Estimating ecological indicators of karst rocky desertification by linear spectral unmixing method

AbstractCoverage rates of vegetation and exposed bedrock are two key indicators of karst rocky desertification. In this study, the abundances of vegetation and exposed rock were retrieved from a hyperspectral Hyperion image using linear spectral unmixing method. The results were verified using the spectral indices of karst rocky desertification (KRDSI) and an integrated LAI spectral index: modified chlorophyll absorption ratio index (MCARI2). The abundances showed significant linear correlations with KRDSI and MCARI2. The coefficients of determination (R2) were 0.93, 0.66, and 0.84 for vegetation, soil, and rock, respectively, indicating that the abundances of vegetation and bedrock can characterize their coverage rates to a certain extent. Finally, the abundances of vegetation and bedrock were graded and integrated to evaluate rocky desertification in a typical karst region. This study suggests that spectral unmixing algorithm and hyperspectral remote sensing imagery can be used to monitor and evaluate karst rocky desertification

A Quantitative Analysis Of Hugelkultur And Its Potential Application On Karst Rocky Desertified Areas In China

A type of environmental degradation, karst rocky desertification (KRD) refers to areas where the soil loss exposes the bedrock and reduces the land’s ability to sustain life and is particularly widespread through the vast karst area of rural southwest China. Hugelkultur is a permaculture method that harnesses the wood decomposition process by burying logs beneath soil. We proposed that hugel beds will demonstrate a higher water holding capacity and enhance soil development, in a way that may show promise as a potential method to help alleviate problems of KRD. Soil samples were taken from hugel plots, non-hugel plots, and KRD-like areas around Bowling Green, Kentucky to determine respective moisture content and project the amount of soil water potentially held in a one-hectare field. Findings show hugels to demonstrate higher water holding capacity meaning they have potential implications for future productivity of agricultural in areas affected by KRD

Spatial Dimensions of Tower Karst and Cockpit Karst: A Case Study of Guilin, China

Tower karst (fenglin) and cockpit karst (fengcong) are two globally important representative styles of tropical karst. Previously proposed sequential and parallel development models are preliminary, and geomorphological studies to date do not provide enough satisfactory evidence to delineate the spatial and temporal relation between the two landscapes. This unclear interpretation of tower-cockpit relationships not only obscures understanding of the process-form dynamics of these tropical karst landforms, but also confuses their definition. Moreover, previous technological limitations, as well as the fragmental nature of the karst landscapes, has limited incorporation of geologic and other data into broad geospatial frameworks based on geographic information science (GIS) and remote sensing (RS), with such data being spatially and temporally disparate. This study incorporates various data sources to address the fenglin-fengcong relationship, particularly the recently postulated edge effect , which has not been examined in detail previously and which may hinge upon the interaction of multiple environmental variables, including geomorphology, vegetation and hydrology. To address these issues, this research combines geographic, geologic and hydrologic data, using GIS and RS technologies to test quantitatively the edge effect hypothesis. Specifically, there are four inter-related objectives of this study. The first is to develop a method to effectively differentiate fenglin and fengcong. The second is to extract optimally the vegetation information from satellite imagery, and investigate the correlation between tropical karst topography and its vegetation. The third is to combine the regional hydrologic data and solute transport models to estimate geochemicals control of fenglin and fengcong. The fourth one, perhaps the most important, is to test the edge effect hypothesis using the results from the other three objectives. There are several significant conclusions. First, DEM data are very useful for extracting profiles of complex surface landforms from satellite imagery. Second, the vegetation distribution varies between tower karst and cockpit karst and the differences correlate with topographic characteristics. The under-representation of vegetation on the south-southwest aspect of tower karst is remarkable, and its overall distribution is both less abundant and dispersed than in cockpit karst. Third, the edge effect exists in the Guilin area, with variable intensity and extension in different dimensions. In summary, the major contributions of the study include the following. First, the study has developed a method to classify fenglin-fengcong tropical karst effectively, even with the presence of shadows that would otherwise hinder traditional classification. Second, the study showed a variance of vegetation vitality within aspects of fenglin that might relate to its geomorphic difference from fengcong. Third, the study combined groundwater and solute transport models to estimate bicarbonate distributions, representing a novel systematic and quantitative approach to tropical karst studies

