Assessment of some remote sensing techniques used to detect land use/land cover changes in South-East Transilvania,Romania

This paper assesses the image differencing technique for the Normalized Difference Vegetation Index (NDVI), the second principal component (PC2), and the TM 4 band (TM 4), as well as the post-classification comparison (PCC) in order to analyze the land use/land cover changes in the South-East Transilvania, Romania. The analysis was performed using two frames from Landsat 5 TM satellite images acquired on August 5, 1993 and July 24, 2009. After applying the NDVI, PC2, and TM 4 image differencing techniques, the images obtained were transformed into change/no change maps. The thresholds identified to highlight the changes were set at 0.6 s for NDVI and 0.7 s for PC2 and TM 4. Before applying the PCC technique, the satellite images were classified through the supervised classification method. The overall accuracy obtained was 85.91 % and the kappa statistics 0.8249 for 1993, 88.18 % and 0.8497 for 2009, respectively. The assessment of the changes detection methods in the studied area shows that the first place is occupied by NDVI image differencing with an overall accuracy of 83.80 %, followed by PCC method with 83.20 %, PC2 difference with an overall accuracy of 81.60 %, and TM 4 difference with an overall accuracy of 79.40 %.     

Landsat and limnologically derived water quality data: A perspective

Fixed station sampling is the conventional method used to obtain data on the median water quality of reservoirs. A major source of uncertainty associated with this technique is that water quality at the fixed stations may not be representative of the ambient water quality in the reservoir at the time of sampling. This problem is particularly relevant for water quality variables such as chlorophyll, which have a markedly patchy spatial distribution. The use of Landsat reflectance data to estimate median chlorophyll concentrations in Roodeplaat Dam was investigated. A linear polynomial regression model for estimating chlorophyll concentrations from Landsat reflectance data, was firstly calibrated with chlorophyll concentration data obtained by sampling seven fixed stations on the reservoir at the time of the satellite overflight to produce an individual calibration. Secondly, the model was calibrated with a pooled set of sampled data obtained from five separate overflights, to obtain a generalised calibration.It was found that median chlorophyll concentrations determined from Landsat-derived data were similar to median chlorophyll concentrations estimated from fixed station data. However, the range of chlorophyll concentrations in the reservoir estimated from Landsat data was considerably larger than that estimated from fixed station data. Landsat derived estimates of chlorophyll concentrations have the added advantage of providing information on the spatial distribution of chlorophyll in the reservoir.     

A combinated approach to investigate the biochemistry and hydrography of a shallow bay in the South Adriatic Sea: the Gulf of Manfredonia (Italy)

The main goal of this study is to understand the hydrological and biochemical set up and processes of a marine coastal area located in the western side of the south Adriatic sea (the gulf of Manfredonia) by the use of both satellite images and in situ investigations A water sampling in the gulf of Manfredonia was performed at 18 fixed stations in June 2003; physico-chemical and biological parameters (temperature, salinity, dissolved oxygen and fluorescence) were measured along the water column and water samples were collected to analyse dissolved nutrients (ammonia, nitrite, nitrate, phosphate and silicate), total nutrients (nitrogen and phosphorus), chlorophyll a and total suspended solids. Surface spatial distributions of field collected data were examined to characterize biochemical and hydrographic conditions of the Gulf of Manfredonia and these results were related with the remote sensing analysis data. Remote sensed data (obtained by Landsat 7 TM and Modis Terra) were processed to obtain maps of chlorophyll a, temperature and optical characteristics of the gulf; these maps were compared to in situ data. From physico-chemical measurements no stratification was observed in the water column except for the south-eastern area. High concentrations of silicate and ammonia were observed in the northern zone of the gulf, while nitrate, phosphate, chlorophyll a and total suspended solids distributions showed higher values in the central coastal zone. These results were confirmed by remote sensing analysis; Modis elaboration showed a distribution with higher concentrations of chlorophyll a near the coast and Landsat images highlighted the extension of the surrounding agricultural areas crossed by the two main rivers which discharge into the gulf. The integration between field data with the remote sensing analysis showed to be a valid support in coastal zone management.     

