Essential Cover and Closure

2000 Mathematics Subject Classification: 16N80, 16S70, 16D25, 13G05.

Analysis of urban land use and land cover changes: a case of study in Bahir Dar, Ethiopia

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies

Reconstructing historical forest cover change in the Lower Amazon floodplains using the LandTrendr algorithm

ABSTRACTThe Amazon várzeas are an important component of the Amazon biome, but anthropic and climatic impacts have been leading to forest loss and interruption of essential ecosystem functions and services. The objectives of this study were to evaluate the capability of the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) algorithm to characterize changes in várzeaforest cover in the Lower Amazon, and to analyze the potential of spectral and temporal attributes to classify forest loss as either natural or anthropogenic. We used a time series of 37 Landsat TM and ETM+ images acquired between 1984 and 2009. We used the LandTrendr algorithm to detect forest cover change and the attributes of "start year", "magnitude", and "duration" of the changes, as well as "NDVI at the end of series". Detection was restricted to areas identified as having forest cover at the start and/or end of the time series. We used the Support Vector Machine (SVM) algorithm to classify the extracted attributes, differentiating between anthropogenic and natural forest loss. Detection reliability was consistently high for change events along the Amazon River channel, but variable for changes within the floodplain. Spectral-temporal trajectories faithfully represented the nature of changes in floodplain forest cover, corroborating field observations. We estimated anthropogenic forest losses to be larger (1.071 ha) than natural losses (884 ha), with a global classification accuracy of 94%. We conclude that the LandTrendr algorithm is a reliable tool for studies of forest dynamics throughout the floodplain.

Cover plants and mineral nitrogen: effects on organic matter fractions in an oxisol under no-tillage in the cerrado

Cover plants are essential for the sustainability of no-tillage systems in tropical regions. However, information on the effects of these plants and N fertilization on soil organic matter fractions is still scarce. This study evaluated the effect of cover crops with different chemical composition and of N topdressing on the labile and humified organic matter fractions of an Oxisol of the Cerrado (savanna-like vegetation). The study in a randomized complete block design was arranged in split-plots with three replications. Four cover species were tested in the plots and the presence or absence of N topdressing in the subplot. The following cover species were planted in succession to corn for eight years: Urochloa ruziziensis; Canavalia brasiliensis M. ex Benth; Cajanus cajan (L.) Millsp; and Sorghum bicolor (L.) Moench. In general, the cultivation of U. ruziziensis increased soil C levels, particularly of C in the humic acid and particulate organic C fractions, which are quality indicators of soil organic matter. The C in humic substances and mineral organic C accounted for the highest proportions of total organic C, demonstrating the strong interaction between organic matter, Fe and Al oxides and kaolinite, which are predominant in these weathered soils of the Cerrado.

Cover Crops and Nitrogen Fertilization Effects on Nitrogen Soil Fractions under Corn Cultivation in a No-Tillage System

ABSTRACT The use of cover crops has recently increased and represents an essential practice for the sustainability of no-tillage systems in the Cerrado region. However, there is little information on the effects of nitrogen fertilization and cover crop use on nitrogen soil fractions. This study assessed changes in the N forms in soil cropped to cover crops prior to corn growing. The experiment consisted of a randomized complete block design arranged in split-plots with three replications. Cover crops were tested in the plots, and the N topdressing fertilization was assessed in the subplots. The following cover species were planted in succession to corn for eight years: Urochloa ruziziensis, Canavalia brasiliensis M. ex Benth, Cajanus cajan (L.) Millsp, and Sorghum bicolor (L.) Moench. After corn harvesting, the soil was sampled at depths of 0.00-0.10 and 0.10-0.20 m. The cover crops showed different effects at different soil depths. The soil cultivated with U. ruziziensis showed higher contents of total-N and particulate-N than the soil cultivated with C. cajan. Particulate-N was the most sensitive to changes in the soil management among the fractions of N assessed. The soil under N topdressing showed a lower content of available-N in the 0.10-0.20 m layer, which may be caused by the season in which the sampling was conducted or the greater uptake of the available-N by corn.

Land use/cover classification in the Brazilian Amazon using satellite images

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation‑based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi‑resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Among the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, have the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical‑based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

The ESA climate change initiative: satellite data records for essential climate variables

Observations of Earth from space have been made for over 40 years and have contributed to advances in many aspects of climate science. However, attempts to exploit this wealth of data are often hampered by a lack of homogeneity and continuity and by insufficient understanding of the products and their uncertainties. There is, therefore, a need to reassess and reprocess satellite datasets to maximize their usefulness for climate science. The European Space Agency has responded to this need by establishing the Climate Change Initiative (CCI). The CCI will create new climate data records for (currently) 13 essential climate variables (ECVs) and make these open and easily accessible to all. Each ECV project works closely with users to produce time series from the available satellite observations relevant to users' needs. A climate modeling users' group provides a climate system perspective and a forum to bring the data and modeling communities together. This paper presents the CCI program. It outlines its benefit and presents approaches and challenges for each ECV project, covering clouds, aerosols, ozone, greenhouse gases, sea surface temperature, ocean color, sea level, sea ice, land cover, fire, glaciers, soil moisture, and ice sheets. It also discusses how the CCI approach may contribute to defining and shaping future developments in Earth observation for climate science.

