Mapping coral reefs at reef to reef-system scales, 10s–1000s km2, using object-based image analysis

Coral reef maps at various spatial scales and extents are needed for mapping, monitoring, modelling, and management of these environments. High spatial resolution satellite imagery, pixel <10 m, integrated with field survey data and processed with various mapping approaches, can provide these maps. These approaches have been accurately applied to single reefs (10–100 km²), covering one high spatial resolution scene from which a single thematic layer (e.g. benthic community) is mapped. This article demonstrates how a hierarchical mapping approach can be applied to coral reefs from individual reef to reef-system scales (10–1000 km²) using object-based image classification of high spatial resolution images guided by ecological and geomorphological principles. The approach is demonstrated for three individual reefs (10–35 km²) in Australia, Fiji, and Palau; and for three complex reef systems (300–600 km²) one in the Solomon Islands and two in Fiji. Archived high spatial resolution images were pre-processed and mosaics were created for the reef systems. Georeferenced benthic photo transect surveys were used to acquire cover information. Field and image data were integrated using an object-based image analysis approach that resulted in a hierarchically structured classification. Objects were assigned class labels based on the dominant benthic cover type, or location-relevant ecological and geomorphological principles, or a combination thereof. This generated a hierarchical sequence of reef maps with an increasing complexity in benthic thematic information that included: ‘reef’, ‘reef type’, ‘geomorphic zone’, and ‘benthic community’. The overall accuracy of the ‘geomorphic zone’ classification for each of the six study sites was 76–82% using 6–10 mapping categories. For ‘benthic community’ classification, the overall accuracy was 52–75% with individual reefs having 14–17 categories and reef systems 20–30 categories. We show that an object-based classification of high spatial resolution imagery, guided by field data and ecological and geomorphological principles, can produce consistent, accurate benthic maps at four hierarchical spatial scales for coral reefs of various sizes and complexities.     

Using MODIS images to examine the surface extents and variations derived from the DEM and laser altimeter data in the Danjiangkou Reservoir,China

We used publicly available digital spatial datasets to study the area extents and their horizontal variations of two water bodies within the Danjiangkou Reservoir, China. Between 2003 and 2005, the water levels varied from 140 to 149 m above mean sea level as measured by the Geoscience Laser Altimeter System (GLAS). Detailed procedures to derive the horizontal extents and variations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) coupled with GLAS data and to verify the extents and variations spatially were provided. For the water bodies on the north and west, the surface water extents derived from four MODIS images varied between 174 and 218 km² and from 96 to 135 km², respectively. The extents by inundating the DEM using the GLAS data were 178–212 km² for the water body on the north and 104–118 km² for the water body on the west. The spatial verifications of surface water extents derived from the MODIS images versus DEM coupled with GLAS data agreed 83–93%. Within the ring areas between water/land boundaries at elevations of 140 and 147 m, and 140 and 149 m, the spatial agreement was 52–75%.     

Production of Landsat ETM+ reference imagery of burned areas within Southern African savannahs: comparison of methods and application to MODIS

Accurate production of regional burned area maps are necessary to reduce uncertainty in emission estimates from African savannah fires. Numerous methods have been developed that map burned and unburned surfaces. These methods are typically applied to coarse spatial resolution (1 km) data to produce regional estimates of the area burned, while higher spatial resolution (<30 m) data are used to assess their accuracy with little regard to the accuracy of the higher spatial resolution reference data. In this study we aimed to investigate whether Landsat Enhanced Thematic Mapper (ETM+)‐derived reference imagery can be more accurately produced using such spectrally informed methods. The efficacy of several spectral index methods to discriminate between burned and unburned surfaces over a series of spatial scales (ground, IKONOS, Landsat ETM+ and data from the MOderate Resolution Imaging Spectrometer, MODIS) were evaluated. The optimal Landsat ETM+ reference image of burned area was achieved using a charcoal fraction map derived by linear spectral unmixing (k = 1.00, a = 99.5%), where pixels were defined as burnt if the charcoal fraction per pixel exceeded 50%. Comparison of coincident Landsat ETM+ and IKONOS burned area maps of a neighbouring region in Mongu (Zambia) indicated that the charcoal fraction map method overestimated the area burned by 1.6%. This method was, however, unstable, with the optimal fixed threshold occurring at >65% at the MODIS scale, presumably because of the decrease in signal‐to‐noise ratio as compared to the Landsat scale. At the MODIS scale the Mid‐Infrared Bispectral Index (MIRBI) using a fixed threshold of >1.75 was determined to be the optimal regional burned area mapping index (slope = 0.99, r ² = 0.95, SE = 61.40, y = Landsat burned area, x = MODIS burned area). Application of MIRBI to the entire MODIS temporal series measured the burned area as 10 267 km² during the 2001 fire season. The char fraction map and the MIRBI methodologies, which both produced reasonable burned area maps within southern African savannah environments, should also be evaluated in woodland and forested environments.     

