Mapping urban change in the UK using satellite radar interferometry

A multitemporal sequence of ERS interferometric coherence data acquired between 1993 and 1999 are utilised for automatically mapping urban change within South Wales, UK. Validation of the change map derived from the coherence data is performed using independent, multidate, digital survey data of the city of Cardiff, UK. All major building developments that have occurred within the study area are located. There is evidence to suggest that this approach is generalisable for a wide range of coherence data and to other regions with similar landscapes.     

Climatological,annual, and seasonal variability in chlorophyll concentration in the Gulf of Mexico,western Caribbean,and Bahamas using NASA colour maps

This study investigates climatological, seasonal, and interannual variability in chlorophyll concentration (Chl) throughout the Gulf of Mexico (GM), the western Caribbean Sea (WC), and the Bahamas (BI). For this purpose, Coastal Zone Color Scanner (CZCS) (1979–1986), Ocean Color and Temperature Scanner (OCTS) (1996–1997), and Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) (1997–2008) NASA climatology, yearly, and seasonal Level-3 Standard Mapped Image series were used. Inspection of the original Chl and the obtained fuzzy unsupervised classified maps show the existence of a transition zone between the already known coastal and open waters. The extension (total number of pixels) and form of polygons representing these water masses vary both annually and seasonally, showing their greatest differences during spring and autumn in the northeastern and northwestern GM, Campeche Sound, and the Honduras coast in the Caribbean. In contrast, the BI present polygons having an almost invariant extension and form. The seasonal averaged Chl values up to 0.8 mg m^?3 present a cyclic variation, showing the highest Chl during winter months and the lowest Chl during summer months, independent of the basin or the sensor under consideration. The CZCS and OCTS products must be considered with care; however, they provide results that are compatible with findings from the SeaWiFS time series. Annual and winter/autumn trends – a decrease in Chl – were identified in the GM and BI. The Caribbean reports constant Chl values during the two periods under study. Possible interpretations of these trends will come from detailed interpretation of local data.     

Mapping of jasperoid in the Cedar Mountains,Utah, U.S.A., using imaging spectrometer data

Jasperoid, an important indicator of disseminated gold deposits, is mapped in the Cedar Mountains, Utah U.S.A. using data gathered by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS). Ratio analysis is employed to detect the sharp rise in reflectance between 1000 nm and 1700 nm in the jasperoid spectrum due to iron absorption at about 900 nm in association with quartz and bound water. A pre-requisite is to remove the effects of dead and senescent vegetation from the ratio image. This is achieved using a mask of dead vegetation created by absorption band-depth analysis of ligno-cellulose absorptions in the Short-Wave Infra-red (SWIR). The absorption feature at about 2080 nm was used, but this choice is partly dictated by the limestone host rocks which exhibit a strong absorption feature which overlaps with the longer wavelength lingo-cellulose absorptions. Validation of the ratio map is accomplished by overlaying a geologic boundary which defined an area of jasperoid in the field.     

Detection of forest change in the Green Mountains of Vermont using Multispectral Scanner data

Abstract

This study evaluates the potential of Landsat Multispectral Scanner (MSS) data to monitor long term changes in high-elevation coniferous forests. August 1973 and 1984 MSS data from the Green Mountains of Vermont were used. Following co-registration and standardization of data sets, colour density slices of 0.75 μm and 0·95 μm band difference images were produced. False colour composites using either (a) the 1973 0·65 μm and 0·75 μm band in conjunction with the 0.75 μm band difference data set or (b) the 1973 0·65 μm and 0·95 μm band in conjunction with the 0.95 μm band difference data set, were also produced. These images indicated that major reflectance decreases occurred for the near-infrared bands between 1973 and 1984 for much of the high-elevation forests. This includes the west-facing slope of Camels Hump, an area for which long term records documenting decreases in basal area and inferred biomass are available. It is believed that the reflectance decreases noted for the high-elevation coniferous forests are related to the forest decline process and are associated with reduction in green leaf biomass and/or the increased levels of mortality and higher amounts of dead branches accompanying this reduction in biomass.     

Mapping intertidal eelgrass (Zostera marina L.) in three coastal estuaries of the Pacific Northwest USA using false colour near-infrared aerial photography

This study describes a hybrid technique of digitally classifying aerial photography used for mapping the intertidal habitat of eelgrass (Zostera marina L.) in Pacific Northwest USA estuaries. The large tidal range (2–3 m) in this region exposes most of this seagrass community at low tide, permitting the use of false colour near-infrared film that provides good contrast in imaging vegetated intertidal habitats. Three Oregon coastal estuaries in the USA were surveyed in 2004 or 2005 at photoscales of 1:10 000 or 1:20 000. Spatial resolution was 0.25 m and the minimum mapping unit was 2.5 × 2.5 m. Spatial accuracy was within 1.5 m. Comparison of the image classification and ground survey results yielded overall classification accuracies of 83% to 97%. This appears to be an effective technique for mapping intertidal eelgrass distributions in turbid coastal estuaries with large tidal ranges.     