A land cover map of Latin America and the Caribbean in the framework of the SERENA project

Land cover maps at different resolutions and mapping extents contribute to modeling and support decision making processes. Because land cover affects and is affected by climate change, it is listed among the 13 terrestrial essential climate variables. This paper describes the generation of a land cover map for Latin America and the Caribbean (LAC) for the year 2008. It was developed in the framework of the project Latin American Network for Monitoring and Studying of Natural Resources (SERENA), which has been developed within the GOFC-GOLD Latin American network of remote sensing and forest fires (RedLaTIF). The SERENA land cover map for LAC integrates: 1) the local expertise of SERENA network members to generate the training and validation data, 2) a methodology for land cover mapping based on decision trees using MODIS time series, and 3) class membership estimates to account for pixel heterogeneity issues. The discrete SERENA land cover product, derived from class memberships, yields an overall accuracy of 84% and includes an additional layer representing the estimated per-pixel confidence. The study demonstrates in detail the use of class memberships to better estimate the area of scarce classes with a scattered spatial distribution. The land cover map is already available as a printed wall map and will be released in digital format in the near future. The SERENA land cover map was produced with a legend and classification strategy similar to that used by the North American Land Change Monitoring System (NALCMS) to generate a land cover map of the North American continent, that will allow to combine both maps to generate consistent data across America facilitating continental monitoring and modeling

Generation and analysis of the 2005 land cover map for Mexico using 250m MODIS data

Land cover plays a key role in global to regional monitoring and modeling because it affects and is being affected by climate change and thus became one of the essential variables for climate change studies. National and international organizations require timely and accurate land cover information for reporting and management actions. The North American Land Change Monitoring System (NALCMS) is an international cooperation of organizations and entities of Canada, the United States, and Mexico to map land cover change of North America's changing environment. This paper presents the methodology to derive the land cover map of Mexico for the year 2005 which was integrated in the NALCMS continental map. Based on a time series of 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) data and an extensive sample data base the complexity of the Mexican landscape required a specific approach to reflect land cover heterogeneity. To estimate the proportion of each land cover class for every pixel several decision tree classifications were combined to obtain class membership maps which were finally converted to a discrete map accompanied by a confidence estimate. The map yielded an overall accuracy of 82.5% (Kappa of 0.79) for pixels with at least 50% map confidence (71.3% of the data). An additional assessment with 780 randomly stratified samples and primary and alternative calls in the reference data to account for ambiguity indicated 83.4% overall accuracy (Kappa of 0.80). A high agreement of 83.6% for all pixels and 92.6% for pixels with a map confidence of more than 50% was found for the comparison between the land cover maps of 2005 and 2006. Further wall-to-wall comparisons to related land cover maps resulted in 56.6% agreement with the MODIS land cover product and a congruence of 49.5 with Globcover.

Content of essential oil of fennel seeds in two different stages of development

This experiment was carried out at Plant Production Sector, Agronomical Science College-Botucatu, S.P., Brazil, in March, 2000. The aim of this assay was to determine the yield of essential oil of fennel (Foeniculum vulgare Miller) in different stages of development. Essential oils were prepared by hydrodistillation from the seeds using of Clevenger apparatus. The water utilized for the extraction of essential oil was sufficient to cover 100 g of seeds and the mixture was distilled for three hours. The volume of essential oil in the graduated side -arm of Clevenger apparatus was observed. There were no significative difference statistic was observed (Tukey 5%) in percentage (v/m) of oil content, based on dry weight of green seeds compared with dry weight of mature seeds, when they were harvested in two different stages of development. There was significative difference statistic between data obtained of humidity content of green seeds when these were compared with mature seeds. These results shows that others specifics studies about adaptation of fennel in tropical conditions are necessary, because the obtained data were different of data described on literature.

Extensive Rhodolith Beds Cover the Summits of Southwestern Atlantic Ocean Seamounts

Calcium carbonate production by marine organisms is an essential process in the global budget of CO32-, and coralline reefs are the most important benthic carbonate producers. Crustose coralline algae (CCA) are well recognized as the most important carbonate builders in the tropical Brazilian continental shelf, forming structural reefs and extensive rhodolith beds. However, the distribution of CCA beds, as well as their role in CO32- mineralization in mesophotic communities and isolated carbonate banks, is still poorly known. To characterize the bottom features of several seamount summits in the Southwestern Atlantic (SWA), side-scan sonar records, remotely operated vehicle imagery, and benthic samples with mixed-gas scuba diving were acquired during two recent research cruises (March 2009 and February 2011). The tops of several seamounts within this region are relatively shallow (similar to 60 m), flat, and dominated by rhodolith beds (Vitoria, Almirante Saldanha, Davis, and Jaseur seamounts, as well as the Trindade Island shelf). On the basis of abundance, dimensions, vitality, and growth rates of CCA nodules, a mean CaCO3 production was estimated, ranging from 0.4 to 1.8 kg m(-2) y(-1) with a total production reaching 1.5 x 10(-3) Gt y(-1). Our results indicate that these SWA seamount summits provide extensive areas of shallow reef area and represent 0.3% of the world's carbonate banks. The importance of this habitat has been highly neglected, and immediate management needs must be fulfilled in the short term to ensure long-term persistence of the ecosystem services provided by these offshore carbonate realms.