Coastal dynamic and shoreline mapping: multi-sources spatial data analysis in Semarang Indonesia

Semarang coastal area has geomorphologically complex processes, such as erosion-sedimentation, land subsidence, and tidal inundation hazard. Multi-years shoreline mapping is considered a valuable task for coastal monitoring and assessment. This paper presents maps illustrating the shoreline dynamic in a coastal area of Semarang-Indonesia using multi-sources spatial data. The segment data has been obtained by visual delineation of the topographic maps Year 1908, 1937, 1992 and Ikonos image Year 2003 as well as digital number (DN) value analysis and masking operation of Landsat MSS Year 1972 and Landsat ETM Year 2001. For the long period of almost 100 year, the shoreline dynamic in Semarang coastal area is dominated by sedimentation process. Shoreline extended to the sea as a result of man-made infrastructure and natural processes. The research's result was satisfactory and the method has proven to be effective considering lack of homogeneous data-series. However, some further improvement regarding geo-processing can be made and the accuracy can be tested in future version.     

Double sampling for stratification for the monitoring of sparse tree populations: the example of Populus euphratica Oliv. forests at the lower reaches of Tarim River, Southern Xinjiang, China

Desertification is a pressing issue in the dry Tarim River basin, which is under anthropogenic stresses. In this study, double sampling for stratification (DSS) is employed to inventory Populus euphratica Oliv. forests in the lower reaches of the Tarim River Basin in Xinjiang, China. The two objectives were evaluating DSS as a sampling technique for monitoring desertification and generating baseline information for permanent observation. Here, DSS consists of two phases: in phase 1, crown cover is observed on a large sample of plots on a high resolution satellite image, and these photo-plots are stratified into five crown cover strata. Phase 2 is a stratified random sample from these photo-plots and the sampled plots are field observed. Approximately 32% of the study area is without P. euphratica trees. As expected, estimated mean poplar tree density and basal area increase with crown cover. DSS takes advantages of stratification (fieldwork efficiency and statistical precision) without the need for a priori strata delineation. It proves feasible for inventory the sparse poplar population and holds promise for the assessment of trees outside the forest, where density varies considerably and pre-stratification is intractable. It can be integrated into permanent observation systems for monitoring vegetation changes.     

How best to quantify soil seed banks in arid rangelands of the Nama Karoo?

Sampling design and three sample treatments prior the application of the seedling emergence method were tested in order to find the best method for seed bank quantification in arid Nama Karoo rangelands. I analyzed species composition and seed densities by contrasting undercanopy and open-matrix samples from two soil depths and by comparing the effects of cold-, heat-, and no stratification on germination rates of species in a greenhouse setting. The soil seed bank showed minimal similarity to the standing vegetation, with only 20 plant species germinated. Spatial distribution of seeds was highly heterogeneous. Nearly 90% of germinated seeds were located in 0- to 4-cm compared to >4- to 8-cm soil depth. Undercanopy seed banks contained significantly more species and seeds than open-matrix seed banks. Neither the number nor the diversity of seeds germinated differed significantly among the three treatments. Cold stratification tended to detect more species and seeds only at >4- to 8-cm soil depth. The results highlight the importance of spatial heterogeneity in the accurate evaluation of soil seed banks in the arid Nama Karoo and the importance of considering seasonal variability in the availability of readily germinable seeds. Data also suggest that sample pretreatment in germination trials may give little return for cost and effort, which emphasizes that it is more important to choose the sampling design most likely to give a representative number of seed bank species. Further studies are needed to analyze seed bank dynamics and species-specific germination requirements to promote recruitment of plant taxa underrepresented in the seed bank.     

