Monitoring northern mixed prairie health using broadband satellite imagery

The mixed prairie in Canada is characterized by its low to medium green vegetation cover, high amount of non‐photosynthetic materials, and ground level biological crust. It has proven to be a challenge for the application of remotely sensed data in extracting biophysical variables for the purpose of monitoring grassland health. Therefore, this study was conducted to evaluate the efficiency of broadband‐based reflectance and vegetation indices in extracting ground canopy information. The study area was Grasslands National Park (GNP) Canada and the surrounding pastures, which represent the northern mixed prairie. Fieldwork was conducted from late June to early July 2005. Biophysical variables—canopy height, cover, biomass, and species composition—were collected for 31 sites. Two satellite images, one SPOT 4 image on 22 June 2005, and one Landsat 5 TM image on 14 July 2005, were collected for the corresponding time period. Results show that the spectral curve of the grass canopy was similar to that of the bare soil with lower reflectance at each band. Consequently, commonly used vegetation indices were not necessarily better than reflectance when it comes to single wavelength regions at extracting biophysical information. Reflectance, NDVI, ATSAVI, and two new coined cover indices were good at extracting biophysical information.     

Sensor and Actuator Networks: Protecting Environmentally Sensitive Areas

The authors' work show how the development of wireless sensor and actuator networks (WSANs) can help protect environmentally sensitive areas using virtual fencing. Virtual fencing enables the spatial control of large cattle herds to protect environmentally sensitive regions based on real-time decisions made within WSANs. These systems can transform the way we undertake our stewardship of the natural environment by providing pervasive technologies that manage and change the environment.     

Cover: Mapping informal settlements using high resolution satellite imagery

Abstract

Multipolarization radar systems determine the scattering matrix associated with each element of the image to be constructed. Ways in which this information can be used to enhance radar images are reviewed, either by suppressing background noise or by enhancing features with known polarization signature. An imaging quantity designed to facilitate the recognition of such signatures is proposed. The elegance and convenience which can be achieved by concentrating on the eigenvectors and eigenvalues of the scattering matrix are emphasized.     

Object-based larch tree-crown delineation using high-resolution satellite imagery

Olgan larch is a traditional construction material used for the renovation of historical timber-frame buildings in China. However, acquiring the necessary large-sized larch trees from old-growth forests has become a challenge in China because of the rare and inaccessible distribution of these trees. In recent years, remote sensing imagery has provided a more effective alternative for delineating tree crowns automatically with high accuracy. In this study, an object-based method for delineating old-growth larch tree crowns using Geoeye-1 imagery is developed. Tree crown delineation results are tested and evaluated by field data. In addition, the correlation between delineated tree-crown and basal areas are quantitatively validated to ensure that the developed method can be applied for estimating the distribution of old-growth larch trees. Results demonstrate that the developed object-based larch tree-crown delineation method is reliable, thus providing a new technique for detecting old-growth larch tree resources in Northeastern China.     

Wheat yield estimates using multi-temporal NDVI satellite imagery

We examined seasonal growth profiles developed from AVHRR-NDVI for estimating wheat yield at regional and farm scales in Montana for the years 1989-1997. Both regions and farms showed strong relationships between wheat yields and integrated NDVI over the entire growing season, and with late-season NDVI parameters. The use of AVHRR-NDVI growth profiles at the regional level provided the strongest yield estimates. At the farm scale, the spatial resolution (1 km 2 ) limited the certainty for accurate portrayal of field locations. However, our models provide a basis for further examination of time-series satellite data.     