QUANTIFYING GRASSLAND NON-PHOTOSYNTHETIC VEGETATION BIOMASS USING REMOTE SENSING DATA

Non-photosynthetic vegetation (NPV) refers to vegetation that cannot perform a photosynthetic function. NPV, including standing dead vegetation and surface plant litter, plays a vital role in maintaining ecosystem function through controlling carbon, water and nutrient uptake as well as natural fire frequency and intensity in diverse ecosystems such as forest, savannah, wetland, cropland, and grassland. Due to its ecological importance, NPV has been selected as an indicator of grassland ecosystem health by the Alberta Public Lands Administration in Canada. The ecological importance of NPV has driven considerable research on quantifying NPV biomass with remote sensing approaches in various ecosystems. Although remote images, especially hyperspectral images, have demonstrated potential for use in NPV estimation, there has not been a way to quantify NPV biomass in semiarid grasslands where NPV biomass is affected by green vegetation (PV), bare soil and biological soil crust (BSC). The purpose of this research is to find a solution to quantitatively estimate NPV biomass with remote sensing approaches in semiarid mixed grasslands. Research was conducted in Grasslands National Park (GNP), a parcel of semiarid mixed prairie grassland in southern Saskatchewan, Canada. Multispectral images, including newly operational Landsat 8 Operational Land Imager (OLI) and Sentinel-2A Multi-spectral Instrument (MSIs) images and fine Quad-pol Radarsat-2 images were used for estimating NPV biomass in early, middle, and peak growing seasons via a simple linear regression approach. The results indicate that multispectral Landsat 8 OLI and Sentinel-2A MSIs have potential to quantify NPV biomass in peak and early senescence growing seasons. Radarsat-2 can also provide a solution for NPV biomass estimation. However, the performance of Radarsat-2 images is greatly affected by incidence angle of the image acquisition. This research filled a critical gap in applying remote sensing approaches to quantify NPV biomass in grassland ecosystems. NPV biomass estimates and approaches for estimating NPV biomass will contribute to grassland ecosystem health assessment (EHA) and natural resource (i.e. land, soil, water, plant, and animal) management

Desertification in Europe: mitigation strategies, land use planning: Proceedings of the advanced study course held in Alghero, Sardinia, Italy from 31 May to 10 June 1999

The present volume is based on lectures given at the course held in Alghero, Sardinia, Italy, from 31 May to 10 June 1999 on ‘Desertification in Europe: Mitigation Strategies, Land Use Planning’. It also contains presentations, given by the participating students, on their own research activities and interests. With the adoption of the International Convention to Combat Desertification, which represents a follow up of the Rio recommendations, this publication is timely. It highlights the specific situation of the Southern European regions and provides a comprehensive and state-of-the-art review of this complex issue

The 2014–2015 Lava Flow Field at Holuhraun, Iceland: Using Airborne Hyperspectral Remote Sensing for Discriminating the Lava Surface

Publisher's version (útgefin grein)The Holuhraun lava flow was the largest effusive eruption in Iceland for 230 years, with an estimated lava bulk volume of ~1.44 km3 and covering an area of ~84 km2. The six month long eruption at Holuhraun 2014–2015 generated a diverse surface environment. Therefore, the abundant data of airborne hyperspectral imagery above the lava field, calls for the use of time-efficient and accurate methods to unravel them. The hyperspectral data acquisition was acquired five months after the eruption finished, using an airborne FENIX-Hyperspectral sensor that was operated by the Natural Environment Research Council Airborne Research Facility (NERC-ARF). The data were atmospherically corrected using the Quick Atmospheric Correction (QUAC) algorithm. Here we used the Sequential Maximum Angle Convex Cone (SMACC) method to find spectral endmembers and their abundances throughout the airborne hyperspectral image. In total we estimated 15 endmembers, and we grouped these endmembers into six groups; (1) basalt; (2) hot material; (3) oxidized surface; (4) sulfate mineral; (5) water; and (6) noise. These groups were based on the similar shape of the endmembers; however, the amplitude varies due to illumination conditions, spectral variability, and topography. We, thus, obtained the respective abundances from each endmember group using fully constrained linear spectral mixture analysis (LSMA). The methods offer an optimum and a fast selection for volcanic products segregation. However, ground truth spectra are needed for further analysis.The first author was supported by the Indonesia Endowment Fund for Education (LPDP) Grant No. 20160222025516, European Network of Observatories and Research Infrastructures for Volcanology (EUROVOLC), The European Facility for Airborne Research (EUFAR) and Vinir Vatnajökuls during his Ph.D. project.Peer Reviewe