Land cover change of watersheds in Southern Guam from 1973 to 2001

Land cover change can be caused by human-induced activities and natural forces. Land cover change in watershed level has been a main concern for a long time in the world since watersheds play an important role in our life and environment. This paper is focused on how to apply Landsat Multi-Spectral Scanner (MSS) satellite image of 1973 and Landsat Thematic Mapper (TM) satellite image of 2001 to determine the land cover changes of coastal watersheds from 1973 to 2001. GIS and remote sensing are integrated to derive land cover information from Landsat satellite images of 1973 and 2001. The land cover classification is based on supervised classification method in remote sensing software ERDAS IMAGINE. Historical GIS data is used to replace the areas covered by clouds or shadows in the image of 1973 to improve classification accuracy. Then, temporal land cover is utilized to determine land cover change of coastal watersheds in southern Guam. The overall classification accuracies for Landsat MSS image of 1973 and Landsat TM image of 2001 are 82.74% and 90.42%, respectively. The overall classification of Landsat MSS image is particularly satisfactory considering its coarse spatial resolution and relatively bad data quality because of lots of clouds and shadows in the image. Watershed land cover change in southern Guam is affected greatly by anthropogenic activities. However, natural forces also affect land cover in space and time. Land cover information and change in watersheds can be applied for watershed management and planning, and environmental modeling and assessment. Based on spatio-temporal land cover information, the interaction behavior between human and environment may be evaluated. The findings in this research will be useful to similar research in other tropical islands.     

Point Sampling Digital Imagery with ‘Samplepoint’

Measuring percent occurrence of objects from digital images can save time and expense relative to conventional field measurements. However, the accuracy of image analysis had, until now, not reached the level of the best conventional field measurements. Additionally, most image-analysis software programs require advanced user training to successfully analyze images. Here we present a new software program, ‘SamplePoint,’ that provides the user a single-pixel sample point and the ability to view and identify the pixel context. We found SamplePoint to allow accuracy comparable with the most accurate field-methods for ground-cover measurements. Expert use of the program requires minimal training and its ease of use allows rapid measurements from image data. We recommend SamplePoint for calibrating the threshold-detection level of image-analysis software or for making direct measurements of percent occurrence from digital images.     

A comparison of radiometric correction techniques in the evaluation of the relationship between LST and NDVI in Landsat imagery

Atmospheric corrections for multi-temporal optical satellite images are necessary, especially in change detection analyses, such as normalized difference vegetation index (NDVI) rationing. Abrupt change detection analysis using remote-sensing techniques requires radiometric congruity and atmospheric correction to monitor terrestrial surfaces over time. Two atmospheric correction methods were used for this study: relative radiometric normalization and the simplified method for atmospheric correction (SMAC) in the solar spectrum. A multi-temporal data set consisting of two sets of Landsat images from the period between 1991 and 2002 of Penang Island, Malaysia, was used to compare NDVI maps, which were generated using the proposed atmospheric correction methods. Land surface temperature (LST) was retrieved using ATCOR3_T in PCI Geomatica 10.1 image processing software. Linear regression analysis was utilized to analyze the relationship between NDVI and LST. This study reveals that both of the proposed atmospheric correction methods yielded high accuracy through examination of the linear correlation coefficients. To check for the accuracy of the equation obtained through linear regression analysis for every single satellite image, 20 points were randomly chosen. The results showed that the SMAC method yielded a constant value (in terms of error) to predict the NDVI value from linear regression analysis-derived equation. The errors (average) from both proposed atmospheric correction methods were less than 10%.     

1984—2015年太湖地区大气能见度变化遥感监测与评价

研究收集整理了1984—2015年太湖区域342幅Landsat系列卫星遥感影像,利用ENVI软件FLAASH模块计算获取了区域大气能见度均值,通过太湖周边苏州、无锡、常州3市地面自动站能见度监测值加以验证,并在此基础上对1984—2015年该地区大气能见度遥感监测结果进行了分析与评价。研究结果表明：太湖地区在20世纪80年代能见度水平相对较好,20世纪90年代后逐渐下滑,总体呈下降趋势。2015年遥感反演能见度均值为14.80 km,与1984年的21.46 km相比下降了31.05%,下降速率约为0.21 km/a。     