Survival analysis of a neotropical rainforest using multitemporal satellite imagery

We present results of an analysis of deforestation at a UNESCO Biosphere Reserve, the Parque National Yasuní, located in the rainforests of eastern Ecuador using multitemporal Landsat TM and ETM+ satellite imagery. Using survival analysis, we assessed both current and future trends in deforestation rates, and investigated the impact of spatial, cultural, and economic factors on deforestation. These factors included the distance from roads, rivers, research facilities, oil facilities, markets and towns, and land ownership by colonists, native inhabitants, and an oil company. We found the annual rate of deforestation is currently only 0.11%, but that this rate is increasing with time and, assuming that the trend of increasing rate of forest loss continues, we would predict that by 2063, 50% of the forest within 2 km of an oil access road will be lost to unhindered colonization and anthropogenic conversion. The Quechua colonists are associated with areas of the highest rate of deforestation, followed by the native Huaorani and the lowest region of deforestation was in areas occupied by a local oil company. By far, the strongest predictor of where deforestation is predicted to occur was proximity to the road. Proximity to research sites, oil facilities, market, and rivers significantly decreases deforestation rates, and proximity to towns significantly increases deforestation rates.     

Detection of marine aerosol particles in coastal zones using satellite imagery

Regions of brightness variations are common in visible and near-infrared satellites images from clear coastal regions. The variations have been hypothesized to be caused by aerosol particles in the marine boundary layer. The hypothesis was tested using in situ particle measurements collected near the time of satellite overpasses. Boundary-layer particle concentrations related to the brightness variations: high concentrations existed in bright regions and vice versa. This result indicates that, in regions over the ocean free of clouds, sunglint and whitecaps, the visible and near-infrared sensors aboard certain orbiting meteorological satellites can detect variations in the concentrations of haze particles in the marine boundary layer.     

Land-use mapping in West Messinia,Greece, using satellite imagery

Abstract

The purpose of this study was to apply thematic mapping using satellite imagery in assessing land-use patterns in W. Messinia, Greece. Various land-use classes of Messinia were mapped by using visual interpretation of false-colour composites based on Landsat Thematic Mapper (TM) and SPOT data. An identification, classification and mapping methodology was needed for the land-use forms that could be found in Greek territories with ecological and geological characteristics similar to those of Messinia.

Ground truth was taken from eighty test sites that cover the basic categories of land use. These sites were localized and identified on the false-colour composites. Based on this investigation, pure and complex land use patterns were defined and mapped on a 1:50000 scale map.

The results of this work show that several land use classes can be easilyidentified and be mapped precisely. Some forms are impossible to identify, becausemany of them can be easily confused with others due to their similar reflectingproperties. Such errors can be limited with the help of local observations, controlsand the use of specific thematic maps as well.

Specifically, the following land use patterns were identified on the Landsat TM and SPOT digital images: olive groves, vineyards, fig trees, orchards, non-irrigated winter crops, annual irrigated crops, green houses, meadows, abandoned land, bare land, shrubland, woodland, riparian vegetation, mudflats, beaches, urban land, quarries and excavations.

An evaluation of these land uses is presented.     

Assessing light to moderate grazing effects on grassland production using satellite imagery

Understanding the influences of grazing intensity on grassland production is essential for grassland conservation and management improvement. Grazing at light to moderate intensity theoretically enhances grassland production, thus benefiting grassland ecosystems. However, inconsistent results of the beneficial effects of light to moderate grazing on grassland production were reported due to the lack of accurate and repeatable techniques for discriminating grazing effects from other abiotic factors. Advanced remote-sensing techniques provide a promising tool for filling this gap in grazing effects research due to their high spatial and temporal resolution. In this article, the influences of light to moderate grazing on grassland production in mixed grasslands were investigated for the period 1986–2005, using spectral data derived from satellite images. The effects of precipitation on the detection of grazing-induced production change were also analysed. The results revealed that the normalized canopy index (NCI) showed superior performance in quantifying grassland production in mixed grasslands. Significant differences in grassland production between grazed and ungrazed treatments occurred in the three years with above-average and average growing-season precipitations (April–August), but not in the dry years. Most of the variation in production (75%) was explained by growing-season precipitation for both grazed and ungrazed sites. Our results demonstrate the feasibility of using remote-sensing data to monitor long-term light to moderate grazing effects and the important role of precipitation, especially growing-season precipitation, in modulating production in mixed-grassland ecosystems.     