Quantifying the spatio-temporal dynamics of woody plant encroachment using an integrative remote sensing, GIS, and spatial modeling approach.

Despite a longstanding universal concern about and intensive research into woody plant encroachment (WPE)---the replacement of grasslands by shrub- and woodlands---our accumulated understanding of the process has either not been translated into sustainable rangeland management strategies or with only limited success. In order to increase our scientific insights into WPE, move us one step closer toward the sustainable management of rangelands affected by or vulnerable to the process, and identify needs for a future global research agenda, this dissertation presents an unprecedented critical, qualitative and quantitative assessment of the existing literature on the topic and evaluates the utility of an integrative remote sensing, GIS, and spatial modeling approach for quantifying the spatio-temporal dynamics of WPE.In sum, this dissertation demonstrates that integrative remote sensing, GIS, and spatial modeling approaches have enormous potential for addressing questions relevant to both rangelands research and management. However, it also suggests that much work remains to be done before we can translate our understanding of WPE into sustainable rangeland management strategies. In particular, we need to more fully explore the limitations and potentials of currently available data and techniques for quantifying WPE; build structures for data sharing and integration; develop a set of relevant standards; more actively engage in collaborative research efforts; and foster cross-cutting dialogues among researchers, managers, and communities.Specifically, this research demonstrates that the application of cutting-edge remote sensing techniques (Multiple Endmember Spectral Mixture Analysis, fuzzy logic-based change detection) to conventional medium spatial and spectral resolution imagery (Landsat Thematic Mapper, Landsat Enhanced Thematic Mapper Plus, ASTER) can be used to generate spatially explicit estimates of temporal changes in the abundance of woody plants and other surface materials. The research also shows that spatial models (Geographically Weighted Regression, Weights of Evidence, Weighted Logistic Regression) integrating this timely remotely sensed information with readily available GIS data can yield reasonably accurate estimates of an area's relative vulnerability to WPE and of the importance of anthropogenic and geoecological variables influencing the process. Such models may also be used for the testing of existing and generation of new scientific hypotheses about WPE, for evaluating the impact of natural or human-induced modifications of a landscape on the landscape's vulnerability to WPE, and for identifying target areas for conservation, restoration, or other management objectives.Findings from this research suggest that gaps in our current understanding of WPE and difficulties in devising sustainable rangeland management strategies are in part due to the complex spatio-temporal web of interactions between geoecological and anthropogenic variables involved in the process as well as limitations of presently available data and techniques. However, an in-depth analysis of the published literature also reveals that aforementioned problems are caused by two further crucial factors: the absence of information acquisition and reporting standards and the relative lack of long-term, large-scale, multi-disciplinary research efforts. The methodological framework proposed in this dissertation yields data that are easily standardized according to various criteria and facilitates the integration of spatially explicit data generated by a variety of studies. This framework may thus provide one common ground for scientists from a diversity of fields. Also, it has utility for both research and management

Time series analysis of high resolution remote sensing data to assess degradation of vegetation cover of the island of Socotra (Yemen)