Downscaling MODIS LST in the East African mountains using elevation gradient and land-cover information

Land-surface temperature (LST) is strongly affected by altitude and surface albedo. In mountain regions where steep slopes and heterogeneous land cover are predominant, LST can vary significantly within short distances. Although remote sensing currently provides opportunities for monitoring LST in inaccessible regions, the coarse resolution of some sensors may result in large uncertainties at sub-pixel scales. This study aimed to develop a simple methodology for downscaling 1 km Moderate Resolution Spectroradiometer (MODIS) LST pixels, by accounting for sub-pixel LST variation associated with altitude and land-cover spatial changes. The approach was tested in Mount Kilimanjaro, Tanzania, where changes in altitude and vegetation can take place over short distances. Daytime and night-time MODIS LST estimates were considered separately. A digital elevation model (DEM) and normalized difference vegetation index (NDVI), both at 250 m spatial resolution, were used to assess altitude and land-cover changes, respectively. Simple linear regressions and multivariate regressions were used to quantify the relationship between LST and the independent variables, altitude and NDVI. The results show that, in Kilimanjaro, altitude variation within the area covered by a 1 km MODIS LST pixel can be up to ±300 m. These altitude changes can cause sub-pixel variation of up to ±2.13°C for night-time and ±2.88°C for daytime LST. NDVI variation within 1 km pixels ranged between –0.2 and 0.2. For night-time measurements, altitude explained up to 97% of LST variation, while daytime LST was strongly affected by land cover. Using multivariate regressions, the combination of altitude and NDVI explained up to 94% of daytime LST variation in Kilimanjaro. Finally, the downscaling approach proposed in this study allowed an improved representation of the influence of landscape features on local-scale LST patterns.     

Tracking bamboo dynamics in Zhejiang,China, using time-series of Landsat data from 1990 to 2014

Bamboo is an important vegetation type and provides a number of critical ecosystem services. Reliable and consistent information on bamboo distribution is required to better estimate its effect on climate change mitigation and socio-economic development. However, such information is rare over a large spatial area. In this study, we evaluate the contribution of different features in the identification of bamboo stands and determine a more discriminative set of features. We propose a bamboo mapping system including feature extraction and feature selection and derive the long-term trends of bamboo distribution in Zhejiang Province, China, using time-series of Landsat data from 1990 to 2014, with an increment of 5 years (1990, 1995, 2000, 2005, 2010, and 2014). The resultant maps of bamboo in the six epochs were evaluated using independent validation samples. The overall accuracies (OAs) of all six epochs range from 85.9% to 90.7%. We found that bamboo distribution in Zhejiang substantially increased from 1990 to 2014, particularly during the 2000s. Based on the produced maps, the area of bamboo in this region increased from 5363 ± 490 km² in 1990 to 11671 ± 653 km² in 2014, which is consistent with the National Forest Resource Inventory (NFRI) data. Our study demonstrates the capability of time-series of Landsat data for continuous monitoring of bamboo at a large spatial scale.     