Analysis of the changes of vegetation coverage of western Beijing mountainous areas using remote sensing and GIS

Mentougou District acts as a crucial component in the ecological buffer in western Beijing mountainous areas, Beijing, China. Using two Landsat MSS/TM images acquired on July 14, 1979 and July 23, 2005, the vegetation coverage of Mentougou District was calculated based on normalized difference vegetation index and spectral mixture analysis (NDVI-SMA) model. Its temporal and spatial changes were analyzed according to digital elevation model (DEM) image, social and economic data. The results showed that the vegetation coverage decreased from 76.4% in 1979 to 72.7% in 2005. Vegetation degradation was probably the result of human disturbance, such as outspreading of resident areas, and coal and stone mining activities, while vegetation restoration might be contributed by the combined effects of both natural processes and ecological construction effort. Vegetation changes were closely related to topographical characteristics. Plants at high altitude were more stable and less degraded than the plants at low altitude, while the plants on steep slope or northwest aspect were more vulnerable to degradation. During the period of 26 years, landscape appeared to become more fragmental, and ecological quality of the land seemed deteriorated sharply in that highly-covered vegetation area has been decreased by 24%.     

An automated (novel) algorithm for estimating green vegetation cover fraction from digital image: UIP-MGMEP

Green vegetation cover fraction (VCF) is an important indicator of vegetation status in ecology and agronomy. Digital image analysis (DIA) has been widely accepted as a new VCF measurement technique. In this study, we present a novel fully automatic threshold segmentation algorithm for VCF measurements, which is named as upper inflection point plus mean gradient magnitude of edge pixels (UIP-MGMEP). The algorithm performs VCF estimation upon the vegetation index Excess Green (EXG). UIP-MGMEP optimizes the EXG threshold by searching the upper inflection point (UIP) of the M-Et curve (mean gradient magnitude of edge pixels (MGMEP) vs. EXG threshold), based on the assumption that EXG variance of the boundary pixels between vegetation and background is larger than the variance of the background. Five typical sample images are used to illustrate how ground complexity reduces the distinctness of the UIP. Three controlled experiments are illustrated to test the robustness of UIP-MGMEP to resolution, exposure, and ground complexity. The results show that UIP-MGMEP is a promising algorithm for automatic VCF estimation upon digital images. Compared to broad-leaved grass, narrow-leaved grass is more sensitive to resolution and exposure. To reduce ground complexity, smaller footprint size while more images to cover the same area may be better than one image with large footprint size. UIP-MGMEP is fully automatic, making it promising for batch processing of VCF measurements that is very difficult in any wide-range field survey in the past. UIP-MGMEP algorithm can only extract green vegetation and is not suitable for non-green (even grayish-green) vegetation, due to the limits of vegetation index EXG. In addition, UIP-MGMEP is not recommended for images with VCF less than 0.5% or greater than 99.5%.     

Mapping broom snakeweed through image analysis of color-infrared photography and digital imagery

A study was conducted on a south Texas rangeland area to evaluate aerial color-infrared (CIR) photography and CIR digital imagery combined with unsupervised image analysis techniques to map broom snakeweed [Gutierrezia sarothrae (Pursh.) Britt. and Rusby]. Accuracy assessments performed on computer-classified maps of photographic images from two sites had mean producer's and user's accuracies for broom snakeweed of 98.3 and 88.3%, respectively; whereas, accuracy assessments performed on classified maps from digital images of the same two sites had mean producer's and user's accuracies for broom snakeweed of 98.3 and 92.8%, respectively. These results indicate that CIR photography and CIR digital imagery combined with image analysis techniques can be used successfully to map broom snakeweed infestations on south Texas rangelands.     