Multi-scale hierarchical sampling change detection using Random Forest for high-resolution satellite imagery

ABSTRACT

High-resolution imagery provides rich information useful for land-use and land-cover change detection; however, methods to exploit these data lag behind data collection technologies. In this article, we propose a novel object-oriented multi-scale hierarchical sampling (MSHS) change detection method for high-resolution satellite imagery. In our method, MSHS is carried out to automatically obtain multi-scale training samples and different sample combinations. The training sample spectra, texture, and shape features are fused to build feature space after MSHS. Sample combinations and corresponding feature spaces are input into Random Forest (RF) to train multiple change classifiers. An optimal RF change detection classifier is selected when the out-of-bag error parameter in RF is at the minimum. In order to validate the proposed method, we applied it to high-resolution satellite image data and compared the detection results from our method and the single-scale sampling change detection method. These experimental results show that false alarm rates and missed detection of changed objects using our method were lower than the single-scale sampling change detection method. To demonstrate the scalability of the algorithm, different change detection methods were applied to three study sites. Experimental results show that our method delivered high overall accuracy and F₁-scores. Compared to traditional methods, our method makes full use of the multi-scale characteristics of ground objects. Our approach does not extend multi-scale feature vectors directly, but instead automatically increases the amount of the training samples at multiple scales, without increasing the volume of manual processing, thus improving the ability of the algorithm to generalize features from the RF model, making it more robust.     

Census from Heaven: An estimate of the global human population using night-time satellite imagery

Night-time satellite imagery provided by the Defense Meteorological Satellite Program's Operational Linescan System (DMSP OLS) is evaluated as a means of estimating the population of all the cities of the world based on their areal extent in the image. A global night-time image product was registered to a dataset of 2000 known city locations with known populations. A relationship between areal extent and city population discovered by Tobler and Nordbeck is identified on a nation by nation basis to estimate the population of the 22 920 urban clusters that exist in the night-time satellite image. The relationship between city population and city areal extent was derived from 1597 city point locations with known population that landed in a 'lit' area of the image. Due to conurbation, these 1597 cities resulted in only 1383 points of analysis for performing regression. When several cities fell into one 'lit' area their populations were summed. The results of this analysis allow for an estimate of the urban population of every nation of the world. By using the known percent of population in urban areas for every nation a total national population was also estimated. The sum of these estimates is a total estimate of the global human population, which in this case was 6.3 billion. This is fairly close to the generally accepted contemporaneous (1997) estimate of the global population which stood at approximately 5.9 billion.     

Measuring long-term ecological changes in densely populated landscapes using current and historical high resolution imagery

Long-term ecological changes within densely populated landscapes account for a growing share of global environmental change. Measuring the causes and consequences of these changes remains a challenge because of their fine spatial scale and complexity. Here, we measure long-term ecological changes, circa 1950 to 2002, within six 1 km² sites in densely populated rural China and in urban and suburban Baltimore, Maryland, USA using a standardized procedure for fine-scale feature-based ecological mapping from high spatial resolution (≤ 1 m) imagery. The median size of ecologically distinct landscape features (ecotopes) mapped by this procedure was just 520 m², though size, count and perimeter of features varied considerably both within and between sites. Land management and vegetation cover changed substantially, over 28% to 87% of site areas, but most of this change occurred in small patches with area < 4000 m². Landscape complexity also increased over time by the fragmentation of landscapes into a larger number of smaller features with an increasing diversity of ecotope classes. Detailed analysis of fine-scale landscape transformations helped identify the causes and consequences of ecologically significant changes within and across sites, including unexpected increases in perennial vegetation cover and the linkage of impervious surface area with population density. These and other results demonstrate the general utility of anthropogenic ecotope mapping as a tool for cross-site comparison and sampled regional estimates of long-term ecological changes within densely populated landscapes.     