Development of a sub-pixel analysis method applied to dynamic monitoring of floods

Traditional ‘in situ’ measurement techniques often fail to record the spatial distribution of floodplains. In that case, remote sensing provides inexpensive and reliable methodologies to map flooded areas and compute flood damage. The identification and monitoring of floods, due to their highly dynamic nature, require the use of high-time-resolution satellite images with the drawback that such images usually have low to medium spatial resolution. In this context, the traditional classification techniques would not be suitable for delineating floods because they use ‘hard methods’ of classification, where the coarse pixel is assigned to a unique land cover class, generating inaccurate maps of the flooded area. In contrast, the ‘soft methods’ assign several land cover classes within the coarse pixels. In this article, the theoretical basis regarding an innovative methodology of sub-pixel analysis (SA) to identify flooded areas is developed. The improvement in flood delineation is achieved with the use of primary topographic attributes, which stem from a digital elevation model (DEM). The methodology was applied to the monitoring of flood events in the lower Senegal River Valley, using satellite images with moderate spatial resolution. The proposed methodology was demonstrated to be effective for mapping the flood extent: the correct mapping of flooded areas was about 80% in all considered regions, whilst the better performance of supervised classification was 53%.     

Quantifying scaling effects on satellite‐derived forest area estimates for the conterminous USA

We quantified the scaling effects on forest area estimates for the conterminous USA using regression analysis and the National Land Cover Dataset 30 m satellite‐derived maps in 2001 and 1992. The original data were aggregated to: (1) broad cover types (forest vs. non‐forest); and (2) coarser resolutions (1 km and 10 km). Standard errors of the model estimates were 2.3% and 4.9% at 1 km and 10 km resolutions, respectively. Our model improved the accuracies for 1 km by 0.6% (12 556 km²) in 2001 and 1.9% (43 198 km²) in 1992, compared to the forest estimates before the adjustments. Forest area observed from Moderate Resolution Imaging Spectroradiometer (MODIS) 2001 1 km land‐cover map for the conterminous USA might differ by 80 811 km² from what would be observed if MODIS was available at 30 m. Of this difference, 58% (46 870 km²) could be a relatively small net improvement, equivalent to 1444 Tg (or 1.5%) of total non‐soil forest CO₂ stocks. With increasing attention to accurate monitoring and evaluation of forest area changes for different regions of the globe, our results could facilitate the removal of bias from large‐scale estimates based on remote sensors with coarse resolutions.     

Waterline mapping at the subpixel scale from remote sensing imagery with high‐resolution digital elevation models

Subpixel mapping technology is a promising method of increasing the spatial resolution of the classification results derived from remote sensing imagery. However, for waterline mapping problems, the traditional spatial dependence principle of subpixel mapping is not suitable as the water flow is always controlled by the topography. This letter presents a novel algorithm based on a high spatial resolution digital elevation model (DEM) to address the subpixel waterline mapping problem. The waterline was mapped at the subpixel scale with a proposed rule according to the physical features of the water flow and additional information provided by the DEM. The method was evaluated with degraded real remotely sensed imagery at different spatial resolutions. The results show that the proposed method can provide more accurate classifications than the traditional subpixel mapping method. Moreover, the fine spatial resolution DEM can be used as feasible supplementary data for subpixel waterline mapping from coarser spatial resolution imagery.     

DEM generation using Ziyuan-3 mapping satellite imagery without ground control points

ABSTRACT

This article proposes a digital elevation model (DEM) generation approach using the Shuttle Radar Topography Mission (SRTM) DEM as the elevation constraint without ground control points. First, during the process of image block adjustment, we took advantage of the relatively high vertical accuracy of the SRTM-DEM in flat terrain regions and applied effective constraints on the object-space elevation-corrected value of tie points using the SRTM-DEM, achieving improved vertical accuracy for large-scale block adjustments. Subsequently, for the DEM matching process, multiple two-linear array stereo image pairs were obtained from along-track and across-track images with different look angles over the same region after the block adjustment. Then, the matching result of each stereo image pair underwent weighted fusion, before being used to generate the final DEM product. This approach can effectively enhance the matching quality and grid density of the final DEM product. The DEM generation experiment, using Ziyuan-3 images covering 186,000 km² of Hubei Province, China, showed that the matching quality of the 10 m grid DEM was excellent. The vertical root mean square errors were 1.5 m in the flat regions and 2.96 m in the mountainous regions, thus achieving China’s 1:25,000 scale specification requirement for DEM products.     

