Sergio Bernardes

Self-Adaptive Gradient-Based Thresholding Method for Coal Fire Detection Using ASTER Thermal Infrared Data, Part I: Methodology and Decadal Change Detection

Coal fires that are induced by natural spontaneous combustion or result from human activities occurring on the surface and in underground coal seams destroy coal resources and cause serious environmental degradation. Thermal infrared image data, which directly measure surface temperature, can be an important tool to map coal fires over large areas. As the first of two parts introducing our coal fire detection method, this paper proposes a self-adaptive threshold-based approach for coal fire detection using ASTER thermal infrared data: the self-adaptive gradient-based thresholding method (SAGBT). This method is based on an assumption that the attenuation of temperature along the coal fire’s boundaries generates considerable numbers of spots with extremely high gradient values. The SAGBT method applied mathematical morphology thinning to skeletonize the potential high gradient buffers into the extremely high gradient lines, which provides a self-adaptive mechanism to generate thresholds according to the thermal spatial patterns of the images. The final threshold was defined as an average temperature value reading from the high temperature buffers (segmented by 1.0 σ from the mean) and along a sequence of extremely high gradient lines (thinned from the potential high gradient buffers and segmented within the lower bounds, ranging from 0.5 σ to 1.5 σ and with an upper bound of 3.2 σ, where σ is the standard deviation), marking the coal fire areas. The SAGBT method used the basic outer boundary of the coal-bearing strata to simply exclude false alarms. The intermediate thresholds reduced the coupling with the temperature and converged by changing the potential high gradient buffers. This simple approach can be economical and accurate in identifying coal fire areas. In addition, it allows for the identification of thresholds using multiple ASTER TIR scenes in a consistent and uniform manner, and supports long-term coal fire change analyses using historical images in local areas. This paper focuses on the introduction of the methodology. Furthermore, an improvement to SAGBT is proposed. In a subsequent paper, subtitled “Part 2, Validation and Sensitivity Analysis,” we address satellite-field simultaneous observations and report comparisons between the retrieved thermal anomalies and field measurements in different aspects to prove that the coal fires are separable by the SAGBT method. These comparisons allowed us to estimate the accuracy and biases of the SAGBT method. As an application of the SAGBT, a relationship between coal fires’ decadal variation and coal production was also examined. Our work documented a total area increase in the beginning of 2003, which correlates with increased mining activities and the rapid increase of energy consumption in China during the decade (2001–2011). Additionally, a decrease in the total coal fire area is consistent with the nationally sponsored fire suppression efforts during 2007–2008. It demonstrated the applicability of SAGBT method for long-term change detection with multi-temporal images.

Self-Adaptive Gradient-Based Thresholding Method for Coal Fire Detection Based on ASTER Data—Part 2, Validation and Sensitivity Analysis

The self-adaptive gradient-based thresholding (SAGBT) method is a simple non-interactive coal fire detection approach involving segmentation and a threshold identification algorithm that adapts to the spatial distribution of thermal features over a landscape. SAGBT detects coal fire using multispectral thermal images acquired by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. The method was detailed by our previous work “Self-Adaptive Gradient-Based Thresholding Method for Coal Fire Detection Based on ASTER Data—Part 1, Methodology”. The current study evaluates the performance of SAGBT and validates its results by using ASTER thermal infrared (TIR) images and ground temperature data collected at the Wuda coalfield (China) during satellite overpass. We further analyzed algorithm performance by using nighttime TIR images and images from different seasons. SAGBT-derived fires matched fire spots measured in the field with an average offset of 32.44 m and a matching rate of 70%–85%. Coal fire areas from TIR images generally agreed with coal-related anomalies from visible-near infrared (VNIR) images. Further, high-temperature pixels in the ASTER image matched observed coal fire areas, including the major extreme high-temperature regions derived from field samples. Finally, coal fires detected by daytime and by nighttime images were found to have similar spatial distributions, although fires differ in shape and size. Results included the stratification of our study site into two temperature groups (high and low temperature), using a fire boundary. We conclude that SAGBT can be successfully used for coal fire detection and analysis at our study site.

Remote sensing and habitat mapping for bearded capuchin monkeys (Sapajus libidinosus): landscapes for the use of stone tools

Abstract. Application of remote sensing and geographic information systems to the characterization of wildlife habitats is an area of growing significance for conservation. We examine the use of space of a group of bearded capuchin monkeys, a species unique in their use of stone tools to extract encapsulated foods. We define important landscape variables associated with the monkeys’ behavior, especially the use of stone tools. Maximum entropy modeling is used to define the landscape characteristics associated with the monkeys’ use of space. The variables evaluated in model building include normalized difference vegetation index, distance to roads, distance to areas of human influence, distance to vertical scarps, elevation, land cover/land use class, and percentages of green vegetation, bare soil, and shadow from spectral mixture analysis. Distance to areas of human influence and distance to vertical scarps were the variables most closely associated with capuchin habitat suitability (permutation importance 31.7% and 21%, respectively). Stone tool use occurred in areas of lower elevation and higher percent green vegetation relative to other behavior. These results may inform efforts for conserving the unique stone tool use of this species, especially relevant due to the recent expansion and intensification of industrial agriculture in the region.