Analysis of multi-temporal landsat satellite images for monitoring land surface temperature of municipal solid waste disposal sites

This studypresents a remote sensing application of using time series Landsat satellite images for monitoring the Trail Road and Nepean municipal solid waste (MSW) disposal sites in Ottawa, Ontario, Canada. Currently, the Trail Road landfill is in operation; however, during the 1960s and 1980s, the city relied heavily on the Nepean landfill. More than 400 Landsat satellite images were acquired from the US Geological Survey (USGS) data archive between 1984 and 2011. Atmospheric correction was conducted on the Landsat images in order to derive the landfill sites’ land surface temperature (LST). The findings unveil that the average LST of the landfill was always higher than the immediate surrounding vegetation and air temperature by 4 to 10 °C and 5 to 11.5 °C, respectively. During the summer, higher differences of LST between the landfill and its immediate surrounding vegetation were apparent, while minima were mostly found in fall. Furthermore, there was no significant temperature difference between the Nepean landfill (closed) and the Trail Road landfill (active) from 1984 to 2007. Nevertheless, the LST of the Trail Road landfill was much higher than the Nepean by 15 to 20 °C after 2007. This is mainly due to the construction and dumping activities (which were found to be active within the past few years) associated with the expansion of the Trail Road landfill. The study demonstrates that the use of the Landsat data archive can provide additional and viable information for the aid of MSW disposal site monitoring.     

Mapping giant salvinia with satellite imagery and image analysis

QuickBird multispectral satellite imagery was evaluated for distinguishing giant salvinia (Salvinia molesta Mitchell) in a large reservoir in east Texas. The imagery had four bands (blue, green, red, and near-infrared) and contained 11-bit data. Color-infrared (green, red, and near-infrared bands), normal color (blue, green and red bands), and four-band composite (blue, green, red, and near-infrared bands) images were studied. Unsupervised image analysis was used to classify the imagery. Accuracy assessments performed on the classification maps of the three composite images had producer’s and user’s accuracies for giant salvinia ranging from 87.8 to 93.5%. Color-infrared, normal color, and four-band satellite imagery were excellent for distinguishing giant salvinia in a complex field habitat.     

Watershed-Based Survey Designs

Watershed-based sampling design and assessment tools help serve the multiple goals for water quality monitoring required under the Clean Water Act, including assessment of regional conditions to meet Section 305(b), identification of impaired water bodies or watersheds to meet Section 303(d), and development of empirical relationships between causes or sources of impairment and biological responses. Creation of GIS databases for hydrography, hydrologically corrected digital elevation models, and hydrologic derivatives such as watershed boundaries and upstream–downstream topology of subcatchments would provide a consistent seamless nationwide framework for these designs. The elements of a watershed-based sample framework can be represented either as a continuous infinite set defined by points along a linear stream network, or as a discrete set of watershed polygons. Watershed-based designs can be developed with existing probabilistic survey methods, including the use of unequal probability weighting, stratification, and two-stage frames for sampling. Case studies for monitoring of Atlantic Coastal Plain streams, West Virginia wadeable streams, and coastal Oregon streams illustrate three different approaches for selecting sites for watershed-based survey designs.     

Automatic habitat classification methods based on satellite images: A practical assessment in the NW Iberia coastal mountains

Although remote sensing is increasingly in use for habitat mapping, traditional image classification methods tend to suffer shortcomings due to non-normality of spectral signatures, as well as overlapping and heterogeneity in radiometric responses of natural and semi natural vegetation. Methods using non-parametric classifiers and object-oriented analysis have been suggested as possible solutions for overcoming these limitations. In this paper, we aimed at evaluating the performance of some of these techniques for the European Natura 2000 network of protected areas habitats mapping. For this purpose, we tested different methods of supervised image classification in the Northern Mountains of Galicia, Spain, an area included in the Natura 2000 network, which is characterized by a highly heterogeneous landscape. Methods involved the use of maximum likelihood and nearest neighbour decision rules in per-pixel and per-object classification analyses on Landsat TM imagery. Per-object classifications were completed using the segment mean and segment means plus standard deviation feature spaces. The results showed the existence of significant differences in the accuracies for the different methodologies, their strengths and weaknesses and identified the most adequate approach for habitat mapping. Analyses pointed out that significant improvements in accuracy were achieved only under certain combinations of per-object analysis, non-parametric classifiers and high dimensionality feature space.     