Mapping decadal land cover changes in the woodlands of north eastern Namibia using the Landsat satellite archive (1975-2014)

Woodland savannahs provide essential ecosystem functions and services to communities. On the African continent, they are widely utilized and converted to intensive land uses. This study investigates the land cover changes of 108,038 km**2 in NE Namibia using multi-temporal, multi-sensor Landsat imagery, at decadal intervals from 1975 to 2014, with a post-classification change detection method and supervised Regression Tree classifiers. We discuss likely impacts of land tenure and reforms over the past four decades on changes in land use and land cover. These changes included losses, gains and exchanges between predominant land cover classes. Exchanges comprised logical conversions between woodland and agricultural classes, implying woodland clearing for arable farming, cropland abandonment and vegetation succession. The most dominant change was a reduction in the area of the woodland class due to the expansion of the agricultural class, specifically, small-scale cereal and pastoral production. Woodland area decreased from 90% of the study area in 1975 to 83% in 2014, while cleared land increased from 9% to 14%. We found that the main land cover changes are conversion from woodland to agricultural and urban land uses, driven by urban expansion and woodland clearing for subsistence-based agriculture and pastoralism.

Downwelling solar irradiance, upwelling solar radiance, sky leaving radiance, and cloud cover observed during ARCHEMHAB study (on Maria S. Merian Leg MSM21/3) from 2012-07-26 to 2012-08-10

The need to obtain ocean color essential climate variables (OC-ECVs) using hyperspectral technology has gained increased interest in recent years. Assessing ocean color on a large scale in high latitude environments using satellite remote sensing is constrained by polar environmental conditions. Nevertheless, on a small scale we can assess ocean color using above-water and in-water remote sensing. Unfortunately, above-water remote sensing can only determine apparent optical properties leaving the sea surface and is susceptible to near surface environmental conditions for example sky and sunglint. Consequently, we have to rely on accurate in-water remote sensing as it can provide both synoptic inherent and apparent optical properties of seawater. We use normalized water leaving radiance LWN or the equivalent remote sensing reflectance RRS from 27 stations to compare the differences in above-water and in-water OC-ECVs. Analysis of above-water and in-water RRS spectra provided very good match-ups (R2 > 0.97, MSE<1.8*10**-7) for all stations. The unbiased percent differences (UPD) between above-water and in-water approaches were determined at common OC-ECVs spectral bands (410, 440, 490, 510 and 555) nm and the classic band ratio (490/555) nm. The spectral average UPD ranged (5 - 110) % and band ratio UPD ranged (0 - 12) %, the latter showing that the 5% uncertainty threshold for ocean color radiometric products is attainable. UPD analysis of these stations West of Greenland, Labrador Sea, Denmark Strait and West of Iceland also suggests that the differences observed are likely a result of environmental and instrumental perturbations.

Soil compaction and vegetation cover in a Scots pine stand at the Mediterranean rangelands

Right development of ROOT SYSTEMS is essential to ensure seedling survival in the initial stages of natural regeneration processes. Soil compaction determines this development both because of its influence on soil Tª & moisture dynamics and for its direct effect on soil mechanical impedance to root growth. All this effects can be assessed as a whole through soil penetration resistance (Soil Strength) measurements. SOIL STRENGTH has been usually evaluated in forest research in connection with severe disturbances derived from heavy machinery works during forest operations. Nevertheless, undisturbed soils are also expected to show different levels of compaction for root development. Organic matter modifies soil structure and so on porosity, compaction and resultant soil resistance to penetration. Its concentration in surface layers is rather related to vegetation cover composition and density. So within forest stands, a relationship is expected to be found between VEGETATION COVER density and compaction measured as resistance to penetration (soil strength)

Ground cover and leaf area index relationship in a grass, legume and crucifer crop

Canopy characterization is essential for describing the interaction of a crop with its environment. The goal of this work was to determine the relationship between leaf area index (LAI) and ground cover (GC) in a grass, a legume and a crucifer crop, and to assess the feasibility of using these relationships as well as LAI-2000 readings to estimate LAI. Twelve plots were sown with either barley (Hordeum vulgare L.), vetch (Vicia sativa L.), or rape (Brassica napus L.). On 10 sampling dates the LAI (both direct and LAI-2000 estimations), fraction intercepted of photosynthetically active radiation (FIPAR) and GC were measured. Linear and quadratic models fitted to the relationship between the GC and LAI for all of the crops, but they reached a plateau in the grass when the LAI mayor que 4. Before reaching full cover, the slope of the linear relationship between both variables was within the range of 0.025 to 0.030. The LAI-2000 readings were linearly correlated with the LAI but they tended to overestimation. Corrections based on the clumping effect reduced the root mean square error of the estimated LAI from the LAI-2000 readings from 1.2 to less than 0.50 for the crucifer and the legume, but were not effective for barley.