Vegetation Changes to the Okavango Delta, Botswana; A Comparison Between Managed and Non-Managed Areas

This capstone explores vegetation changes in the Okavango Delta area of Botswana. Spatial analyses were conducted using Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index satellite imagery and Geographic Information System land management data to compare vegetation changes within managed areas to determine whether management practices have had beneficial or adverse impacts. Rainfall, logging, and livestock data were utilized to attempt to find a link to precipitation, logging, or overgrazing. After analysis the livestock data were the only one that showed a correlation to the vegetation changes observed. Of the vegetation cover types analyzed, forest showed the most change, a significant decrease. Little difference in vegetation changes was found in the managed areas, indicating that land management techniques are ineffective.

Validación local de la temperatura superficial del mar del sensor MODIS en aguas someras del Mediterráneo occidental

La temperatura superficial del mar (SST) estimada a partir de los productos 11 μm diurnos y nocturnos y 4 μm nocturnos del sensor MODIS (Moderate Resolution Imaging Spectroradiometer) a bordo de la plataforma Aqua, han sido comparados con datos medidos in situ a tres profundidades diferentes (15, 50 y 100 cm) en una zona costera del Mediterráneo Occidental. Esta comparación ha permitido analizar la incertidumbre que existe en la estimación de este parámetro en aguas someras y próximas a la costa mediante imágenes de satélite de baja resolución espacial. Los resultados obtenidos demuestran que el producto diurno SST_11 μm, obtiene los estadísticos RMSE (error cuadrático medio) y r2 (coeficiente de correlación de Pearson) más ajustados con valores de 1°C y 0,96, respectivamente, para la profundidad 50 cm.

Sea ice habitat characteristics and number of narwhal (Monodon monoceros) sightings in Disko Bay, West Greenland

There is a paucity of information on abundance, densities, and habitat selection of narwhals Monodon monoceros in the offshore pack ice of Baffin Bay, West Greenland, despite the critical importance of winter foraging regions and considerable sea ice declines in the past decades. We conducted a double-platform visual aerial survey over a narwhal wintering ground to obtain pack ice densities and develop the first fully corrected abundance estimate using point conditional mark-recapture distance sampling. Continuous video recording and digital images taken along the trackline allowed for in situ quantification of winter narwhal habitat and for the estimation of fine-scale narwhal habitat selection and habitat-specific sighting probabilities. Abundance at the surface was estimated at 3484 (coefficient of variation [CV] = 0.46) including whales missed by observers. The fully corrected abundance of narwhals was 18 044 (CV = 0.46), or approximately one-quarter of the entire Baffin Bay population. The narwhal wintering ground surveyed (~9500 km**2) had 2.4 to 3.2% open water based on estimates from satellite imagery (NASA Moderate Resolution Imaging Spectroradiometer) and 1565 digital photographic images collected on the trackline. Thus, the ~18 000 narwhals had access to 233 km**2 of open water, resulting in an average density of ~77 narwhals/km**2 open water. Narwhal sighting probability near habitats with <10% or 10 to 50% open water was significantly higher than sighting probability in habitats with >50% open water, suggesting narwhals select optimal foraging areas in dense pack ice regardless of open water availability. This study provides the first quantitative ecological data on densities and habitat selection of narwhals in pack ice foraging regions that are rapidly being altered with climate change.

Modeling Light Use Efficiency in a Subtropical Mangrove Forest Equipped with CO2 Eddy Covariance °C

Despite the importance of mangrove ecosystems in the global carbon budget, the relationships between environmental drivers and carbon dynamics in these forests remain poorly understood. This limited understanding is partly a result of the challenges associated with in situ flux studies. Tower-based CO2 eddy covariance (EC) systems are installed in only a few mangrove forests worldwide, and the longest EC record from the Florida Everglades contains less than 9 years of observations. A primary goal of the present study was to develop a methodology to estimate canopy-scale photosynthetic light use efficiency in this forest. These tower-based observations represent a basis for associating CO2 fluxes with canopy light use properties, and thus provide the means for utilizing satellite-based reflectance data for larger scale investigations. We present a model for mangrove canopy light use efficiency utilizing the enhanced green vegetation index (EVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) that is capable of predicting changes in mangrove forest CO2 fluxes caused by a hurricane disturbance and changes in regional environmental conditions, including temperature and salinity. Model parameters are solved for in a Bayesian framework. The model structure requires estimates of ecosystem respiration (RE), and we present the first ever tower-based estimates of mangrove forest RE derived from nighttime CO2 fluxes. Our investigation is also the first to show the effects of salinity on mangrove forest CO2 uptake, which declines 5% per each 10 parts per thousand (ppt) increase in salinity. Light use efficiency in this forest declines with increasing daily photosynthetic active radiation, which is an important departure from the assumption of constant light use efficiency typically applied in satellite-driven models. The model developed here provides a framework for estimating CO2 uptake by these forests from reflectance data and information about environmental conditions.