Mapping urban areas on a global scale: which of the eight maps now available is more accurate?

Eight groups from government and academia have created 10 global maps that offer a ca 2000 portrait of land in urban use. Our initial investigation found that their estimates of the total amount of urban land differ by as much as an order of magnitude (0.27–3.52 ×10⁶ km²). Since it is not possible for these heterogeneous maps to all represent urban areas accurately, we undertake the first global accuracy assessment of these maps using a two-tiered approach that draws on a stratified random sample of 10 000 high-resolution Google Earth validation sites and 140 medium-resolution Landsat-based city maps. Employing a wide range of accuracy measures at different spatial scales, we conclude that the new MODIS 500 m resolution global urban map has the highest accuracy, followed by a thresholded version of the Global Impervious Surface Area map based on the Night-time Lights and LandScan datasets.     

Mapping geological features of the Jharia coalfield from Landsat-5 TM data

False Colour Composite 754, the principal component imagery and edge enhanced imagery of Landsat-5 TM data have been found useful in delineating the regional geological features, viz, the curvilinear coal bands and sandstone ridges, faults, fractures and dolerite dykes of the Jharia Coalfield of Bihar, India, covering an area of about 480 km², where many parts are inaccessible to direct field mapping due to extensive opencast mining operations. The features, their occurrences and spatial dispositions, as deciphered from satellite data, are found to match significantly well with selective field maps.     

Multitemporal assessment of the geomorphologic evolution of the Restinga of Marambaia,Rio de Janeiro,Brazil

The Restinga of Marambaia is an emerged sand bar located between the Sepetiba Bay and the South Atlantic Ocean, on the south‐east coast of Brazil. The objective of this study was to observe the geomorphologic evolution of the coastal zone of the Restinga of Marambaia using multitemporal satellite images acquired by multisensors from 1975 to 2004. The images were digitally segmented by a region growth algorithm and submitted to an unsupervised classification procedure (ISOSEG) followed by a raster edit based on visual interpretation. The image time‐series showed a general trend of decrease in the total sand bar area with values varying from 80.61 km² in 1975 to 78.15 km² in 2004. The total area calculation based on the 1975 and 1978 Landsat MSS data was shown to be super‐estimated in relation to the Landsat TM, Landsat ETM+, and CBERS‐2 CCD data. These differences can also be associated to the relatively poorer spatial resolution of the MSS data, nominally 79 m, against the 20 m of the CCD data and 30 m of the TM and ETM+ data. For the estimates of the width in the central portion of the sand bar the variation was from 158 m (1975) to 100 m (2004). The formation of a spit in the northern region of the study area was visually observed. The area of the spit was estimated, with values varying from 0.82 km² (1975) to 0.55 km² (2004).     

Technical Note: Mapping and forecasting of North Indian winter fog: an application of spatial technologies

In India, the Indo‐Gangetic plain (part of Northern India) is invariably affected by dense fog in the winter months every year due to typical meteorological, environmental and prevailing terrain conditions. Pollution also plays an important role in the formation of fog (smoke+fog = smog) in India. Using National Oceanic and Space Administration‐advanced very high resolution radiometer data the fog‐affected regions in Northern India were delineated and the spatial extent of fog for the winter months of the years 2002–03, 2003–04 and 2004–05 (December–February) were studied and mapped. Forecast for future fog based on the analysis of satellite and meteorological (air temperature, relative humidity and wind speed) data was also done. The fog‐affected areas were classified into maximum‐fog‐affected area, moderately fog‐affected area and least fog‐affected area. It has been found that in the winter months of the years 2002–03, 2003–04 and 2004–05, the fog‐affected area in Northern India was about 867 000 km², 625 000 km² and 706 800 km² respectively. The maximum fog‐affected area was found to be 606 400 km², the moderately fog‐affected area was found to be 230 400 km² and the least fog‐affected area was found to be 404 500 km². Further, based on meteorological parameters, such as temperature, humidity and wind speed along with elevation data was used to derive an approach for future fog prediction in this region.     