Object-based satellite image analysis applied for crop residue estimating using Landsat OLI imagery

ABSTRACT

The main objective of this study is to apply an object-based image analysis (OBIA) approach to satellite image processing and determining crop residue cover (CRC) and tillage intensity. To achieve this goal, we collected ground truth data using line-transect method from 35 plots of farmlands with an area of 528 ha. Accordingly, Landsat Operational Land Imager (OLI) satellite image together with global positioning system (GPS)-based survey data set were considered for applying the OBIA methods and deriving CRC. To process the data, object-based image processing steps including segmentation and classification were applied to develop intelligent objects and establish classification using spectral and spatial characteristics of CRC. We developed three categories of rule sets including mean indices, tillage indices, and grey-level co-occurrence matrix (GLCM) texture features using the OBIA algorithms and assign class method. Results were validated against of ground control data set and were collected by GPS in field survey. Results of this study indicated that the brightness, normalised difference tillage index, and GLCM texture feature mean performed out as effective techniques. Overall accuracy and kappa coefficient (κ) were computed to be about 0.91 and 0.86; 0.93 and 0.90; 0.60 and 0.35, respectively, for the above-mentioned indices. The foregoing discussion has attempted to demonstrate that the remotely sensed data can be effective approach and substitute for ground methods, especially in large areas.     

Classification of Amazonian primary rain forest vegetation using Landsat ETM+ satellite imagery

A lack of spatially and thematically accurate vegetation maps complicates conservation and management planning, as well as ecological research, in tropical rain forests. Remote sensing has considerable potential to provide such maps, but classification accuracy within primary rain forests has generally been inadequate for practical applications. Here we test how accurately floristically defined forest types in lowland tropical rain forests in Peruvian Amazonia can be recognized using remote sensing data (Landsat ETM+ satellite image and STRM elevation model). Floristic data and a vegetation classification with four forest classes were available for eight line transects, each 8 km long, located in an area of ca 800 km². We compared two sampling unit sizes (line transect subunits of 200 and 500 m) and several image feature combinations to analyze their suitability for image classification. Mantel tests were used to quantify how well the patterns in elevation and in the digital numbers of the satellite image correlated with the floristic patterns observed in the field. Most Mantel correlations were positive and highly significant. Linear discriminant analysis was used first to build a function that discriminates between forest classes in the eight field-verified transects on the basis of remotely sensed data, and then to classify those parts of the line transects and the satellite image that had not been visited in the field. Classification accuracy was quantified by 8-fold crossvalidation. Two of the tierra firme (non-inundated) forest types were combined because they were too often misclassified. The remaining three forest types (inundated forest, terrace forest and Pebas formation/intermediate tierra firme forest) could be separated using the 500-m sampling units with an overall classification accuracy of 85% and a Kappa coefficient of 0.62. For the 200-m sampling units, the classification accuracy was clearly lower (71%, Kappa 0.35). The forest classification will be used as habitat data to study wildlife habitat use in the same area. Our results show that remotely sensed data and relatively simple classification methods can be used to produce reasonably accurate forest type classifications, even in structurally homogeneous primary rain forests.     

Improved classification of conservation tillage adoption using high temporal and synthetic satellite imagery