Global surface net-radiation at 5 km from MODIS Terra

Reliable and fine resolution estimates of surface net-radiation are required for estimating latent and sensible heat fluxes between the land surface and the atmosphere. However, currently, fine resolution estimates of net-radiation are not available and consequently it is challenging to develop multi-year estimates of evapotranspiration at scales that can capture land surface heterogeneity and are relevant for policy and decision-making. We developed and evaluated a global net-radiation product at 5 km and 8-day resolution by combining mutually consistent atmosphere and land data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Terra. Comparison with net-radiation measurements from 154 globally distributed sites (414 site-years) from the FLUXNET and Surface Radiation budget network (SURFRAD) showed that the net-radiation product agreed well with measurements across seasons and climate types in the extratropics (Wilmott’s index ranged from 0.74 for boreal to 0.63 for Mediterranean sites). Mean absolute deviation between the MODIS and measured net-radiation ranged from 38.0 ± 1.8 W∙m−2 in boreal to 72.0 ± 4.1 W∙m−2 in the tropical climates. The mean bias was small and constituted only 11%, 0.7%, 8.4%, 4.2%, 13.3%, and 5.4% of the mean absolute error in daytime net-radiation in boreal, Mediterranean, temperate-continental, temperate, semi-arid, and tropical climate, respectively. To assess the accuracy of the broader spatiotemporal patterns, we upscaled error-quantified MODIS net-radiation and compared it with the net-radiation estimates from the coarse spatial (1° × 1°) but high temporal resolution gridded net-radiation product from the Clouds and Earth’s Radiant Energy System (CERES). Our estimates agreed closely with the net-radiation estimates from the CERES. Difference between the two was less than 10 W•m−2 in 94% of the total land area. MODIS net-radiation product will be a valuable resource for the science community studying turbulent fluxes and energy budget at the Earth’s surface.

Assessing trends in land use change in the Borana rangeland Ethiopia as one cause of greenhouse gas emissions and carbon sequestration variations