The island of Socotra has long been in geographical isolation, hence nearly 30% of the plant species are believed to be endemic to the island. Until the end of 20th century there was only very little and incomplete information and literature about the vegetation on the island. This isolation broke down in 1990 with the country unification in which then the island received much attention. Subsequently the scientific knowledge of the local flora slowly increased, but many of plant species are now reported to be confined into small populations, hence being particularly vulnerable to habitat loss, overgrazing, as well as urban expansion. 1. The overall objective of this research attempted to assess and examine the trends of vegetation changes since 1972 to 2010 with the use of Landsat MSS, TM and ETM+ images and to investigate the related driving factors, such as rainfall, grazing pressure changes and underlying spatial variability of the landscape. This is to answer the overall question: Is there a trend in biomass, cover and species composition on Socotra Island over the last 40 years? If so, is that trend associated with the rainfall patterns? What are the drivers behind the vegetation change? And then how can we define changes in patterns or changes in this study area? 2. From a methodological point of view, our approach of systematically using remote sensing technology data proved scientifically an applicable tool to improve our understanding of the spatial complexity and heterogeneity of the vegetation cover as well as to provide a conceptual method with specific data for monitoring the changes over this time period. Our data obtained from these different Landsat sensors during the study period were - after many sophisticated processing steps - essentially able to provide time series information for Normalized Difference Vegetation Index (NDVI) data and to assess the long term trend in vegetation cover in the island. 3. Moreover, our approach combining supervised maximum-likelihood and unsupervised classification with the pre- and the post-classification approaches besides the knowledge based classification was table to provide sufficient results to distinguish and to map nine (9) terrestrial vegetation cover classes. The overall accuracy (compared with ground truth data) was about 91%, 77%, 70% and 72% for the images 2005, 1994, 1984 and 1972 respectively. Consecutively, the GIS analysis allowed estimates of highly valuable information as absolute areas and relative coverage of particular vegetation classes over the island with their spatial distribution and also their ecological requirements. Analysis of climatic conditions and NDVI 4. As a results of the complex topography of the study area and the wide climate range, with the guidance of prior knowledge of functional relationships between site parameters, ecosystem and the specific form of biological production, our work resulted in a division of the entire area into six variously sized ecosystem units, which were enough to properly depict the spatial heterogeneity of the rainfall and vegetation and to assist reflecting the influence and reaction between environmental parameters as well as it might have significance both for development of resources and for conservation of environment

The 2014-2015 lava flow field at Holuhraun: Deriving physical properties of the lava using multi remote sensing techniques and datasets

