Margaret Kalacska

Estimating leaf area index from satellite imagery using Bayesian networks

In this study, we investigated the use of Bayesian networks for inferring tropical dry forest leaf area index (LAI) from satellite imagery in dry and wet seasons. LAI was chosen as the variable of interest because leaf area is the exchange surface between the photosynthetically active component of the canopy and the atmosphere. Initial network estimates were obtained from ground truth plot data with known forest structure, LAI, and satellite reflectance in the red and near-infrared bands (as observed by the Landsat 7 Enhanced Thematic Mapper Plus sensor). We tested the performance of the Bayesian networks with scoring rules and also with confidence and surprise scores. We evaluated the networks on a per-pixel basis and created both LAI maps of the study area as well predicted the probability maps for the highest LAI states. Results not only demonstrate the predictive power of a Bayesian network but also its explanatory power which is far beyond what is typically available with current pixel classifier approaches such as spectral vegetation indices or other approaches such as neural networks.

The Application of Remote Sensing for Detecting Mass Graves: An Experimental Animal Case Study from Costa Rica

Abstract:  Detection of mass graves utilizing the hyperspectral information in airborne or satellite imagery is an untested application of remote sensing technology. We examined the in situ spectral reflectance of an experimental animal mass grave in a tropical moist forest environment and compared it to an identically constructed false grave which was refilled with soil, but contained no cattle carcasses over the course of a 16‐month period. The separability of the in situ reflectance spectra was examined with a combination of feature selection and five different nonparametric pattern classifiers. We also scaled up the analysis to examine the spectral signature of the same experimental mass grave from an air‐borne hyperspectral image collected 1 month following burial. Our results indicate that at both scales (in situ and airborne), the experimental grave had a spectral signature that was distinct and therefore detectable from the false grave. In addition, we observed that vegetation regeneration was severely inhibited over the mass grave containing cattle carcasses for up to a period of 16 months. This experimental study has demonstrated the real utility of airborne hyperspectral imagery for the detection of a relatively small mass grave (5 m2) within a specific climatic zone. Other climatic zones will require similar actualistic modeling studies, but it is clear that the applications of this technology provide the international community with both an early detection tool and a tool for ongoing monitoring.

Hyperspectral remote sensing of tropical and sub-tropical forests

Tropical Dry Forest Phenology and Discrimination of Tropical Tree Species Using Hyperspectral Data, K.L. Castro-Esau and M. Kalacska Remote Sensing and Plant Functional Groups: Physiology, Ecology, and Spectroscopy in Tropical Systems, M. Alvarez-Anorve, M. Quesada, and E. de la Barrera Hyperspectral Data for Assessing Carbon Dynamics and Biodiversity of Forests, R. Lucas, A. Mitchell, and P. Bunting Effect of Soil Type on Plant Growth, Leaf Nutrient/Chlorophyll Concentration, and Leaf Reflectance of Tropical Tree and Grass Species, J.C. Calvo-Alvarado, M. Kalacska, G.A. Sanchez-Azofeifa, and L.S. Bell Spectral Expression of Gender: A Pilot Study with Two Dioecious Neotropical Tree Species, J.P. Arroyo-Mora, M. Kalacska, B.L. Caraballo, J.E. Trujillo, and O. Vargas Species Classification of Tropical Tree Leaf Reflectance and Dependence on Selection of Spectral Bands, B. Rivard, G.A. Sanchez-Azofeifa, S. Foley, and J.C. Calvo-Alvarado Discriminating Sirex noctilio Attack in Pine Forest Plantations in South Africa Using High Spectral Resolution Data, R. Ismail, O. Mutanga, and F. Ahmed Hyperspectral Remote Sensing of Exposed Wood and Deciduous Trees in Seasonal Tropical Forests, S. Bohlman Assessing Recovery Following Selective Logging of Lowland Tropical Forests Based on Hyperspectral Imagery, J.P. Arroyo-Mora, M. Kalacska, R.L. Chazdon, D.L. Civco, G. Obando-Vargas, and A.A. Sanchun Hernandez A Technique for Reflectance Calibration of Airborne Hyperspectral Spectrometer Data Using a Broad, Multiband Radiometer, T. Miura, A.R. Huete, L.G. Ferreira, E.E. Sano, and H. Yoshioka Assessment of Phenologic Variability in Amazon Tropical Rainforests Using Hyperspectral Hyperion and MODIS Satellite Data, A.R. Huete, Y. Kim, P. Ratana, K. Didan, Y.E. Shimabukuro, and T. Miura Hyperspectral Remote Sensing of Canopy Chemistry, Physiology, and Biodiversity in Tropical Rainforests, G.P. Asner Tropical Remote Sensing -Opportunities and Challenges, J.A. Gamon Index