Landnutzungsänderungen sind eine wesentliche Ursache von Treibhausgasemissionen. Die Umwandlung von Ökosystemen mit permanenter natürlicher Vegetation hin zu Ackerbau mit zeitweise vegetationslosem Boden (z.B. nach der Bodenbearbeitung vor der Aussaat) führt häufig zu gesteigerten Treibhausgasemissionen und verminderter Kohlenstoffbindung. Weltweit dehnt sich Ackerbau sowohl in kleinbäuerlichen als auch in agro-industriellen Systemen aus, häufig in benachbarte semiaride bis subhumide Rangeland Ökosysteme. Die vorliegende Arbeit untersucht Trends der Landnutzungsänderung im Borana Rangeland Südäthiopiens. Bevölkerungswachstum, Landprivatisierung und damit einhergehende Einzäunung, veränderte Landnutzungspolitik und zunehmende Klimavariabilität führen zu raschen Veränderungen der traditionell auf Tierhaltung basierten, pastoralen Systeme. Mittels einer Literaturanalyse von Fallstudien in ostafrikanischen Rangelands wurde im Rahmen dieser Studie ein schematisches Modell der Zusammenhänge von Landnutzung, Treibhausgasemissionen und Kohlenstofffixierung entwickelt. Anhand von Satellitendaten und Daten aus Haushaltsbefragungen wurden Art und Umfang von Landnutzungsänderungen und Vegetationsveränderungen an fünf Untersuchungsstandorten (Darito/Yabelo Distrikt, Soda, Samaro, Haralo, Did Mega/alle Dire Distrikt) zwischen 1985 und 2011 analysiert. In Darito dehnte sich die Ackerbaufläche um 12% aus, überwiegend auf Kosten von Buschland. An den übrigen Standorten blieb die Ackerbaufläche relativ konstant, jedoch nahm Graslandvegetation um zwischen 16 und 28% zu, während Buschland um zwischen 23 und 31% abnahm. Lediglich am Standort Haralo nahm auch „bare land“, vegetationslose Flächen, um 13% zu. Faktoren, die zur Ausdehnung des Ackerbaus führen, wurden am Standort Darito detaillierter untersucht. GPS Daten und anbaugeschichtlichen Daten von 108 Feldern auf 54 Betrieben wurden in einem Geographischen Informationssystem (GIS) mit thematischen Boden-, Niederschlags-, und Hangneigungskarten sowie einem Digitales Höhenmodell überlagert. Multiple lineare Regression ermittelte Hangneigung und geographische Höhe als signifikante Erklärungsvariablen für die Ausdehnung von Ackerbau in niedrigere Lagen. Bodenart, Entfernung zum saisonalen Flusslauf und Niederschlag waren hingegen nicht signifikant. Das niedrige Bestimmtheitsmaß (R²=0,154) weist darauf hin, dass es weitere, hier nicht erfasste Erklärungsvariablen für die Richtung der räumlichen Ausweitung von Ackerland gibt. Streudiagramme zu Ackergröße und Anbaujahren in Relation zu geographischer Höhe zeigen seit dem Jahr 2000 eine Ausdehnung des Ackerbaus in Lagen unter 1620 müNN und eine Zunahme der Schlaggröße (>3ha). Die Analyse der phänologischen Entwicklung von Feldfrüchten im Jahresverlauf in Kombination mit Niederschlagsdaten und normalized difference vegetation index (NDVI) Zeitreihendaten dienten dazu, Zeitpunkte besonders hoher (Begrünung vor der Ernte) oder niedriger (nach der Bodenbearbeitung) Pflanzenbiomasse auf Ackerland zu identifizieren, um Ackerland und seine Ausdehnung von anderen Vegetationsformen fernerkundlich unterscheiden zu können. Anhand der NDVI Spektralprofile konnte Ackerland gut Wald, jedoch weniger gut von Gras- und Buschland unterschieden werden. Die geringe Auflösung (250m) der Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI Daten führte zu einem Mixed Pixel Effect, d.h. die Fläche eines Pixels beinhaltete häufig verschiedene Vegetationsformen in unterschiedlichen Anteilen, was deren Unterscheidung beeinträchtigte. Für die Entwicklung eines Echtzeit Monitoring Systems für die Ausdehnung des Ackerbaus wären höher auflösende NDVI Daten (z.B. Multispektralband, Hyperion EO-1 Sensor) notwendig, um kleinräumig eine bessere Differenzierung von Ackerland und natürlicher Rangeland-Vegetation zu erhalten. Die Entwicklung und der Einsatz solcher Methoden als Entscheidungshilfen für Land- und Ressourcennutzungsplanung könnte dazu beitragen, Produktions- und Entwicklungsziele der Borana Landnutzer mit nationalen Anstrengungen zur Eindämmung des Klimawandels durch Steigerung der Kohlenstofffixierung in Rangelands in Einklang zu bringen.

Burned Area Mapping in the North American Boreal Forest Using Terra-MODIS LTDR (2001–2011): A Comparison with the MCD45A1, MCD64A1 and BA GEOLAND-2 Products