Object-based image analysis for operational fine-scale regional mapping of land cover within river corridors from multispectral imagery and thematic data

Accurate mapping of land-cover diversity within riparian areas at a regional scale is a major challenge for better understanding the influence of riparian landscapes and related natural and anthropogenic pressures on river ecological status. As the structure (composition and spatial organization) of riparian area land cover (RALC) is generally not accessible using moderate-scale satellite imagery, finer spatial resolution imagery and specific mapping techniques are needed. For this purpose, we developed a classification procedure based on a specific multiscale object-based image analysis (OBIA) scheme dedicated to producing fine-scale and reliable RALC maps in different geographical contexts (relief, climate and geology). This OBIA scheme combines information from very high spatial resolution multispectral imagery (satellite or airborne) and available spatial thematic data using fuzzy expert knowledge classification rules. It was tested over the Hérault River watershed (southern France), which presents contrasting landscapes and a total stream length of 1150 km, using the combination of SPOT (Système Probatoire d'Observation de la Terre) 5 XS imagery (10 m pixels), aerial photography (0.5 m pixels) and several national spatial thematic data. A RALC map was produced (22 classes) with an overall accuracy of 89% and a kappa index of 83%, according to a targeted land-cover pressures typology (six categories of pressures). The results of this experimentation demonstrate that the application of OBIA to multisource spatial data provides an efficient approach for the mapping and monitoring of RALC that can be implemented operationally at a regional or national scale. We further analysed the influence of map resolution on the quantification of riparian spatial indicators to highlight the importance of such data for studying the influence of landscapes on river ecological status at the riparian scale.     

The effects of sensor spatial resolution and changing grain size on fragmentation indices in urban landscape

Satellite images are now used as the primary source of spatial information for landscape pattern analysis. Choosing data of proper resolution has always been a big problem for landscape pattern analysis using satellite images. However, the effects of remote sensor spatial resolution on urban landscape fragmentation have never been reported. In this study, landscape fragmentation of Shanghai was analysed using land use maps produced from TM and IRS‐PAN images, attempting to discover the effects of remote sensor spatial resolution on urban landscape fragmentation. Four landscape fragmentation indices were computed along a 51 km long and 9 km wide transect cutting across Shanghai with a moving window to detect the behaviour of landscape indices along the gradient of land use zones. The results showed that fragmentation indices were sensitive to varied spatial resolution and grain size, indicating that fine resolution data and small grain size of about 7.5 m might be optimal for urban fragmentation analysis. The behaviour of landscape indices along the gradient of land use zones suggests that urban fragmentation can be distinguished as occurring in two phases: dissection introduced by roads and shrinkage caused primarily by urban sprawl.     

Frontal changes in the Manimahesh and Tal Glaciers in the Ravi basin,Himachal Pradesh,northwestern Himalaya (India), between 1971 and 2013

The Manimahesh and Tal Glaciers are located in the Budhil fifth-order sub-basin of the Ravi, Himachal Himalaya, Northwestern Himalaya (India). These glaciers were analysed using high- (Corona KH-4A) to medium- (Landsat TM/ETM+/OLI, ASTER) spatial resolution satellite data between 1971 and 2013, along with extensive field measurements (2011–2014) of frontal changes. The results show that the Manimahesh and Tal Glaciers retreated by 157 ± 34 m (4 ± 1 m year^–1) and 45 ± 34 m (1 ± 1 m year^–1), respectively, whereas, the total area lost is estimated at 0.21 ± 0.01 km² (0.005 km² year^–1) and 0.010 ± 0.003 km² (0.0002 km² year^–1), respectively, between 1971 and 2013. The rate of retreat is significantly lower than that previously reported. Our field measurements (2011–2014) also suggest a retreating trend and validate the measured glacier changes using remotely sensed temporal data.     