The purpose of this thesis is to employ remote sensing to study lava flow products during the 2014-2015 eruption at Holuhraun, Iceland. Multimodal remote sensing techniques and datasets were applied and developed for three study themes (1) deriving thermal properties from satellite infrared remote sensing, (2) differentiating lava surface using airborne hyperspectral remote sensing, and (3) quantifying lava surface roughness from elevation model acquired by airborne LiDAR. In the first study, we present a new approach based on infrared satellite images to derive thermal properties within the lava field during eruption and then compare the results with field measurement during the 2014-2015 eruption at Holuhraun. We develop a new spectral index for Landsat 8, named the thermal eruption index (TEI), based on the SWIR and TIR bands (bands 6 and 10). The purpose of the TEI consists mainly of two parts: (i) as a threshold for differentiating between different thermal domains; and (ii) to apply dualband technique to determine the maximum subpixel temperature (Th) of the lava. Lava surface roughness effects are accounted for by using the Hurst exponent (H), which is estimated from radar backscattering profiles. A higher H (smooth surface) generates thinner crust and high thermal flux meanwhile a lower H (rough surface) generates thicker crust and lower thermal flux. The total thermal flux peak is underestimated compared to other studies, although the trend shows good agreement with both field observation and other studies. In the second study, we focus on retrieving the lava surface types contributing to the signal recorded by airborne hyperspectral at the very top surface of the 2014-2015 lava flow field at Holuhraun. For this purpose, an airborne hyperspectral image acquired at Holuhraun with an AisaFENIX sensor onboard a NERC (Natural Environment Research Council Airborne Research Facility) campaign. For sub-pixel analysis, we used the sequential maximum angle convex cone (SMACC) algorithm to identify the spectral image endmembers and the linear spectral mixture analysis (LSMA) method was employed to retrieve the abundances. SMACC and LSMA methods offer a fast selection for volcanic product segregation. However, ground-truthing of spectra is recommended for future work. In the third study, we perform both the topographic position index (TPI) and onedimensional Hurst Exponent to derived lava flow unit roughness on the 2014-2015 lava flow field at Holuhraun using both airborne LiDAR and photogrammetry topography datasets. The roughness assessment was acquired from four lava flow features: (1) spiny pāhoehoe, (2) lava pond, (3) rubbly pāhoehoe lava, and (4) inflated channel. The TPI patterns on spiny lava and inflated channels show that the intermediate TPI values correspond to a small slope indicating a flat and smooth surface. Lava pond is characterized by low to high TPI values and forms a wave-like pattern. Meanwhile, irregular transitions patterns from low to high TPI values characterize lava with rough blocky surfaces, i.e. rubbly pāhoehoe to 'ā'a flows and lobes and their margins. These lobes and margins may give the impression of having similar roughness as the ”rough” surface on meters scale since this is an “apparent” roughness. On centimeters scale these multitudes of lobes feature coherent and smooth surfaces because they are pāhoehoe. The surface roughness of these lava features falls within the H range of 0.30 ± 0.05 to 0.76 ± 0.04. The rubbly pāhoehoe / 'ā'a has the roughest surface and the inflated lava channel along with pāhoehoe feature the smoothest surfaces among these four surface types. In general, the Hurst exponent values in the 2014-2015 lava field at Holuhraun has a strong tendency in 0.5, which is compatible with results from other study of geological surface roughness. Overall, this project provides an important insights into the application of remote sensing for monitoring and studying active lava flow fields and the techniques developed here will benefit such work in future events.Tilgangurinn með verkefninu var að rannsaka hraunrennsli og landform er urðu til í eldgosinu norðan Vatnajökuls 2014-2015 og kennt við Holuhraun. Fjölþátta fjarkönnunartækni og gögn úr gervitunglum og flugvélum voru nýtt við úrvinnslu verkefnisins. Rannsóknin sneri að þremur megin þáttum: (1) greiningu á eðli varmaútstreymis frá Holuhrauni, út frá innrauðri varmageislun sem mæld er með gervitunglagögnum (2) aðgreining á mismunandi hraunyfirborði, út frá ofur-fjölrófs mælingum úr lofti, og (3) greiningu og flokkun á yfirborðshrjúfleika Holuhrauns út frá hæðarlíkani er