An algorithm based on a Bayesian network classifier was adapted to produce 10-day burned area (BA) maps from the Long Term Data Record Version 3 (LTDR) at a spatial resolution of 0.05° (~5 km) for the North American boreal region from 2001 to 2011. The modified algorithm used the Brightness Temperature channel from the Moderate Resolution Imaging Spectroradiometer (MODIS) band 31 T31 (11.03 μm) instead of the Advanced Very High Resolution Radiometer (AVHRR) band T3 (3.75 μm). The accuracy of the BA-LTDR, the Collection 5.1 MODIS Burned Area (MCD45A1), the MODIS Collection 5.1 Direct Broadcast Monthly Burned Area (MCD64A1) and the Burned Area GEOLAND-2 (BA GEOLAND-2) products was assessed using reference data from the Alaska Fire Service (AFS) and the Canadian Forest Service National Fire Database (CFSNFD). The linear regression analysis of the burned area percentages of the MCD64A1 product using 40 km × 40 km grids versus the reference data for the years from 2001 to 2011 showed an agreement of R2 = 0.84 and a slope = 0.76, while the BA-LTDR showed an agreement of R2 = 0.75 and a slope = 0.69. These results represent an improvement over the MCD45A1 product, which showed an agreement of R2 = 0.67 and a slope = 0.42. The MCD64A1, BA-LTDR and MCD45A1 products underestimated the total burned area in the study region, whereas the BA GEOLAND-2 product overestimated it by approximately five-fold, with an agreement of R2 = 0.05. Despite MCD64A1 showing the best overall results, the BA-LTDR product proved to be an alternative for mapping burned areas in the North American boreal forest region compared with the other global BA products, even those with higher spatial/spectral resolution

Modelling global pyrogeography using data derived from satellite imagery

Doutoramento em Engenharia Florestal e dos Recursos Naturais - Instituto Superior de Agronomia - UL

Séries temporais de EVI/MODIS na identificação da dinâmica da soja em sistema plantio direto no Mato Grosso, Brasil.

As respostas espectrais monitoradas pelo sensor MODIS (MODerate-resolution Imaging Spectroradiometer) podem auxiliar não apenas na identificação dos cultivos, mas também no sistema de manejo adotado pelos produtores rurais de uma região. Objetivou-se com este trabalho avaliar respostas da soja através de índices de vegetação realçado (EVI) extraídos do MODIS como resposta a dinâmica da soja em sistema plantio direto no Estado de Mato Grosso. A área considerada abrange 23 municípios mais representativos na produção de soja no Estado, respondendo no ano agrícola de 2005-2006 a cerca de 65% da produção de soja no Estado. O índice biofísico EVI é eficiente para mapear áreas com cultivos de soja e identificar áreas que adotam práticas conservacionistas como as preconizadas pelo sistema plantio direto. A evolução espaço-temporal do plantio direto apontado pelas respostas espectrais aponta que houve influência sócio-cultural na adoção de práticas do sistema plantio direto, pelos produtores rurais do Estado de Mato Grosso.

Avaliação da dinâmica do carbono na fitomassa de cana-de-açúcar e pastagem.

A avaliação na alteração dos estoques de carbono na fitomassa agrícola ocorreu em uma área de 51.650 km2, compreendendo 125 municípios das regiões, central, norte e nordeste do Estado de São Paulo. Essas regiões possuem as cadeias de produção especializadas da cana-de-açúcar e das pastagens que estão presentes em praticamente quase todos os municípios da região e competem por área. Por meio da investigação do sensor Moderate Resolution Imaging Spectroradiometer (MODIS) e da interpretação de imagens do sensor Thematic Mapper (TM), avaliou-se a mudança de uso e cobertura da terra nos anos de 1988 e 2015. A expansão a área de cana-de-açúcar acelerou-se significativamente em toda a região e nos últimos 27 anos a área cultivada passou de 1.085.900 ha (21% da área de estudo) para 1.966.445 ha (38% da área de estudo). As áreas de pastagens reduziram-se de 1.397.724 ha (26% da área de estudo) para 684.323 ha (13% da área de estudo). A análise dos dados revelou que a cana-de-açúcar é capaz de acumular 107,2 t.ha.-1.ano-1 de carbono na fitomassa, enquanto as pastagens cultivadas somente 11,7 t.ha.-1.ano-1 de carbono. Em 1988 toda a área de cana-de-açúcar retinha na fitomassa 116 milhões de toneladas de CO2 e em 27 anos esse acúmulo passou para 211 milhões de toneladas de CO2 .ano-1. Constata-se com isso que o carbono pode, ao menos em parte, ser recomposto pelos agroecossistemas durante o subsequente uso do solo. Dos 125 municípios avaliados, 118 deles apresentaram elevação do carbono acumulado na fitomassa devido a incorporação de áreas de pastagens por cana-de-açúcar, num total de 592 mil ha. Somente nas áreas de pastagens que foram substituídas por cana-de-açúcar nesses 27 anos, promoveu-se a remoção de 54 milhões de toneladas de CO2 da atmosfera.