Mapping defoliation by leaf-cutting ants Atta species in Eucalyptus plantations using the Sentinel-2 sensor

ABSTRACT

Leaf-cutting ants are one of the most important pests of eucalyptus plantations and it is essential to monitor and control these insects to avoid damage to wood production. The objective of this research was to assess the utility of the multispectral sensor, Sentinel-2 (S-2) in mapping defoliation caused by leaf-cutting ants in Eucalyptus plantations. The study was carried out in Eucalyptus plantations in Telêmaco Borba, Paraná state, Brazil. The leaf-cutting ant nests were located, measured and georeferenced. Multispectral images from bands 2, 3, 4 and 8 of the S-2 satellite and four vegetation indices (VIs) were tested in predicting and mapping leaf-cutting ant defoliation. From each image scene the reflectance values were extracted and a partial least squares discriminant analysis (PLS-DA) was performed. The predictive variables, included in the PLS-DA regression, were selected based on the calculation of the variable importance in the projection (VIP). The accuracy of the mapping was 84.5% using six main components, demonstrating a satisfactory approach to operationally classifying canopies of Eucalyptus stands attacked by leaf-cutting ants. The study indicates the potential of freely available medium resolution Multispectral instrument (MSI) S-2 images, to accurately detect and map defoliation caused by Atta species in commercial forest plantations.     

Analysing environmental changes in the neighbourhood of mines using compressed change vector analysis: case study of Carajas Mountains,Brazil

Remote sensing image databases and geographic information system have the potential to act as accurate tools for environmental monitoring. Carajas Mountains are an important mineral deposit in Brazil and as environmental laws protect a great portion of this region, they have been at the core of conflicts involving human and nature interests. The biggest mining project in Brazil is active in this region (Carajas project) and this analysis aims at identifying the environmental impact caused directly or indirectly by this activity using state-of-the-art methods. This study collects information of land-use and land-coverage from an area larger than 111,000 km² including five municipalities, aiming at observing the landscape intervention from a large-scale perspective as a counterpoint to other studies which are focused on a particular region, such as watersheds. Therefore, employing the resultant products of the multispectral approach called Compressed change vector analysis analyses both the environmental changes in each studied municipality of the Carajas Mountains and the environmental counterpart of the company that runs the mining activity. The results show that in general the vegetative coverage was replaced by pasture lands, which in turn were replaced by urban occupations. The comparison with official statistics indicates good accuracy of the present study in the estimation of vegetative cover, although the authors claim that the official methodology can produce inaccurate percentages, probably due to the shortcoming of classification of degraded forests and forest in regeneration process. The presence of environmentally protected areas has prevented the increase of deforestation in the mountains, in which the observed change rates were at least 15% lower than non-protected regions.     

Mapping Hurricane Katrina's widespread destruction in New Orleans using multisensor data and the normalized difference change detection (NDCD) technique

This paper introduces a new and general method for change detection based on the normalized difference change detection (NDCD) technique. A case study shows the use of the NDCD technique for flood mapping. Flood maps for the city of New Orleans (Louisiana, USA) resulting from the passage of Hurricane Katrina in 2005 were produced from the data processing of Satellite Pour l‘Observation de la Terre (SPOT)-4 High-Resolution Visible Infra-Red (HRVIR) and Landsat-5 Thematic Mapper (TM) images and the accuracies of the maps were verified using as ground truth the flood extension map of the city of New Orleans produced at the Dartmouth Flood Observatory (Dartmouth College, USA). The potentialities and performances of the NDCD technique in flood mapping were also compared to other standard change detection methods such as: (i) the near-infrared (NIR) normalized difference, (ii) unsupervised post-classification comparison, (iii) change vector analysis (CVA) and (iv) spectral–temporal minimum noise fraction (STMNF) transformation. When using the SPOT-4 HRVIR data, the NDCD technique led to better results than the other change detection methods considered here, while for the Landsat-5 TM data processing the closeness of the post-flood image to the Katrina landfall influenced the overall performances negatively. However, with respect to flood mapping in the urban area alone, which may be of major interest in most cases, the NDCD technique also performed better than all the other change detection methods considered here when using the Landsat-5 TM data.     

Robert B. Horn (illustrator), Jeff Yoshimi,Mark Deering,Russ McBride,David Fleischman (illustrator), Thierry DiDonna (illustrator), Jennifer Wedel (Editor), Mapping Great Debates. Can Computers Think?: 7 Maps and a Handbook.