Remote Sensing as a Tool for the Detection of Clandestine Mass Graves

ABSTRACT Mass graves in both an international and domestic setting often present a difficult, time-consuming and complicated detection problem. Historically, witness testimony has been relied upon to determine location. Other detection methods such as geophysical resistivity, magnetometry and ground penetrating radar may be, and have been, employed for the detection of graves with some success. However, these methods require that the grave locale is known fairly precisely and that personnel can physically walk the grave site to undertake data collection. Remote sensing (i.e. airborne and satellite imagery) is a detection tool that can be used to search larger geographical areas without placing investigative personnel at risk. Hyperspectral imagery acquired from aircraft or satellite provides over a hundred layers (bands) of data that can be selectively examined and analyzed to detect subtle changes in the reflectance spectra of the surface. Work presented here indicates, from an ongoing long-term experimental mass grave and an actual mass grave site, that remote sensing is a powerful detection tool, one which has the capacity to discriminate a mass grave from its surroundings in real-time or in certain cases even retrospectively.

Using Landsat Thematic Mapper records to map land cover change and the impacts of reforestation programmes in the borderlands of southeast Yunnan, China: 1990–2010

Abstract At the beginning of the new millennium, after a severe drought and destructive floods along the Yangtze River, the Chinese government implemented two large ecological rehabilitation and reforestation projects: the Natural Forest Protection Programme and the Sloping Land Conversion Programme. Using Landsat data from a decade before, during and after the inception of these programmes, we analyze their impacts along with other policies on land use, land cover change (LULCC) in southwest China. Our goal is to quantify the predominant land cover changes in four borderland counties, home to tens of thousands of ethnic minority individuals. We do this in three time stages (1990, 2000 and 2010). We use support vector machines as well as a transition matrix to monitor the land cover changes. The land cover classifications resulted in an overall accuracy and Kappa coefficient for forested area and cropland of respectively 91% (2% confidence interval) and 0.87. Our results suggest that the total forested area observed increased 3% over this 20-year period, while cropland decreased slightly (0.1%). However, these changes varied over specific time periods: forested area decreased between 1990 and 2000 and then increased between 2000 and 2010. In contrast, cropland increased and then decreased. These results suggest the important impacts of reforestation programmes that have accelerated a land cover transition in this region. We also found large changes in LULC occurring around fast growing urban areas, with changes in these peri-urban zones occurring faster to the east than west. This suggests that differences in socioeconomic conditions and specific local and regional policies have influenced the rates of forest, cropland and urban net changes, disturbances and net transitions. While it appears that a combination of economic growth and forest protection in this region over the past 20 years has been fairly successful, threats like drought, other extreme weather events and land degradation remain.

Videographic Analysis of Eriophorum Vaginatum Spatial Coverage in an Ombotrophic Bog

The use of Remotely Piloted Aircraft Systems (RPAS) as well as newer automated unmanned aerial vehicles is becoming a standard method in remote sensing studies requiring high spatial resolution (<1 m) and very precise temporal data to capture phenological events. In this study we use a low cost rotorcraft to map Eriophorum vaginatum at Mer Bleue, an ombrotrophic bog located east of Ottawa, ON, Canada. We focus on E. vaginatum because this sedge plays an important role in methane (CH4) gas exchange in peatlands. Using the remote controlled rotorcraft we were able to record, process, and mosaic 11.1 hectares of 4.5 cm spatial resolution imagery extracted from individual frames of video recordings (post georegistration RMSE 4.90 ± 4.95 cm). Our results, based on a supervised classification (96% accuracy) of the red, green, blue image planes, indicate a total tussock cover of 2,417 m2. Because the basal area of the plant is more relevant for calculating its contribution to the CH4 flux, the tussock area was related to the basal area from field data (R2 = 0.88, p < 0.0001). Our final results indicate a total basal area of 1,786 ± 62.8 m2. Based on temporal measurements of CH4 flux from the peatland as a whole that vary over the growing season, we estimate the E. vaginatum contribution to range from 3.0% to 17.3% of that total. Overall, our low cost approach was an effective non-destructive way to derive E. vaginatum coverage and estimate CH4 exchange over the growing season.

