi-deciduous tropical forest in Mexico

A number of methods to overcome the 2003 failure of the Landsat 7 Enhanced Thematic Mapper (ETM+) scan-line corrector (SLC) are compared in this article in a forest-monitoring application in the Yucatan Peninsula, Mexico. The objective of this comparison is to determine the best approach to accomplish SLC-off image gap-filling for the particular landscape in this region, and thereby provide continuity in the Landsat data sensor archive for forest-monitoring purposes. Four methods were tested: (1) local linear histogram matching (LLHM); (2) neighbourhood similar pixel interpolator (NSPI); (3) geostatistical neighbourhood similar pixel interpolator (GNSPI); and (4) weighted linear regression (WLR). All methods generated reasonable SLC-off gap-filling data that were visually consistent and could be employed in subsequent digital image analysis. Overall accuracy, kappa coefficients (κ), and quantity and allocation disagreement indices were used to evaluate unsupervised Iterative Self-Organizing Data Analysis (ISODATA) land-cover classification maps. In addition, Pearson correlation coefficients (r) and root mean squares of the error (RMSEs) were employed for estimates agreement with fractional land cover. The best results visually (overall accuracy > 85%, κ < 9%, quantity disagreement index < 5.5%, and allocation disagreement index < 12.5%) and statistically (r > 0.84 and RMSE < 7%) were obtained from the GNSPI method. These results suggest that the GNSPI method is suitable for routine use in reconstructing the imagery stack of Landsat ETM+ SLC-off gap-filled data for use in forest-monitoring applications in this type of heterogeneous landscape.     

Mapping soil moisture in the central Ebro river valley (northeast Spain) with Landsat and NOAA satellite imagery: a comparison with meteorological data

This paper analyses and maps the spatial distribution of soil moisture using remote sensing: National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) and Landsat-Enhanced Thematic Mapper (ETM+) images. The study was carried out in the central Ebro river valley (northeast Spain), and examines the spatial relationships between the distribution of soil moisture and several meteorological and geographical variables following a long, intense dry period (winter 2000). Soil moisture estimates were obtained using thermal, visible and near-infrared data and by applying the ‘triangle method’, which describes relationships between surface temperature (T_s ) and fractional vegetation cover (F_r ). Low differences were found between the soil moisture estimates obtained using AVHRR and ETM+ sensors. Soil moisture estimated using remote sensing is close to estimations obtained from climate indices. This fact, and the high similarity between estimations of both sensors, suggests the reasonable reliability of soil moisture remote sensing estimations. Moreover, in estimations from both sensors the spatial distribution of soil moisture was largely accounted for by meteorological variables, mainly precipitation in the dry period. The results indicate the high reliability of remote sensing for determining areas affected by water deficits and for quantifying drought intensity.     

Evaluation of pre/post-fire differenced spectral indices for assessing burn severity in a Mediterranean environment with Landsat Thematic Mapper

In this study several pre/post-fire differenced spectral indices for assessing burn severity in a Mediterranean environment are evaluated. GeoCBI (Geo Composite Burn Index) field data of burn severity were correlated with remotely sensed measures, based on the NBR (Normalized Burn Ratio), the NDMI (Normalized Difference Moisture Index) and the NDVI (Normalized Difference Vegetation Index). In addition, the strength of the correlation was evaluated for specific fuel types and the influence of the regression model type is pointed out. The NBR was the best remotely sensed index for assessing burn severity, followed by the NDMI and the NDVI. For this case study of the 2007 Peloponnese fires, results show that the GeoCBI–dNBR (differenced NBR) approach yields a moderate–high R ²?=?0.65. Absolute indices outperformed their relative equivalents, which accounted for pre-fire vegetation state. The GeoCBI–dNBR relationship was stronger for forested ecotypes than for shrub lands. The relationship between the field data and the dNBR and dNDMI (differenced NDMI) was nonlinear, while the GeoCBI–dNDVI (differenced NDVI) relationship appeared linear.     

Comparison of four task-based measurement indices with full-shift dosimetry in a complicated noise environment

This study was conducted to verify the agreement between four task-based measurement indices (TBMs) and full-shift dosimetry in a complicated noise environment. The study involved six production lines and 63 fixed jobs from an automobile wheel manufacturer. The subjects were simultaneously measured by the TBMs and a personal dosimeter, and 158 measurements were completed in total. There were two methods for measuring the level-at-task: average dosimetry noise level (ADL) and direct measure noise level (DML), and two methods for measuring time-at-task: worker diary (WD) and observation diary (OD). As for the differences, Pearson correlation coefficients, paired-samples t-tests, scatter and Bland–Altman plots were undertaken to assess the agreement between TBMs and the dosimeter. The results indicated that the TBMs agreed well with the personal dosimeter; the differences between them ranged from 0.16 to 3.07 dBA. The DML of level-at-task was less than the ADL result of 3.39 dBA and using the DML could cause a systematic error. The results showed that the TBMs from WD were as accurate as the TBMs from OD, and the WD recorded 88% of the task transitions of OD. Our research suggests that the TBMs, which uses ADL and OD, can be a reliable and more feasible as a cost effective strategy for assessing the full-shift noise exposures in practice. The study showed a high degree of agreement between TBM and dosimetry in fixed jobs and complicated noise environments. However it is not clear how well the agreement between TBM and dosimetry is in mobile jobs, and thus requires further studies to assess these environments.     

Study on the cooling effects of urban parks on surrounding environments using Landsat TM data: a case study in Guangzhou,southern China

The temperature cooling effects of ten urban parks on surrounding environments in Guangzhou, southern China, are analysed and quantified using Landsat Thematic Mapper data. The results show that there is a temperature rise (about 1.74°C) between green spaces of parks and bare-ground areas of the surroundings. For those parks whose green area percentage is more than 69% and length:width ratio is close to 1, the average temperature differences between boundaries and surrounding sites of parks have linear relationships with the green areas of parks (R ² > 0.82). Moreover, the nonlinear relationship between the average cooling distance of parks and green areas can be simulated very well using a logarithmic curve (R ² > 0.93). When the green areas of parks are smaller than 10 566 m², parks will have no temperature cooling effects on their surrounding environments. When the green areas of parks reach 740 000 m², the increase of temperature cooling distance is less than 1 m per 10 000 m² increase of the green area. The most appropriate size of green areas of urban parks should fall between 10 566 and 740 000 m². For those parks with water areas larger than 128 889 m², the temperature cooling effects are usually more remarkable. When the length:width ratios of the green areas of urban parks are more than or equal to 2, their temperature cooling distances are always larger than those with length:width ratios equal to 1 given similar green area. Parks with larger green areas (37 163 m²) or larger water areas (>128 889 m²) will have more significant temperature cooling effects in June than in October.