Conservation tillage management has been advocated for carbon sequestration and soil quality preservation purposes. Past satellite image analyses have had difficulty in differentiating between no-till (NT) and minimal tillage (MT) conservation classes due to similarities in surface residues, and may have been restricted by the availability of cloud-free satellite imagery. This study hypothesized that the inclusion of high temporal data into the classification process would increase conservation tillage accuracy due to the added likelihood of capturing spectral changes in MT fields following a tillage disturbance. Classification accuracies were evaluated for Random Forest models based on 250-m and 500-m MODIS, 30-m Landsat, and 30-m synthetic reflectance values. Synthetic (30-m) data derived from the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) were evaluated because high frequency Landsat image sets are often unavailable within a cropping season due to cloud issues. Classification results from a five-date Landsat model were substantially better than those reported by previous classification tillage studies, with 94% total and ≥ 88% class producer's accuracies. Landsat-derived models based on individual image scenes (May through August) yielded poor MT classifications, but a monthly increase in accuracy illustrated the importance of temporal sampling for capturing regional tillage disturbance signatures. MODIS-based model accuracies (90% total; ≥ 82% class) were lower than in the five-date Landsat model, but were higher than previous image-based and survey-based tillage classification results. Almost all the STARFM prediction-based models had classification accuracies higher than, or comparable to, the MODIS-based results (> 90% total; ≥ 84% class) but the resulting model accuracies were dependent on the MODIS/Landsat base pairs used to generate the STARFM predictions. Also evident within the STARFM prediction-based models was the ability for high frequency data series to compensate for degraded synthetic spectral values when classifying field-based tillage. The decision to use MODIS or STARFM-based data within conservation tillage analysis is likely situation dependent. A MODIS-based approach requires little data processing and could be more efficient for large-area mapping; however a STARFM-based analysis might be more appropriate in mixed-pixel situations that could potentially compromise classification accuracy.     

A study of moisture conditions in the Pantanal of Brazil using satellite imagery

The combined use of additive viewing and digital processing of LANDSAT-2 imagery of part of the Pantanal of Brazil has allowed detailed maps of the drainage network to be constructed. The distributions have been made of wet and dry areas, including differentiations of clear water, water containing suspended sediments, and categories of land with differing moisture conditions. Some unconventional use of color filters and MSS band combinations are suggested in order to extract maximum information from the imagery. Density slicing has allowed gray-scale values to be placed on the three categories of land identified. The distribution of the identified categories are verified by comparing the information from the visual classification with the classes isolated by density slicing.     

Detecting changes in high-resolution satellite coastal imagery using an image object detection approach

This article presents a spatial contrast-enhanced image object-based change detection approach (SICA) to identify changed areas using shape differences between bi-temporal high-resolution satellite images. Each image was segmented and intrinsic image objects were extracted from their hierarchic candidates by the proposed image object detection approach (IODA). Then, the dominant image object (DIO) presentation was labelled from the results of optimal segmentation. Comparing the form and the distribution of bi-temporal DIOs by using the raster overlay function, ground objects were recognized as being spatially changed where the corresponding image objects were detected as merged or split into geometric shapes. The result of typical spectrum-based change detection between two images was enhanced by using changed spatial information of image objects. The result showed that the change detection accuracies of the pixels with both attribute and shape changes were improved from 84% to 94% for the strong attribute pixel, and from 36% to 81% for the weak attribute pixel in study area. The proposed approach worked well on high-resolution satellite coastal images.     

Remote sensing of vegetation water content from equivalent water thickness using satellite imagery

Vegetation water content (VWC) is one of the most important parameters for the successful retrieval of soil moisture content from microwave data. Normalized Difference Infrared Index (NDII) is a widely-used index to remotely sense Equivalent Water Thickness (EWT) of leaves and canopies; however, the amount of water in the foliage is a small part of total VWC. Sites of corn (Zea mays), soybean (Glycine max), and deciduous hardwood woodlands were sampled to estimate EWT and VWC during the Soil Moisture Experiment 2005 (SMEX05) near Ames, Iowa, USA. Using a time series of Landsat 5 Thematic Mapper, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Advanced Wide Field Sensor (AWiFS) imagery, NDII was related to EWT with R² of 0.85; there were no significant differences among land-cover types. Furthermore, EWT was linearly related to VWC with R² of 0.87 for corn and 0.48 for soybeans, with a significantly larger slope for corn. The 2005 land-cover classification product from the USDA National Agricultural Statistics Service had an overall accuracy of 92% and was used to spatially distribute VWC over the landscape. SMEX05 VWC versus NDII regressions were compared with the regressions from the Soil Moisture Experiment 2002 (SMEX02), which was conducted in the same study area. No significant difference was found between years for corn (P = 0.13), whereas there was a significant difference for soybean (P = 0.04). Allometric relationships relate the size of one part of a plant to the sizes of other parts, and may be the result from the requirements of structural support or material transport. Relationships between NDII and VWC are indirect, NDII is related to canopy EWT, which in turn is allometrically related to VWC.     