Minds and Machines -     

Binary images in seasonal land-cover change identification: a comparative study in parts of New South Wales,Australia

Numerous land-cover change detection techniques have been developed with varying opinions about their appropriateness and success. Decisions on the selection of the most suitable change detection method is often influenced by the study region landscape complexity and the type of data used for analysis. For different climatic areas, the method that suits best the seasonal land-cover change identification remains uncertain. In this study, 11 different binary change detection methods were tested and compared with respect to their capability in detecting land-cover change/no-change information in different seasons. The methods include image differencing (I_Diff), Improved image differencing (Imp_Diff), principal component image differencing (PC_Diff), vegetation index differencing (VI_Diff), change vector analysis (CVA), image ratioing (IR), improved image ratioing (Imp_IR), vegetation index image ratioing (VI_R), multi-date principal component analysis (PCA) using all bands (M_PCA), two-date bands PCA (B_PCA), and two-date vegetation index images PCA (VI_PCA). Multi-Date Thematic Mapper (TM) data were used for a wide set of change image generation. A relatively new approach was applied for optimal threshold value determination for the separation of change/no-change areas. Research results indicated that any methods involving TM Band 4 performed better than those using TM Band 3 or 5 on each of the change periods. However, irrespective of the method used, the accuracy assessment and change/no-change validation results from normalized difference vegetation index (NDVI)-based techniques outperformed all other tested techniques in the change detection process (overall accuracy >90% and kappa value >0.85 for all six change periods). The image differencing technique was found to be marginally better than PCA and IR in most cases and any of these techniques can be used for change detection. However, because of the simplistic nature and relative ease in identifying both negative and positive changes from difference images, the NDVI differencing technique is recommended for seasonal land-cover change identification in the study region.     

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.     

Mapping population change index in Southern Serbia (1961-2027) as a function of environmental factors

Niche analysis methods developed within the biogeography community are routinely used for species distribution modeling of wildlife and endangered species. So far, such techniques have not been used to explain distribution of people in an area, nor to assess spatio-temporal dynamics of human populations. In this paper, the MaxEnt approach to species distribution modeling and publicly available gridded predictors were used to analyze the population dynamics in Southern Serbia (South Pomoravlje Region) for the period 1961-2027. Population values from the census administrative units were first downscaled to 200 m grid using a detailed map of populated places and dasymetric interpolation. In the second step, a point pattern representing the whole population (468,500 inhabitants in 2002) was simulated using the R package spatstat. MaxEnt was then used to derive habitat suitability index (HSI) as a function of gridded predictors: distance to roads, elevation, slope, topographic wetness index, enhanced vegetation index and land cover classes. HSI and environmental predictors were further used to explain spatial patterns in the population change index (PCI) through regression modeling. The results show that inhabiting preference for year 1961 is mainly a function of topography (TWI, elevation). The HSI for year 2027 shows that large portions of remote areas are becoming less preferred for inhabiting. The results of cross-validation in MaxEnt show that distribution of population is distinctly controlled by environmental factors (AUC > 0.84). Population decrease is particularly significant in areas >25 km distant from the main road network. The results of regression analysis show that 40% of variability in the PCI values can be explained with these environmental maps, distance to roads and urban areas being the main drivers of migration process. This approach allows precise mapping of demographic patterns that otherwise would not be visible from the census data alone.     

Correlated change in normalized difference vegetation index and the seasonal trajectory of photosynthetic capacity in a conifer stand

Seasonal changes in canopy photosynthetic activity play an important role in carbon assimilation. However, few simulation models for estimating carbon balances have included them due to scarcity in quality data. This paper investigates some important aspects of the relationship between the seasonal trajectory of photosynthetic capacity and the time series of a common vegetation index (normalized difference vegetation index, NDVI), which was derived from on site micrometeorological measurements or smoothed and downscaled from satellite‐borne NDVI sensors. A parameter indicating the seasonality of canopy physiological activity, P _E, was retrieved through fitting a half‐hour step process model, PROXEL_NEE, to gross primary production (GPP) estimates by inversion for carboxylation and light utilization efficiencies. The relative maximum rate of carboxylation (V _rm), a parameter that indicates the seasonality of CO₂ uptake potential under prevailing temperature, was then calculated from P _E and daily average air temperature. Statistical analysis revealed that there were obvious exponential relationships between NDVI and the seasonal courses for both canopy physiological activities P _E and V _rm. Among them, the on‐site broadband NDVI provided a robust and consistent relationship with canopy physiological activities (R ² = 0.84). The relationships between satellite‐borne NDVI time series with instantaneous canopy physiological activities at the time of satellite passing were also checked. The results indicate that daily step NDVI time series (data downscaled from composite temporal resolution NDVI) better represent the daily average activity of the canopy. These findings may enable us to retrieve the seasonal course of canopy physiological activity from widely available NDVI data series and, thus, to include it into carbon assimilation models. However, both smoothing methods for satellite‐borne NDVI time series may generate incorrect estimates and must be treated with care.     