Detection of single graves by airborne hyperspectral imaging

Airborne hyperspectral imaging (HSI) was assessed as a potential tool to locate single grave sites. While airborne HSI has shown to be useful to locate mass graves, it is expected the location of single graves would be an order of magnitude more difficult due to the smaller size and reduced mass of the targets. Two clearings were evaluated (through a blind test) as potential sites for containing at least one set of buried remains. At no time prior to submitting the locations of the potential burial sites from the HSI were the actual locations of the sites released or shared with anyone from the analysis team. The two HSI sensors onboard the aircraft span the range of 408-2524nm. A range of indicators that exploit the narrow spectral and spatial resolutions of the two complimentary HSI sensors onboard the aircraft were calculated. Based on the co-occurrence of anomalous pixels within the expected range of the indicators three potential areas conforming to our underlying assumptions of the expected spectral responses (and spatial area) were determined. After submission of the predicted burial locations it was revealed that two of the targets were located within GPS error (10m) of the true burial locations. Furthermore, due to the history of the TPOF site for burial work, investigation of the third target is being considered in the near future. The results clearly demonstrate promise for hyperspectral imaging to aid in the detection of buried remains, however further work is required before these results can justifiably be used in routine scenarios.

Journey to Grow Linking Process to Outcome in Target Site Selection for Cannabis Cultivation

Objectives: To test whether there is a relationship between characteristics of the journey to an outdoor cannabis cultivation site and the total number of plants grown. Methods: Spatial data on the location of a sample of 132 cultivation sites derived from aerial detection policing efforts is used. TwoStep cluster analysis is employed to derive profiles of cultivation sites based on three measures of distance (i.e., distance to road, to water, and elevation) and regression analysis is used to examine their implications for the number of plants grown. Results: Four types of cultivation sites are found: prime, rugged, dry, and remote. Prime sites are fairly close to roads and water sources and are at relatively low elevation. They grow the greatest number of plants (mean = 171). Low elevation is the single most important factor correlate of operation size. Further, remote sites (both further from road and at higher elevation) tend to be larger. Conclusions: A majority of growers are capable of identifying “prime” locations in which the tradeoff between rewards and security appears to be maximized. This study is limited by the fact that there was no information available on the offenders themselves. Future research should employ interviews to clarify decision-making processes.

Spectral Reflectance of Polar Bear and Other Large Arctic Mammal Pelts; Potential Applications to Remote Sensing Surveys

Spectral reflectance within the 350–2500 nm range was measured for 17 pelts of arctic mammals (polar bear, caribou, muskox, and ringed, harp and bearded seals) in relation to snow. Reflectance of all pelts was very low at the ultraviolet (UV) end of the spectrum ( 90%), gradually dropped to near zero at 1500 nm, and then fluctuated between zero and 20% up to 2500 nm. All pelts could be distinguished from clean snow at many wavelengths. The polar bear pelts had higher and more uniform averaged reflectance from about 600–1100 nm than most other pelts, but discrimination was challenging due to variation in pelt color and intensity among individuals within each species. Results suggest promising approaches for using remote sensing tools with a broad spectral range to discriminate polar bears and other mammals from clean snow. Further data from live animals in their natural environment are needed to develop functions to discriminate among species of mammals and to determine whether other environmental elements may have similar reflectance.

Constrained Energy Maximization and Self-Referencing Method for Invisible Ink Detection from Multispectral Historical Document Images

This article deals with a serious form of degradation that often affects the readability of historical document images: the invisibility of text or ink. Due to wear over long periods of storage, the ink may become invisible to the human eye, an undesirable situation for scholars (i.e. Indian Ocean World project (IOW1, with whom we are working closely). Because only the class of ink is known a priori (reference), it can be considered as a target to be detected. This can be achieved by designing a linear filter that maximizes an energy function while minimizing the false detection of document image background elements. For each document image in which the ink is targeted, an internal reference is defined by a new self-referencing strategy. The proposed method is compared with a state-of-the-art methods, and validated on samples of real historical document images.