A scale-adjusted measure of “Urban sprawl” using nighttime satellite imagery

“Urban Sprawl” is a growing concern of citizens, environmental organizations, and governments. Negative impacts often attributed to urban sprawl are traffic congestion, loss of open space, and increased pollutant runoff into natural waterways. Definitions of “Urban Sprawl” range from local patterns of land use and development to aggregate measures of per capita land consumption for given contiguous urban areas (UA). This research creates a measure of per capita land use consumption as an aggregate index for the spatially contiguous urban areas of the conterminous United States with population of 50,000 or greater. Nighttime satellite imagery obtained by the Defense Meteorological Satellite Program's Operational Linescan System (DMSP OLS) is used as a proxy measure of urban extent. The corresponding population of these urban areas is derived from a grid of the block group level data from the 1990 U.S. Census. These numbers are used to develop a regression equation between Ln(Urban Area) and Ln(Urban Population). The ‘scale-adjustment’ mentioned in the title characterizes the “Urban Sprawl” of each of the urban areas by how far above or below they are on the “Sprawl Line” determined by this regression. This “Sprawl Line” allows for a more fair comparison of “Urban Sprawl” between larger and smaller metropolitan areas because a simple measure of per capita land consumption or population density does not account for the natural increase in aggregate population density that occurs as cities grow in population. Cities that have more “Urban Sprawl” by this measure tended to be inland and Midwestern cities such as Minneapolis-St. Paul, Atlanta, Dallas-Ft. Worth, St. Louis, and Kansas City. Surprisingly, west coast cities including Los Angeles had some of the lowest levels of “Urban Sprawl” by this measure. There were many low light levels seen in the nighttime imagery around these major urban areas that were not included in either of the two definitions of urban extent used in this study. These areas may represent a growing commuter-shed of urban workers who do not live in the urban core but nonetheless contribute to many of the impacts typically attributed to “Urban Sprawl”. “Urban Sprawl” is difficult to define precisely partly because public perception of sprawl is likely derived from local land use planning decisions, spatio-demographic change in growing urban areas, and changing values and social mores resulting from differential rates of international migration to the urban areas of the United States. Nonetheless, the aggregate measures derived here are somewhat different than similar previously used measures in that they are ‘scale-adjusted’; also, the spatial patterns of “Urban Sprawl” shown here shed some insight and raise interesting questions about how the dynamics of “Urban Sprawl” are changing.     

Monitoring fall foliage coloration dynamics using time-series satellite data

Fall foliage coloration is a phenomenon that occurs in many deciduous trees and shrubs worldwide. Measuring the phenology of fall foliage development is of great interest for climate change, the carbon cycle, ecology, and the tourist industry; but little effort has been devoted to monitoring the regional fall foliage status using remotely-sensed data. This study developed an innovative approach to monitoring fall foliage status by means of temporally-normalized brownness derived from MODIS (Moderate Resolution Imaging Spectroradiometer) data. Specifically, the time series of the MODIS Normalized Difference Vegetation Index (NDVI) was smoothed and functionalized using a sigmoidal model to depict the continuous dynamics of vegetation growth. The modeled temporal NDVI trajectory during the senescent phase was further combined with the mixture modeling to deduce the temporally-normalized brownness index which was independent of the surface background, vegetation abundance, and species composition. This brownness index was quantitatively linked with the fraction of colored and fallen leaves in order to model the fall foliage coloration status. This algorithm was tested by monitoring the fall foliage coloration phase using MODIS data in northeastern North America from 2001 to 2004. The MODIS-derived timing of foliage coloration phases was compared with in-situ measurements, which showed an overall absolute mean difference of less than 5 days for all foliage coloration phases and about 3 days for near peak coloration and peak coloration. This suggested that the fall foliage coloration phase retrieved from the temporally-normalized brownness index was qualitatively realistic and repeatable.