Field-oriented assessment of agricultural crops through temporal segmentation of modis VI data.

Monitoring agricultural crops constitutes a vital task for the general understanding of land use spatio-temporal dynamics. This paper presents an approach for the enhancement of current crop monitoring capabilities on a regional scale, in order to allow for the analysis of environmental and socio-economic drivers and impacts of agricultural land use. This work discusses the advantages and current limitations of using 250m VI data from the Moderate Resolution Imaging Spectroradiometer (MODIS) for this purpose, with emphasis in the difficulty of correctly analyzing pixels whose temporal responses are disturbed due to certain sources of interference such as mixed or heterogeneous land cover. It is shown that the influence of noisy or disturbed pixels can be minimized, and a much more consistent and useful result can be attained, if individual agricultural fields are identified and each field's pixels are analyzed in a collective manner. As such, a method is proposed that makes use of image segmentation techniques based on MODIS temporal information in order to identify portions of the study area that agree with actual agricultural field borders. The pixels of each portion or segment are then analyzed individually in order to estimate the reliability of the temporal signal observed and the consequent relevance of any estimation of land use from that data. The proposed method was applied in the state of Mato Grosso, in mid-western Brazil, where extensive ground truth data was available. Experiments were carried out using several supervised classification algorithms as well as different subsets of land cover classes, in order to test the methodology in a comprehensive way. Results show that the proposed method is capable of consistently improving classification results not only in terms of overall accuracy but also qualitatively by allowing a better understanding of the land use patterns detected. It thus provides a practical and straightforward procedure for enhancing crop-mapping capabilities using temporal series of moderate resolution remote sensing data.

Comparison of multitemporal MODIS-EVI smoothing algorithms and its contribution to crop monitoring.

Crop monitoring and more generally land use change detection are of primary importance in order to analyze spatio-temporal dynamics and its impacts on environment. This aspect is especially true in such a region as the State of Mato Grosso (south of the Brazilian Amazon Basin) which hosts an intensive pioneer front. Deforestation in this region as often been explained by soybean expansion in the last three decades. Remote sensing techniques may now represent an efficient and objective manner to quantify how crops expansion really represents a factor of deforestation through crop mapping studies. Due to the special characteristics of the soybean productions' farms in Mato Grosso (area varying between 1000 hectares and 40000 hectares and individual fields often bigger than 100 hectares), the Moderate Resolution Imaging Spectroradiometer (MODIS) data with a near daily temporal resolution and 250 m spatial resolution can be considered as adequate resources to crop mapping. Especially, multitemporal vegetation indices (VI) studies have been currently used to realize this task [1] [2]. In this study, 16-days compositions of EVI (MODQ13 product) data are used. However, although these data are already processed, multitemporal VI profiles still remain noisy due to cloudiness (which is extremely frequent in a tropical region such as south Amazon Basin), sensor problems, errors in atmospheric corrections or BRDF effect. Thus, many works tried to develop algorithms that could smooth the multitemporal VI profiles in order to improve further classification. The goal of this study is to compare and test different smoothing algorithms in order to select the one which satisfies better to the demand which is classifying crop classes. Those classes correspond to 6 different agricultural managements observed in Mato Grosso through an intensive field work which resulted in mapping more than 1000 individual fields. The agricultural managements above mentioned are based on combination of soy, cotton, corn, millet and sorghum crops sowed in single or double crop systems. Due to the difficulty in separating certain classes because of too similar agricultural calendars, the classification will be reduced to 3 classes : Cotton (single crop), Soy and cotton (double crop), soy (single or double crop with corn, millet or sorghum). The classification will use training data obtained in the 2005-2006 harvest and then be tested on the 2006-2007 harvest. In a first step, four smoothing techniques are presented and criticized. Those techniques are Best Index Slope Extraction (BISE) [3], Mean Value Iteration (MVI) [4], Weighted Least Squares (WLS) [5] and Savitzky-Golay Filter (SG) [6] [7]. These techniques are then implemented and visually compared on a few individual pixels so that it allows doing a first selection between the five studied techniques. The WLS and SG techniques are selected according to criteria proposed by [8]. Those criteria are: ability in eliminating frequent noises, conserving the upper values of the VI profiles and keeping the temporality of the profiles. Those selected algorithms are then programmed and applied to the MODIS/TERRA EVI data (16-days composition periods). Tests of separability are realized based on the Jeffries-Matusita distance in order to see if the algorithms managed in improving the potential of differentiation between the classes. Those tests are realized on the overall profile (comprising 23 MODIS images) as well as on each MODIS sub-period of the profile [1]. This last test is a double interest process because it allows comparing the smoothing techniques and also enables to select a set of images which carries more information on the separability between the classes. Those selected dates can then be used to realize a supervised classification. Here three different classifiers are tested to evaluate if the smoothing techniques as a particular effect on the classification depending on the classifiers used. Those classifiers are Maximum Likelihood classifier, Spectral Angle Mapper (SAM) classifier and CHAID Improved Decision tree. It appears through the separability tests on the overall process that the smoothed profiles don't improve efficiently the potential of discrimination between classes when compared with the original data. However, the same tests realized on the MODIS sub-periods show better results obtained with the smoothed algorithms. The results of the classification confirm this first analyze. The Kappa coefficients are always better with the smoothing techniques and the results obtained with the WLS and SG smoothed profiles are nearly equal. However, the results are different depending on the classifier used. The impact of the smoothing algorithms is much better while using the decision tree model. Indeed, it allows a gain of 0.1 in the Kappa coefficient. While using the Maximum Likelihood end SAM models, the gain remains positive but is much lower (Kappa improved of 0.02 only). Thus, this work's aim is to prove the utility in smoothing the VI profiles in order to improve the final results. However, the choice of the smoothing algorithm has to be made considering the original data used and the classifier models used. In that case the Savitzky-Golay filter gave the better results.