Management Implications of Aquifer Fractures on Ecosystem and Habitat Suitability for Panthers in Southern Florida

Our case study analyzed the proximity of previously mapped fractures in the aquifer matrix to 93 Florida panther (Puma concolor coryi) dens mapped from 2007-2016 in south Florida. Dens occurred in five counties (Collier = 77, Dade = 1, Hendry = 9, Lee = 5, and Monroe = 1) and three sub-basins of the Greater Everglades Basin (Big Cypress Swamp = 83, Caloosahatchee = 3, and Everglades = 7). Fractured aquifers occur worldwide, but are not the focus of habitat suitability studies, despite evidence that fractures influence plant species composition and density. Habitat alterations can occur many kilometers from the surface footprint of groundwater alterations in the regional Floridan aquifer system via preferential flow through fractures. Increased natural discharge from and recharge to the aquifer occur at fracture intersections. Greater induced recharge and habitat changes also may occur at fracture intersections. All dens were within 5 km of a previously mapped fracture; 36% and 74% were within 1 km and 2 km, respectively, of those fractures; and 47%, 74%, and 90% of dens were within 2 km, 3.25 km and 5 km, respectively, from the nearest fracture intersection. Results suggest fractures influence the suitability and/or availability of habitat for panther dens, selection of den sites, and availability as well as abundance of high quality prey items essential for the nutritional demands of successfully rearing panther kittens in the wild. We recommend more detailed investigations of: a) vegetation characteristics near dens, b) groundwater alterations and cumulative impacts of those alterations associated with fractures in panther habitat (e.g., altered plant species composition and density), and c) influence of aquifer fractures in all habitats underlain by fractures.

Seasonality of vegetation types of South America depicted by moderate resolution imaging spectroradiometer (MODIS) time series

The development, implementation and enforcement of policies involving the rational use of the land and the conservation of natural resources depend on an adequate characterization and understanding of the land cover, including its dynamics. This paper presents an approach for monitoring vegetation dynamics using high-quality time series of MODIS surface reflectance data by generating fraction images using Linear Spectral Mixing Model (LSMM) over South America continent. The approach uses physically-based fraction images, which highlight target information and reduce data dimensionality. Further dimensionality was also reduced by using the vegetation fraction images as input to a Principal Component Analysis (PCA). The RGB composite of the first three PCA components, accounting for 92.9% of the dataset variability, showed good agreement with the main ecological regions of South America continent. The analysis of 21 temporal profiles of vegetation fraction values and precipitation data over South America showed the ability of vegetation fractions to represent phenological cycles over a variety of environments. Comparisons between vegetation fractions and precipitation data indicated the close relationship between water availability and leaf mass/chlorophyll content for several vegetation types. In addition, phenological changes and disturbance resulting from anthropogenic pressure were identified, particularly those associated with agricultural practices and forest removal. Therefore the proposed method supports the management of natural and non-natural ecosystems, and can contribute to the understanding of key conservation issues in South America, including deforestation, disturbance and fire occurrence and management.

An enhanced vegetation index time series for the Amazon based on combined gap-filling approaches and quality datasets

Vegetation indices from MODIS data are subject to residual atmospheric noise, affecting processes requiring data continuity and analyses. This work reconstructed a time series of MODIS EVI mosaics for the Amazon using a novel combination of curve-fitting and spatiotemporal gap-filling. TIMESAT was used for initial curve fitting and gap filling, using a Double Logistic method and MODIS Usefulness values as weights. Pixels with large temporal gaps were handled by a spatiotemporal gap filling approach. The method scans Julian Days before and after the image being gap filled, searching for a good quality pixel (Pg) at the location of the pixel to be replaced. If Pg is found, a window is defined around it and a search for good quality pixels (Px) with spectral characteristics similar to Pg is performed. Window size increases during processing and pixel similarity uses Euclidean distance based on MOD13A2 reflectances. A good quality EVI value for the image being gap filled and at the location analogous to the minimum distance Px replaces the low quality pixel. Results from the spatiotemporal gap filling were then used in TIMESAT for smoothing. An evaluation strategy of the spatiotemporal approach involved flagging 5,000 randomly selected good-quality pixels as low-quality, running the algorithm and regressing the results with the original EVI values (R2= 0.62). The combined strategy was able to find replacement pixels and reduce spikes for images with high cloud cover and was used to rebuild a time series of EVI over the Amazon region for the period 2000-2010.