Analysing decadal land use/cover dynamics of the Lake Basaka catchment (Main Ethiopian Rift) using LANDSAT imagery and GIS

Development of accurate classification methods for rapidly changing catchments like that of Lake Basaka is fundamental to better understanding the catchment dynamics, which were not addressed in previous studies. Thus, the aim of this study was to map the decadal land use/cover (LUC) regimes of the Lake Basaka catchment, utilizing time series of LANDSAT images and to analyse the changes that occurred at different time periods. Both unsupervised and supervised image classification systems were utilized in Earth Resources Data Analysis System (ERDAS) Imagine (9.1). Appropriate pre‐ and postprocessing also was utilized. Seven major LUC classes were identified in the final land cover maps produced after the supervised (maximum likelihood) classification exercise. The analysis results indicated the Lake Basaka catchment had experienced a drastic change in its LUC conditions over the last 4–5 decades because of rapid increases in human settlement, deforestation, establishment of irrigation schemes and Awash National Park (ANP). Approximately 18 924 ha of forest and 4730 ha of grazing lands were devastated between 1973 and 2008. At the same time, there was a shift in land cover from forests/woodlands to open woodlands, shrub and grazing lands. The land cover classifications generally were achieved at a very high overall accuracy (84.34%) and overall kappa statistics (0.802), substantiating the value of using the classified LUC in this study as an input to hydrological models. This study results provide an opportunity to better understand and quantify the hydrological response regimes of the lake catchment from the perspective of changing LUC conditions during different hydrological periods and the resulting dynamics of the lake water balance.     

Mapping the total phosphorus concentration of biosolid amended surface soils using LANDSAT TM data

Conventional methods for soil sampling and analysis for soil variability in chemical characteristics are too time-consuming and expensive for multi-seasonal monitoring over large-scale areas. Hence, the objectives of this study are: 1) to determine changes in chemical concentrations of soils that are amended with treated sewage sludge; and 2) to determine if LANDSAT TM data can be used to map surface chemical characteristics of such amended soils. For this study, we selected two fields in NW Ohio, designated as F34 and F11, that had been applied with 34 and 11 ton acre^− 1 of biosolids, respectively. Soil samples from a total of 70 sampling locations across the two fields were collected one day prior to LANDSAT 5 overpass and were analyzed for several elemental concentrations. The accumulation of Ba, Cd, Cu, S and P were found to be significantly higher in the surface soils of field F34, compared to field F11. Regression equations were established to search for algorithms that could map these five elemental concentrations in the surface soils using six, dark-object-subtracted (DOS) LANDSAT TM bands and the 15 non-reciprocal spectral ratios derived from these six bands for the May 20, 2005, LANDSAT 5 TM image. Phosphorus (P) had the highest R² adjusted value (67.9%) among all five elements considered, and the resulting algorithm employed only spectral ratios. This model was successfully tested for robustness by applying it to another LANDSAT TM image obtained on June 5, 2005. Our results enabled us to conclude that LANDSAT TM imagery of bare-soil fields can be used to quantify and map the spatial variation of total phosphorous concentration in surface soils. This research has significant implications for identification and mapping of areas with high P, which is important for implementing and monitoring the best phosphorous management practices across the region.     

The temporal dimension of differenced Normalized Burn Ratio (dNBR) fire/burn severity studies: The case of the large 2007 Peloponnese wildfires in Greece

The temporal dimension of differenced Normalized Burn Ratio (dNBR) fire/burn severity studies was studied for the case of the large 2007 Peloponnese wildfires in Greece. Fire severity is defined as the degree of environmental change as measured immediately post-fire, whereas burn severity combines the direct fire impact and ecosystems responses. Geo Composite Burn Index (GeoCBI), two pre-/post-fire differenced Thematic Mapper (TM) dNBR assessments and a Moderate Resolution Imaging Spectroradiometer (MODIS) dNBR time series were used to analyze the temporal dimension. MODIS dNBR time series were calculated based on the difference between the NBR of the burned and control pixels, which were retrieved using time series similarity of a pre-fire year. The analysis incorporated the optimality statistic, which evaluates index performance based on displacements in the mid-infrared-near infrared bi-spectral space. Results showed a higher correlation between field and TM data early post-fire (R² = 0.72) than one-year post-fire (R² = 0.56). Additionally, mean dNBR (0.56 vs. 0.29), the dNBR standard deviation (0.29 vs. 0.19) and mean optimality (0.65 vs. 0.47) were clearly higher for the initial assessment than for the extended assessment. This is due to regenerative processes that obscured first-order fire effects impacting the suitability of the dNBR to assess burn severity in this case study. This demonstrates the importance of the lag timing, i.e. time since fire, of an assessment, especially in a quickly recovering Mediterranean ecosystem. The MODIS time series was used to study intra-annual changes in index performance. The seasonal timing of an assessment highly impacts what is actually measured. This seasonality affected both the greenness of herbaceous resprouters and the productivity of the control pixels, which is land cover specific. Appropriate seasonal timing of an assessment is therefore of paramount importance to anticipate false trends (e.g. caused by senescence). Although these findings are case study specific, it can be expected that similar temporal constraints affect assessments in other ecoregions. Therefore, within the limitations of available Landsat imagery, caution is recommended for the temporal dimension when assessing post-fire effects. This is crucial, especially for studies that aim to evaluate trends in fire/burn severity across space and time. Also, clarification in associated terminology is suggested.     

Arctic tundra functional types by classification of single-date and AVHRR bi-weekly NDVI composite datasets

Maps of functional vegetation types are often used to extrapolate in situ measurements of biogeochemical fluxes to make regional-scale or biome-scale estimates. The objective of this study was to determine the optimal spectral-radiometric and temporal features derived from single-date and seasonal time series NOAA AVHRR imagery for classifying three Arctic tundra functional types. A single-date, three-band (VIS, NIR and NDVI) input yielded a map with the highest agreement (86.1% for supervised and 87.8% for unsupervised approaches) compared to a reference map. The map generated with the conventional maximum value NDVI for 10 semi-monthly periods through the growing season was in 82.7% agreement.     

