Mapping the species richness and composition of tropical forests from remotely sensed data with neural networks

The understanding and management of biodiversity is often limited by a lack of data. Remote sensing has considerable potential as a source of data on biodiversity at spatial and temporal scales appropriate for biodiversity management. To-date, most remote sensing studies have focused on only one aspect of biodiversity, species richness, and have generally used conventional image analysis techniques that may not fully exploit the data's information content. Here, we report on a study that aimed to estimate biodiversity more fully from remotely sensed data with the aid of neural networks. Two neural network models, feedforward networks to estimate basic indices of biodiversity and Kohonen networks to provide information on species composition, were used. Biodiversity indices of species richness and evenness derived from the remotely sensed data were strongly correlated with those derived from field survey. For example, the predicted tree species richness was significantly correlated with that observed in the field (r=0.69, significant at the 95% level of confidence). In addition, there was a high degree of correspondence (?83%) between the partitioning of the outputs from Kohonen networks applied to tree species and remotely sensed data sets that indicated the potential to map species composition. Combining the outputs of the two sets of neural network based analyses enabled a map of biodiversity to be produce

HCMM satellite to take earth's temperature

The heat capacity mapping mission (HCMM), a low cost modular spacecraft built for the Applications Explorer Missions (AEM), was designed to allow scientists to determine the feasibility of using day/night thermal infrared remote sensor-derived data to: (1) discriminate various rock types and locate mineral resources; (2) measure and monitor surface soil moisture changes; (3) measure plant canopy temperatures at frequent intervals to determine transpiration of water and plant stress; and (4) measure urban heat islands. The design of the spacecraft (AEM-A), its payload, launch vehicle, orbit, and data collection and processing methods are described. Projects in which the HCMM data will be applied by 12 American and 12 foreign investigators are summarized

Land Cover Mapping and Change Analysis in Tropical Humid-highlands: Case of Ndakaini Water Reservoir in Central Kenya

We successfully used optical remote sensing approach to test the skills of post-classification change detection technique as well as techniques of circumventing the challenges of cloud/cloud-shadow contamination and of working in a data-scarce environment in tropical humid highlands. The aim was to generate an accurate estimate of current land cover distribution map and analyze land-cover change around Ndakaini area in Kenya.   Landsat imageries (TM and ETM+) acquired between 1985 and 2011 and corresponding to the study area was selected. Employing bands 3 and 4 of respective Landsat images, thresholding techniques, Boolean and masking operations were implemented in detecting cloud/cloud-shadows and subsequent removal and filling of gaps. In absence of other historical ancillary data about land cover types, a total of 278 points across the study area were captured from Google Earth and used to evaluate the accuracy of each of the generated land cover maps. From the results, cloud/cloud-shadow gaps were reduced immensely (e.g. 90% for the 1985 image and 82% for the 2011 image). With regard to quality of classification outputs, the respective land cover/land-use maps of 2000, 2005 and 2010 anniversaries had fairly high level of overall accuracy (64%, 79% and 68% respectively) and Kappa statistic (0.47, 0.69 and 0.53 respectively) while classification outputs of 1985 and 1995 yielded slightly lower overall accuracy (60%) and Kappa statistic (0.42). Post-classification change involving three land cover classes, tea plantation, forest/woodlot and annual crop fields denoted as others were successfully determined and conclusions based on trend analysis drawn. The satisfactory results of this study imply the usefulness of post-classification change detection method in generating information about land cover dynamics in tropical humid highlands especially when coupled with robust techniques that adequately circumvent the cloud and cloud-shadow problem and scarcity of ancillary data often common in these areas. Keywords: Post-classification change detection, thresholding and Boolean techniques, landcover change, tropical humid-highlands DOI: 10.7176/JNSR/10-8-03 Publication date: April 30th 202

Mapping and monitoring forest remnants : a multiscale analysis of spatio-temporal data

KEYWORDS : Landsat, time series, machine learning, semideciduous Atlantic forest, Brazil, wavelet transforms, classification, change detectionForests play a major role in important global matters such as carbon cycle, climate change, and biodiversity. Besides, forests also influence soil and water dynamics with major consequences for ecological relations and decision-making. One basic requirement to quantify and model these processes is the availability of accurate maps of forest cover. Data acquisition and analysis at appropriate scales is the keystone to achieve the mapping accuracy needed for development and reliable use of ecological models.The current and upcoming production of high-resolution data sets plus the ever-increasing time series that have been collected since the seventieth must be effectively explored. Missing values and distortions further complicate the analysis of this data set. Thus, integration and proper analysis is of utmost importance for environmental research. New conceptual models in environmental sciences, like the perception of multiple scales, require the development of effective implementation techniques.This thesis presents new methodologies to map and monitor forests on large, highly fragmented areas with complex land use patterns. The use of temporal information is extensively explored to distinguish natural forests from other land cover types that are spectrally similar. In chapter 4, novel schemes based on multiscale wavelet analysis are introduced, which enabled an effective preprocessing of long time series of Landsat data and improved its applicability on environmental assessment.In chapter 5, the produced time series as well as other information on spectral and spatial characteristics were used to classify forested areas in an experiment relating a number of combinations of attribute features. Feature sets were defined based on expert knowledge and on data mining techniques to be input to traditional and machine learning algorithms for pattern recognition, viz . maximum likelihood, univariate and multivariate decision trees, and neural networks. The results showed that maximum likelihood classification using temporal texture descriptors as extracted with wavelet transforms was most accurate to classify the semideciduous Atlantic forest in the study area.In chapter 6, a multiscale approach to digital change detection was developed to deal with multisensor and noisy remotely sensed images. Changes were extracted according to size classes minimising the effects of geometric and radiometric misregistration.Finally, in chapter 7, an automated procedure for GIS updating based on feature extraction, segmentation and classification was developed to monitor the remnants of semideciduos Atlantic forest. The procedure showed significant improvements over post classification comparison and direct multidate classification based on artificial neural networks.</p