Hourly simulations of the microwave brightness temperature of seasonal snow in Quebec, Canada, using a coupled snow evolution-emission model

To interpret the snowpack evolution, and in particular to estimate snow water equivalent (SWE), passive microwave remote sensing has proved to be a useful tool given its sensitivity to snow properties. However, the main uncertainties using existing SWE algorithms arise from snow metamorphism which evolves during the winter season, and changes the snow emissivity. To consider the evolution in snow emissivity a coupled snow evolution-emission model can be used to simulate the brightness temperature (T_B) of the snowpack.During a dedicated campaign in the winter season, from November to April, of 2007-2008 two surface-based radiometers operating at 19 GHz and 37 GHz continuously measured the passive microwave radiation emitted through a seasonal snowpack in southern Quebec (Canada). This paper aims at modeling and interpreting this time series of T_B over the whole season, with an hourly step, using a coupled multi-layer snow evolution-emission model. The thermodynamic snow evolution model, referred as to Crocus, was driven by local meteorological measurements. Results from this model provided, in turn, the input variables to run the Microwave Emission Model of Layered Snowpacks (MEMLS) in order to predict T_B at 19 GHz and 37 GHz for both vertical (V) and horizontal (H) polarizations. The accuracy of T_B predicted by the Crocus-MEMLS coupled model was evaluated using continuous measurements from the surface-based radiometers.The weather conditions observed during the winter season were diverse, including several warm periods with melting snow and rain-on-snow events, producing very complex variations in the time series of T_B. To aid our analysis, we identified days with melting snow versus days with dry snow. The Crocus-MEMLS coupled model was able to accurately predict melt events with a success rate of 86%. The residual error was due to an overestimation of the duration of several melt events simulated by Crocus. This problem was explained by 1) a limitation of percolation, and 2) a very long-acting melt of lower layers due to geothermal flux.When the snowpack was completely dry, the global trend of T_B during the season was characterized by a decrease of T_B due to growth in the snow grain size. During most of the season, Crocus-MEMLS correctly predicted the evolution of T_B resulting from temperature gradient metamorphism; the root mean square errors ranged between 2.8 K for the 19 GHz vertical polarization (19V) and 6.9 K for the 37 GHz horizontal polarization (37H). However, during dry periods near the end of the season, the values of T_B were strongly overestimated. This overestimation was mainly due to a limitation of the growth of large snow grains in the wet snowpack simulated by Crocus. This effect was confirmed by estimating snow grain sizes from the observed T_B and the coupled model. The estimated snow grain sizes were larger and more realistic than those initially predicted by the Crocus model.     

Gross primary production simulation in a coniferous forest using a daily gas exchange model with seasonal change of leaf physiological parameters derived from remote sensing data

The importance of including the seasonal changes of canopy physiology in carbon balance simulation models was emphasized in earlier research. This paper proposes a new approach to combine the commonly available Normalized Difference Vegetation Index (NDVI) to derive the seasonal changes of canopy physiology with a modified daily step gas exchange model to simulate the gross primary production (GPP) in a coniferous forest stand. For validation, four years (1997–2000) of continuous time‐series data for GPP were derived using the observations of net ecosystem carbon dioxide exchange obtained with eddy covariance techniques. A variety of scenarios were designed to simulate the GPP with the daily model and the results (1) reaffirm the importance of including seasonal changes of canopy physiology in the carbon balance model; (2) demonstrate the feasibility of deriving the seasonal change information of canopy physiology from NDVI time‐series; (3) show the importance of the quality of NDVI time‐series. The results suggest that the daily model can be linked with remote sensing data, which provides information on the seasonal changes of canopy physiology, and can be an efficient tool for regional carbon balance simulation.     

Mapping snow cover in the pan-Arctic zone,using multi-year (1998-2001) images from optical VEGETATION sensor

Timely information on spatial distribution and temporal dynamics of snow cover in the pan-Arctic zone is needed, as snow cover plays an important role in climate, hydrology and ecological processes. Here we report estimates of snow cover in the pan-Arctic zone (north of 45° N) at 1-km spatial resolution and at a 10-day temporal interval over the period of April 1998 to December 2001, using 10-day composite images of VEGETATION sensor onboard Système Pour l'Observation de la Terre (SPOT)-4 satellite. The results show that snow covered area (SCA) in North America (north of 45° N) increased from 1998 to 2001, while SCA in Eurasia (north of 45° N) decreased from 1998 to 2000 but increased in 2001. There were large spatial and temporal variations of snow cover in the pan-Arctic zone during 1998-2001.