Land surface temperature retrieval from LANDSAT TM 5

In this paper, three methods to retrieve the land surface temperature (LST) from thermal infrared data supplied by band 6 of the Thematic Mapper (TM) sensor onboard the Landsat 5 satellite are compared. The first of them lies on the estimation of the land surface temperature from the radiative transfer equation using in situ radiosounding data. The others two are the mono-window algorithm developed by Qin et al. [International Journal of Remote Sensing 22 (2001) 3719] and the single-channel algorithm developed by Jiménez-Muñoz and Sobrino [Journal of Geophysical Research 108 (2003)]. The land surface emissivity (LSE) values needed in order to apply these methods have been estimated from a methodology that uses the visible and near infrared bands. Finally, we present a comparison between the LST measured in situ and the retrieved by the algorithms over an agricultural region of Spain (La Plana de Requena-Utiel). The results show a root mean square deviation (rmsd) of 0.009 for emissivity and lower than 1 K for land surface temperature when the Jiménez-Muñoz algorithm is used.     

Assessment of vegetation cover in India: LANDSAT images on band 5 black and white of 1980–82

An accurate estimate about the extent of vegetation coveri.e. tree vegetation, shrubs etc. of the country is not readily available. All the existing estimates about vegetation/forest area were based on either the legal forest areas which cover blanks, river beds, snow-covered areas, shrubs, grass land and tree-covered areas or large scale generalization of some stray observations. An attempt has been made in this paper to assess the extent of vegetative area with crown cover density on macrolevel using LANDSAT MSS imageries on band 5.     

A non-parametric clustering scheme for landsat

A 4-dimensional histogram is computed to reduce the large LANDSAT pixel data (up to 7.6 million pixels to a frame) to the much smaller number (6,000) of distinct vectors and their frequency of occurrence in the scene. The vectors are clustered by a recent non-parametric clustering algorithm⁽³⁾ using the histogram count as a probability density estimate. The resultant clusters are unimodal m the 4-dimensional histogram and can possess arbitrary shapes. The algorithm is non-iterative and does not require specification of the number of clusters a priori.

Hashing is used to generate the histogram and also subsequent table look-up classification of the individual pixels in the image after the histogram vectors are clustered. The resultant clustering scheme is very efficient and a 512 × 512 LANDSAT scene can be clustered in less than 2 min of CPU time on a PDP-10 computer. Results of the application of the clustering scheme on representative LANDSAT scenes are included.     

Distance preserving linear feature selection

A new method for linear feature selection is described which has as its underlying theme the preservation of actual distances between training data points in the lower dimensional space. Comparison with existing methodology places the method closer to the principle components or Karhunen- Loève approach than to methods based on an approach through statistical pattern recognition. A computer program implementing the technique is described. An example application to 12 dimensional LANDSAT data is given.     

Phycocyanin detection from LANDSAT TM data for mapping cyanobacterial blooms in Lake Erie

Algorithms were developed from LANDSAT 7 ETM+ data for the July 1, 2000 overpass and LANDSAT 5 Thematic Mapper (TM) data for the September 27, 2000 overpass for Path 20 Row 31 (including Toledo, OH) to measure relative phycocyanin content (PC) and turbidity in the western basin of Lake Erie. Water samples were collected from discrete hydrographic stations arranged in a 20×4 km grid adjacent to the Ohio shoreline during a 6-h period spanning each of the two LANDSAT overpasses. The samples were analyzed for chlorophyll (chl) a content and turbidity. In addition, the concentration of phycocyanin, a light-harvesting pigment associated with cyanobacteria, was estimated from the ratio of phycocyanin/chl a in vivo fluorescence (IVPF/IVCF). A dark-object-subtracted, spectral ratio model derived from the July 1, 2000 data was found to be the most robust, when applied to the September 27, 2000 data. The same July 1, 2000 model (or algorithm) for PC was then applied to LANDSAT 7 ETM+ frames for July 16 and August 1, 2002 of the Path 19 Row 31 frame (including Cleveland, OH) and to the August 8, 2002 frame of Path 20 Row 31. Moderate, very low, and high PC values were detected in the western basin of Lake Erie on July 16, August, 1, and August 8, 2002, respectively. On September 17, 2002, local media reported a large Microcystis bloom in the western basin. The high PC values on August 8, 2002 may have represented early stage detection of the large Microcystis bloom that was reported 5 weeks later. The PC algorithm derived in this study will improve our understanding of the temporal and spatial dynamics of cyanobacterial bloom formation in Lake Erie and other systems. It may also serve to alert municipalities to the presence of potentially toxic bloom events.     

Remote Sensing: Latest Developments and Uses

This paper considers currently available airborne and satellite-derived data suitable for monitoring coastal and river zones, and the potential offered by geographical information systems to manage this information. The main advantage of satellite data is the relatively low cost of acquisition over broad areas at regular intervals in time. Examples are (a) LANDSAT thematic mapper data and 'satellite pour l'observation de la terre'multi-spectral and panchromatic imagery which provide high resolution imagery for land-use mapping and bathymetric analysis, (b) National Oceanic and Atmospheric Administration's advanced very high resolution radiometer which provides a synoptic view of sea surface temperature and meteorological conditions, and (c) European remote sensing satellites whose array of sensors provide information on wave characteristics and wind speed and direction. In the field of geographical information systems, the challenge is to create systems which can both store the large quantity of data available and incorporate models which can predict the effects of change to the environment.