NASA Tech Briefs Index, 1977, volume 2, numbers 1-4

Announcements of new technology derived from the research and development activities of NASA are presented. Abstracts, and indexes for subject, personal author, originating center, and Tech Brief number are presented for 1977

Utilizing Remote Sensing and Geospatial Techniques to Determine Detection Probabilities of Large Mammals

Whether a species is rare and requires protection or is overabundant and needs control, an accurate estimate of population size is essential for the development of conservation plans and management goals. Wildlife censuses in remote locations or over extensive areas are logistically difficult, frequently biased, and time consuming. My dissertation examined various techniques to determine the probability of detecting animals using remotely sensed imagery. We investigated four procedures that integrated unsupervised classification, texture characteristics, spectral enhancements, and image differencing to identify and count animals in remotely sensed imagery. The semi-automated processes had relatively high errors of over-counting (i.e., greater than 60%) in contrast to low (i.e. less than 19%) under-counting errors. The single-day image differencing had over-counting errors of 53% while the manual interpretation had over-counting errors of 19%. The probability of detection indicates the ability of a process or analyst to detect animals in an image or during an aerial wildlife survey and can adjust total counts to estimate the size of a population. The probabilities of detecting an animal in remotely sensed imagery with semi-automated techniques, single-day image differencing, or manual interpretation were high (e.g. ≥ 80%). Single-day image differencing resulted in the highest probability of detection suggesting this method could provide a new technique for managers to estimate animal populations, especially in open, grassland habitats. Remotely sensed imagery can be successfully used to identify and count animals in isolated or remote areas and improve management decisions. Sightability models, used to estimate population abundances, are derived from count data and the probability of detecting an animal during a census. Global positioning systems (GPS) radio-collared bison in the Henry Mountains of south-central Utah provided a unique opportunity to examine remotely sensed physiographic and survey characteristics for known occurrences of double-counted and missed animals. Bison status (detected, missed, or double-counted) was determined by intersecting helicopter survey paths with bison travel paths during annual helicopter surveys. The probability of detecting GPS-collared bison during the survey ranged from 91% in 2011 to 88% in 2012

Unmanned Aerial Systems (UASs) for Environmental Monitoring: A Review with Applications in Coastal Habitats

Nowadays the proliferation of small unmanned aerial systems or vehicles (UAS/Vs), formerly known as drones, coupled with an increasing interest in tools for environmental monitoring, have led to an exponential use of these unmanned aerial platforms for many applications in the most diverse fields of science. In particular, ecologists require data collected at appropriate spatial and temporal resolutions to describe ecological processes. For these reasons, we are witnessing the proliferation of UAV-based remote sensing techniques because they provide new perspectives on ecological phenomena that would otherwise be difficult to study. Therefore, we propose a brief review regarding the emerging applications of low-cost aerial platforms in the field of environmental sciences such as assessment of vegetation dynamics and forests biodiversity, wildlife research and management, map changes in freshwater marshes, river habitat mapping, and conservation and monitoring programs. In addition, we describe two applications of habitat mapping from UAS-based imagery, along the Central Mediterranean coasts, as study cases: (1) The upper limit of a Posidonia oceanica meadow was mapped to detect impacted areas, (2) high-resolution orthomosaic was used for supporting underwater visual census data in order to visualize juvenile fish densities and microhabitat use in four shallow coastal nurseries

Cloud Detection And Trace Gas Retrieval From The Next Generation Satellite Remote Sensing Instruments

Thesis (Ph.D.) University of Alaska Fairbanks, 2005The objective of this thesis is to develop a cloud detection algorithm suitable for the National Polar Orbiting Environmental Satellite System (NPOESS) Visible Infrared Imaging Radiometer Suite (VIIRS) and methods for atmospheric trace gas retrieval for future satellite remote sensing instruments. The development of this VIIRS cloud mask required a flowdown process of different sensor models in which a variety of sensor effects were simulated and evaluated. This included cloud simulations and cloud test development to investigate possible sensor effects, and a comprehensive flowdown analysis of the algorithm was conducted. In addition, a technique for total column water vapor retrieval using shadows was developed with the goal of enhancing water vapor retrievals under hazy atmospheric conditions. This is a new technique that relies on radiance differences between clear and shadowed surfaces, combined with ratios between water vapor absorbing and window regions. A novel method for retrieving methane amounts over water bodies, including lakes, rivers, and oceans, under conditions of sun glint has also been developed. The theoretical basis for the water vapor as well as the methane retrieval techniques is derived and simulated using a radiative transfer model