aflað var með LiDAR settur upp í flugvél. Fyrsti þáttur beindist að eðli varmaútstreymis á meðan á eldgosi stóð. Stuðst var við gervitunglagögn og mælingar með FLIR tækni á meðan eldgosið stóð yfir. Afraksturinn er nýr hitastuðull fyrir Landsat 8 og greiningu á eldgosum, (TEI). Hitastuðullinn TEI er unninn út frá SWIR og TIR böndum Landsat 8 (bönd 6 og 10). Með TEI næst fram tvennt: (i) að greina þröskuld milli tveggja hitasviða; og (ii) að beita tvíbanda tækni til að greina hitastig innan hverrar myndeiningar (Th) af hrauninu. Hrjúfleiki hraunsins hefur áhrif á varmaútstreymi, og er gert ráð fyrir honum með því að reikna Hurst veldisstuðulinn (H) og eru reiknuð út frá radar endurkasti hraunyfirborðs. Hátt H einkennir flatt og mjúkt yfirborð og þunna skorpu á hrauninu, á meðan að lágt H einkennir úfið yfirborð, þykka skorpu og lága varmaútgeislun. Heildar varmaútgeislun með þessari aðferð er heldur vanmetin en ofmetin í samanburði við aðrar aðferðir. Hinsvegar er góð fylgni með mælingum í mörkinni og samanburðar aðferðum. Annar hluti rannsóknarinnar sneri að túlkun ofur-fjölrófsgreininga á yfirborði Holuhrauns. Flogið var yfir Holuhraun sumarið 2015 með ofur-fjölrófsmæli (AisaFENIX) um borð í flugvél frá NERC (Natural Environment Research Council Airborne Research Facility). Við greiningu á yfirborði innan hverrar myndeiningar var, (i) notast við aðferð runubundins hámarkshorns kúptrar keilu (SMACC) til að finna útmörk ofurrófs mælinganna, (ii) blönduð línulega rófgreining (LSMA) var nýtt til að greina styrk eða gnægð innan myndeiningar. SMACC og LSMA aðferðirnar bjóða upp á mjög hraða greiningu á yfirborði og útfellingum efna á yfirborðið. Hins vegar þarf að gera fleiri rófmælingar á staðnum, til þess að auka notkunnargetu aðferðarinnar í hraungosum framtíðarinnar. Þriðji þáttur rannsóknarinnar sneri að því að greina landfræðilega stöðuvísitölu (TPI) og einvíðan Hurst veldisvísi til að meta hrjúfleika á hinu endanlega yfirborði Holuhrauns. Við þessa greiningu var notast við LiDAR mælingu af hrauninu og hæðagrunn unninn út frá ljósmyndum. Hrjúfleikinn var metinn fyrir fjögur yfirborð sem einkenna hraunið: (1) broddahraun „spiny pāhoehoe lava“, (2) hrauntjörn „lava pond“, (3) klumpahraun „rubbly pāhoehoe lava“ og (4) upptjakkaða hrauntröð „inflated lava channel“. TPI fyrir yfirborð (1) og (4) gefur meðalgildi sem einkennist af litlum halla og flötu yfirborði. Hrauntjörnin einkennist af lágum og háum TPI gildum sem endurspegla bylgjukennt mynstur. Hinsvegar einkennast hrjúfustu yfirborðin (3) og hraunjaðrar af óreglulegu mynstri lágra og hárra TPI gilda. Hrjúfleika stuðull þessara yfirborða H, er á bilinu 0.30 ± 0.05 til 0.76 ± 0.04. Mestur er hrjúfleiki kubbahrauna og minnstur er hrjúfleiki þandar hrauntraðar. Hurts veldisvísir Holuhrauns er nærri 0.5, en það er í mjög góðu samræmi við niðurstöður fyrri rannsókna á jarðfræðilegum yfirborðum. Í heild gefur verkefnið mikilvæga sýn á notagildi fjarkönnunaraðferða við rauntímaeftirlit með hraungosum, m.a. með þróun stuðla sem munu nýtast við atburði framtíðar. Þá voru tengsl hraunmyndana við ýmsa eiginleika eldgosa skýrð, sem aftur getur gefið vísbendingar um eðli fyrri atburða

Land Degradation Assessment with Earth Observation

This Special Issue (SI) on “Land Degradation Assessment with Earth Observation” comprises 17 original research papers with a focus on land degradation in arid, semiarid and dry-subhumid areas (i.e., desertification) in addition to temperate rangelands, grasslands, woodlands and the humid tropics. The studies cover different spatial, spectral and temporal scales and employ a wealth of different optical and radar sensors. Some studies incorporate time-series analysis techniques that assess the general trend of vegetation or the timing and duration of the reduction in biological productivity caused by land degradation. As anticipated from the latest trend in Earth Observation (EO) literature, some studies utilize the cloud-computing infrastructure of Google Earth Engine to cope with the unprecedented volume of data involved in current methodological approaches. This SI clearly demonstrates the ever-increasing relevance of EO technologies when it comes to assessing and monitoring land degradation. With the recently published IPCC Reports informing us of the severe impacts and risks to terrestrial and freshwater ecosystems and the ecosystem services they provide, the EO scientific community has a clear obligation to increase its efforts to address any remaining gaps—some of which have been identified in this SI—and produce highly accurate and relevant land-degradation assessment and monitoring tools