A landscape approach to quantifying land cover changes in Yulin, Northwest China

In this study we quantified land cover changes in the arid region of Yulin City, Northwest China between 1985 and 2000 using remote sensing and GIS in conjunction with landscape modeling. Land covers were mapped into 20 categories from multitemporal Landsat TM images. Five landscape indices were calculated from these maps at the land cover patches level. It was found that fallow land decreased by 125,148 ha while grassland and woodland increased by 107,975 and 17,157 ha, respectively. Landscape heterogeneity, dominance and fractal dimension changed little during the 15-year period while landscape became more fragmented, with an index rising from 0.56 to 0.58. The major factors responsible for these changes are identified as the change in the government policy on preserving the environment, continued growth in mining, and urbanization.     

Monitoring and assessing of landscape heterogeneity at different scales

In numerous studies, spatial and spectral aggregations of pixel information using average values from imaging spectrometer data are suggested to derive spectral indices and the subsequent vegetation parameters that are derived from these. Currently, there are very few empirical studies that use hyperspectral data, to support the hypothesis for deriving land surface variables from different spectral and spatial scales. In the study at hand, for the first time ever, investigations were carried out on fundamental scaling issues using specific experimental test flights with a hyperspectral sensor to investigate how vegetation patterns change as an effect of (1) different spatial resolutions, (2) different spectral resolutions, (3) different spatial and spectral resolutions as well as (4) different spatial and spectral resolutions of originally recorded hyperspectral image data compared to spatial and spectral up- and downscaled image data. For these experiments, the hyperspectral sensor AISA-EAGLE/HAWK (DUAL) was mounted on an aircraft to collect spectral signatures over a very short time sequence of a particular day. In the first experiment, reflectance measurements were collected at three different spatial resolutions ranging from 1 to 3 m over a 2-h period in 1 day. In the second experiment, different spectral image data and different additional spatial data were collected over a 1-h period on a particular day from the same test area. The differently recorded hyperspectral data were then spatially and spectrally rescaled to synthesize different up- and down-rescaled images. The normalised difference vegetation index (NDVI) was determined from all image data. The NDVI heterogeneity of all images was compared based on methods of variography. The results showed that (a) the spatial NDVI patterns of up- and downscaled data do not correspond with the un-scaled image data, (b) only small differences were found between NDVI patterns determined from data recorded and resampled at different spectral resolutions and (c) the overall conclusion from the tests carried out is that the spatial resolution is more important in determining heterogeneity by means of NDVI than the depth of the spectral data. The implications behind these findings are that we need to exercise caution when interpreting and combining spatial structures and spectral indices derived from satellite images with differently recorded geometric resolutions.     

The Impact of Landsat Satellite Monitoring on Conservation Biology

Landsat 7s recent malfunctioning will result in significant gaps in long-term satellite monitoring of Earth, affecting not only the research of the Earth science community but also conservation users of these data. To determine whether or how important Landsat monitoring is for conservation and natural resource management, we reviewed the Landsat programs history with special emphasis on the development of user groups. We also conducted a bibliographic search to determine the extent to which conservation research has been based on Landsat data. Conservation biologists were not an early user group of Landsat data because a) biologists lacked technical capacity – computers and software – to analyze these data; b) Landsats 1980s commercialization rendered images too costly for biologists budgets; and c) the broad-scale disciplines of conservation biology and landscape ecology did not develop until the mid-to-late 1980s. All these conditions had changed by the 1990s and Landsat imagery became an important tool for conservation biology. Satellite monitoring and Landsat continuity are mandated by the Land Remote Sensing Act of 1992. This legislation leaves open commercial options. However, past experiments with commercial operations were neither viable nor economical, and severely reduced the quality of monitoring, archiving and data access for academia and the public. Future satellite monitoring programs are essential for conservation and natural resource management, must provide continuity with Landsat, and should be government operated.