Produto modis associado à condição hídrica no solo para diferenciar padrões na vegetação no município de Dom Eliseu, Pará.

Objetivou avaliar a dinâmica de padrões na vegetação usando NDVI (Normalized Difference Vegetation Index) associado à oferta hídrica no município de Dom Eliseu, no Pará com base na reflectância em áreas com cultivos anuais de grãos e plantios florestais, nos períodos de maior e menor deficiência de água no solo. Foram analisados dados meteorológicos para calcular balanços hídricos (CAD = 300 mm) e respostas em NDVI (Normalized Difference Vegetation Index) extraídos do sensor MODIS (Moderate Resolution Imaging Spectroradiometer). As imagens-índice (NDVI) referentes aos meses de janeiro a dezembro de 2012 foram processadas no aplicativo Envi 4.7 e reclassificadas no ArcGIS10.1. Os resultados apontaram variações temporais ao longo do ano, tanto relacionados aos sistemas de agrícolas de produção, quanto aos remanescentes florestais os quais indicavam associações à oferta hídrica na região e possíveis respostas fenológicas. Em Dom Eliseu, o mês de maior valor em NDVI foi em abril com mais 60% do município expressando manutenção das folhas e da capacidade fotossintética das plantas, pois os valores em NDVI foram superiores a 0,6. No período de agosto a setembro ocorrem as menores cotas pluviais, ocasionando déficits hídricos que atingem valores superiores a 70 mm. Observou-se que as respostas em NDVI foram mais expressivas no mês de outubro, totalizando 16% da área de estudo com valores entre 0,2 a 0,3, evidenciando reduzida expressão em resposta espectral na biomassa dos remanescentes de vegetação e plantios florestais. Conclui-se que existe sensibilidade do NDVI em resposta à condição hídrica no solo. Ao contabilizar-se as diferenças entre a reflectâncias no infravermelho próximo e no vermelho divididos pela soma dessas reflectância, os baixos valores de NDVI, reforçam que no período de maior deficiência hídrica há queda de folhas, pois a superfície imageada, responde com valores mais elevados no solo do que na vegetação.

Ultra-high resolution imaging of the functional anatomy and plasticity of the human hippocampal-entorhinal circuitry

Magdeburg, Univ., Fak. für Naturwiss., Diss., 2015