Cerian Gibbes

Application of Object Based Classification and High Resolution Satellite Imagery for Savanna Ecosystem Analysis

Savanna ecosystems are an important component of dryland regions and yet are exceedingly difficult to study using satellite imagery. Savannas are composed are varying amounts of trees, shrubs and grasses and typically traditional classification schemes or vegetation indices cannot differentiate across class type. This research utilizes object based classification (OBC) for a region in Namibia, using IKONOS imagery, to help differentiate tree canopies and therefore woodland savanna, from shrub or grasslands. The methodology involved the identification and isolation of tree canopies within the imagery and the creation of tree polygon layers had an overall accuracy of 84%. In addition, the results were scaled up to a corresponding Landsat image of the same region, and the OBC results compared to corresponding pixel values of NDVI. The results were not compelling, indicating once more the problems of these traditional image analysis techniques for savanna ecosystems. Overall, the use of the OBC holds great promise for this ecosystem and could be utilized more frequently in studies of vegetation structure.

Time Series Analysis of Land Cover Change: Developing Statistical Tools to Determine Significance of Land Cover Changes in Persistence Analyses

Despite the existence of long term remotely sensed datasets, change detection methods are limited and often remain an obstacle to the effective use of time series approaches in remote sensing applications to Land Change Science. This paper establishes some simple statistical tests to be applied to NDVI-derived time series of remotely sensed data products. Specifically, the methods determine the statistical significance of three separate metrics of the persistence of vegetation cover or changes within a landscape by comparison to various forms of “benchmarks”; directional persistence (changes in sign relative to some fixed reference value), relative directional persistence (changes in sign relative to the preceding value), and massive persistence (changes in magnitude relative to the preceding value). Null hypotheses are developed on the basis of serially independent, normally distributed random variables. Critical values are established theoretically through consideration of the numeric properties of those variables, application of extensive Monte Carlo simulations, and parallels to random walk processes. Monthly pixel-level NDVI values for the state of Florida are analyzed over 25 years, illustrating the techniques’ abilities to identify areas and/or times of significant change, and facilitate a more detailed understanding of this landscape. The potential power and utility of such techniques is diverse within the area of remote sensing studies and Land Change Science, especially in the context of global change.

Linking vegetation response to seasonal precipitation in the Okavango–Kwando–Zambezi catchment of southern Africa

Understanding how intra-annual precipitation variability affects seasonal vegetation dynamics is critical for assessing the potential impacts of climate variability on vegetation structure and composition. This is important in semi-arid and arid ecosystems of southern Africa, where water is a limiting resource and timing of seasonal rainfall combined with the water storage capacity of different plant vegetation types affects remotely measured phenological cycles. Various lags and leads of savanna vegetation response to rainfall have been identified using remotely sensed data, but little attention has been given to vegetation greenness leading into the dry season. Vegetation production at the onset of the dry season interacts with the availability of water resources affecting fire dynamics, forage materials for wildlife and wildlife movement throughout the dry season. This is important for southern Africa as large proportions of the human population and economy are dependent on wildlife tourism. This study investigates the response of the end of the wet season vegetation production, as measured by Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) greenness, to the different months of wet season rainfall for different savanna vegetation types in a regional catchment of southern Africa. We estimated monthly precipitation using Tropical Rainfall Monitoring Mission 3B43 data and used MODIS 13A1 NDVI at a monthly time step for the years 2000–2009. Our model estimated greenness at the beginning of the dry season from the prior rainy season (October–April) precipitation using geographically weighted regression (GWR). The results show intra-annual wet season rainfall accounts for significant amounts of April NDVI variation. Overall intra-annual rainfall has a stronger effect for areas with greater proportions of grassland and dry, deciduous woodlands than for wetter, evergreen woodlands. These findings support previous research by highlighting the stronger association of grassland and open canopy woodlands to the end of the wet season monthly rainfall. This relationship is important for understanding seasonal precipitation effects on different savanna vegetation covers.

Influence of leaf and canopy characteristics on rainfall interception and urban hydrology

ABSTRACT Considering the rapid expansion of urban populations and the corresponding urbanization of landscapes, a dearth of knowledge exists regarding the role of urban vegetation in modulating urban ecosystem functioning. In response to the need for the development of new approaches to quantify ecohydrological processes along urban-to-rural gradients at alternate scales, this study explores the relationship between individual plant selection choices in landscaping and changes in urban hydrological functioning. This research examines differences in the variation of rainfall interception, leaf hydrophobicity, canopy structure, and water storage, between 13 species in an urban, semi-arid location. The species studied were selected based on resident preferences, and hence this research considers the role that urban residents play, through individual choices, in modifying the ecohydrology of an urban watershed. Rainfall interception, canopy surface storage, leaf hydrophobicity, and water droplet retention were significantly different between species. Results indicate that individual choice in plant selection for landscaping may influence urban hydrology.

LiDAR as a tool for archaeological research: a case study

Airborne light detection and ranging (LiDAR) is a technology that offers the ability to create highly detailed digital terrain models (DTMs) that expose low relief topographic features. The availability of these models holds potential to augment archaeological field research by producing visual imagery that can used to identify traces of ancient anthropogenic activity. This capability is particularly useful in hard to access areas and in areas of dense vegetation, where manual surveys are difficult to plan and to execute. Additionally, LiDAR technology is nonintrusive so that initial surveys can be performed without altering or destroying the integrity of the landscape and any features that it may contain. This paper explores the use of LiDAR within the field of archaeology and uses a case study approach to investigate the potential of LiDAR data for identifying earthworks dating back to the pre-Roman period in central England. Additionally, an evaluation of a technique to enhance the imagery in order to facilitate detecting human activity on the landscape is undertaken. Vegetation cover, particularly during leaf-on periods, can interfere with the ability of LiDAR to penetrate to the surface and can therefore impact its accuracy. The effect of vegetation cover on the ability of LiDAR to produce accurate DTMs is evaluated in relationship to its impact on the identification of archaeological features.