High‐resolution spatial analysis of mountain landscapes using a low‐altitude remote sensing approach

Mountain landscapes are characterized by great spatial diversity. One basic problem is that there are few high‐resolution data for secluded mountain areas. We present a new approach towards topographic mapping and vegetation monitoring: low‐altitude remote sensing using Kite Aerial Photography (KAP). The study was conducted in the Norwegian mountains above the treeline. We assessed this approach under specific alpine circumstances. Following the collection of data, we derived a digital elevation model (DEM) from two overlapping images. The model was evaluated by the statistical correlation of 265 random field points and extracted heights from (i) linear contour line interpolation of a topographic map of scale 1 : 50 000, (ii) photogrammetric analysis of kite aerial photographs, and (iii) kriging interpolation of approximately 1000 measured field points. Finally, the vegetation was classified, using both supervised and unsupervised methods. The accuracy of the classification results was evaluated by comparing 265 random points, derived from terrestrial mappings, to classified vegetation types by an error matrix. The generation of derived data compared well with data obtained from high‐resolution field surveys and was better than data derived from public‐domain government cartography and moderate‐scale satellite remote sensing data. Our results demonstrate the economic and logistic advantages of this new KAP‐based methodology. The flexibility and outstanding high resolution of our new low‐altitude remote sensing approach proved to be particularly suitable for closing the gap between terrestrial investigations and high‐altitude remote sensing. Hence, our KAP approach addresses the challenge of multiscale research in mountain landscapes.     

Accuracy assessment of the ASTER GDEM and SRTM3 DEM: an example in the Loess Plateau and North China Plain of China

The digital elevation model (DEM) produced by the Shuttle Radar Topographic Mission (SRTM) has provided important fundamental data for topographic analysis in many fields. The recently released global digital elevation model (GDEM) produced by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) has higher spatial resolution and wider coverage than the SRTM3 DEM, and thus may be of more value to researchers. Taking two typical study areas—the Loess Plateau and the North China Plain of China—as an example, this article assesses the accuracy of the SRTM3 DEM and ASTER GDEM by collecting ground control points from topographical maps. It is found that both the SRTM3 DEM and the ASTER GDEM are far more accurate for the North China Plain than for the Loess Plateau. For the Loess Plateau, the accuracy of the ASTER GDEM is similar to that of the SRTM3 DEM; whereas for the North China Plain, it is much worse than that of the SRTM3 DEM. Considering the negative bias of the ASTER GDEM for flat or gentle regions, we improve its accuracy by adding the difference of the mean value between the SRTM3 DEM and ASTER GDEM for the North China Plain; then, the root mean square error (RMSE) of ±7.95 m from the original ASTER GDEM is improved to ±5.26 m, which demonstrates that it is a simple but useful way to improve the accuracy of the ASTER GDEM in flat or gentle regions.     

Relationship between SST gradients and upwelling off Peru and Chile: model/satellite data analysis

The upwelling system off Peru/Chile is characterized by significant mesoscale to submesoscale surface variability that results from the instability of the coastal currents (due to the strong vertical and horizontal shears) and to the marked density cross-shore gradients (associated with the mean upwelling). Here we investigate to what extent upwelling intensity can be inferred from sea surface temperature (SST) derived from remote sensing. As a first step in validation, a comparison between SST observations is performed, which indicates that the 1 km gridded multi-scale ultra-high-resolution (MUR) SST data set is defining a zone of maximum SST gradients closer to shore than the low-resolution National Centers for Environmental Information 0.25° resolution data set. Two model versions, at nominal resolutions of 2 km and 4 km, of the Massachusetts Institute of Technology general circulation model are analysed. A high-resolution version at 2 km is examined for the period 13 September 2011–23 January 2013, while a 4 km version is examined for 6 March 2011–22 April 2013. MUR shows maxima SST gradients in the range of 0.03 ± 0.02 K km^?1 while the model showed higher gradients around 0.05 ± 0.02 K km^?1. Based on coherence spectra, the relationship between upwelling rate (as inferred from the vertical velocity) and SST gradient is documented in the model from intraseasonal to annual timescales. It suggests that changes in SST gradient magnitudes are related to changes in the intensity of coastal upwelling off Peru and Chile. Such a relationship between SST gradients and vertical velocity would allow for the use of satellite-derived SSTs to monitor the intensity of coastal upwelling from the intraseasonal to annual timescales.