The Monitoring of Land-Cover Change and Management across Gradient Landscapes in Africa

Understanding the interactive effects of land management decisions and socioecological functioning is central to the study of human-environment interactions. Strategies such as designating or physically bounding parks are commonly used to conserve biodiversity and mitigate direct human impact on the environment. Remote sensing is an attractive source of data for monitoring such parks, as it provides a continuous source of consistent data across broad spatial extents. The current challenge to the field is its application in gradient landscapes where shifts from one land-cover class to another are subtle, as is the case in many savanna regions across Africa. This chapter explores implications of landscape monitoring and management strategies employed in eastern and southern Africa. We examine the suitability of various remote sensing approaches for quantifying land-use and land-cover change and how such studies can be used to monitor and inform the management of conservation areas in the broader African landscape.

Dynamics of the relationship between NDVI and SWIR32 vegetation indices in southern Africa: implications for retrieval of fractional cover from MODIS data

ABSTRACT Fractional cover of photosynthetic vegetation (FPV), non-photosynthetic vegetation (FNPV), and bare soil (FBS) has been retrieved for Australian tropical savannah based on linear unmixing of the two-dimensional response envelope of the normalized difference vegetation index (NDVI) and short wave infrared ratio (SWIR)32 vegetation indices (VI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data. The approach assumes that cover fractions are made up of a simple mixture of green leaves, senescent leaves, and bare soil. In this study, we examine retrieval of fractional cover using this approach for a study area in southern Africa with a more complex vegetation structure. Region-specific end-members were defined using Hyperion images from different locations and times of the season. These end-members were applied to a 10-year time series of MODIS-derived NDVI and SWIR32 (from 2002 to 2011) to unmix FPV, FNPV, and FBS. Results of validation with classified high-resolution imagery indicated major bias in estimation of FNPV and FBS, with regression coefficients for predicted versus observed data substantially less than 1.0 and relatively large intercept values. Examination with Hyperion images of the inverse relationship between the MODIS-equivalent SWIR32 index and the Hyperion-derived cellulose absorption index (CAI) to which it nominally approximates revealed: (1) non-compliant positive regression coefficients for certain vegetation types; and (2) shifts in slope and intercept of compliant regression curves related to day of year and geographical location. The results suggest that the NDVI–SWIR32 response cannot be used to approximate the NDVI–CAI response in complex savannah systems like southern Africa that cannot be described as simple mixtures of green leaves, dry herbaceous material high in cellulose, and bare soil. Methods that use a complete set of multispectral channels at higher spatial resolution may be needed for accurate retrieval of fractional cover in Africa.

Land use and land cover in a transitioning militarized landscape

ABSTRACT The repurposing of military lands is common in many parts of the world and presents a variety of conservation opportunities. This study examines land cover at Big Oaks National Wildlife Refuge, Indiana (U.S.A.) as it transitioned from military proving ground to wildlife refuge from 1985 to 2013. We use remote sensing, semi-structured interviews, and a review of planning and management documents to examine this transition. Limited change in land cover composition and distribution are detected, despite changes in use and management. This landscape similarity relates to similarities in land management practices, and the impact of landscape history on current management practices. The findings suggest that military use and conservation objectives at this site yield similar land covers and are not necessarily in contrast to each other. As military base closures continue, the potential to maintain and expand conservation opportunities on these lands will likely grow in importance.

Integrating remotely sensed fires for predicting deforestation for REDD

Fire is an important tool in tropical forest management, as it alters forest composition, structure, and the carbon budget. The United Nations program on Reducing Emissions from Deforestation and Forest Degradation (REDD+) aims to sustainably manage forests, as well as to conserve and enhance their carbon stocks. Despite the crucial role of fire management, decision-making on REDD+ interventions fails to systematically include fires. Here, we address this critical knowledge gap in two ways. First, we review REDD+ projects and programs to assess the inclusion of fires in monitoring, reporting, and verification (MRV) systems. Second, we model the relationship between fire and forest for a pilot site in Colombia using near-real-time (NRT) fire monitoring data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). The literature review revealed fire remains to be incorporated as a key component of MRV systems. Spatially explicit modeling of land use change showed the probability of deforestation declined sharply with increasing distance to the nearest fire the preceding year (multi-year model area under the curve [AUC] 0.82). Deforestation predictions based on the model performed better than the official REDD early-warning system. The model AUC for 2013 and 2014 was 0.81, compared to 0.52 for the early-warning system in 2013 and 0.68 in 2014. This demonstrates NRT fire monitoring is a powerful tool to predict sites of forest deforestation. Applying new, publicly available, and open-access NRT fire data should be an essential element of early-warning systems to detect and prevent deforestation. Our results provide tools for improving both the current MRV systems, and the deforestation early-warning system in Colombia.

An Examination of Student Veteran Education Pathways at an American University.

Abstract Military veterans are enrolling in higher education at the highest rates since the Second World War. This research seeks to examine how military experiences related to student experiences within the discipline of Geography. We use a survey instrument to measure student motivations, attitudes, and aspirations for declared Geography majors. Given a high presence of military connected students, we then examine the similarities and differences in motivations, attitudes, and aspirations between military connected and non-military students. Findings suggest that there are similarities between military and non-military students with regard to motivating factors for selecting Geography as a major, there are differences with regards to attitudes towards cultural geography, and differences in how students perceive their future interactions with the environment. Differences in demographics and travel experiences also are identified and likely contribute to shaping undergraduate geography experiences. The results offer useful insight on current Geography student needs, and assist faculty and departments in tailoring learning based on student experience.