Estimating potential soil erosion for environmental services in a sugarcane growing area using multisource remote sensing data

Characterization of landscapes is crucial in modelling potential soil erosion to ascertain environmental services that are provided by the main land use in the ecosystem. Remote sensing techniques have proved successful in characterization of landscapes. In this study area of a rain-fed Kibos-Miwani sugar zone of Kenya, we used Normalized Difference Vegetation Index (NDVI) data extracted from satellite imagery to characterize the spatial and temporal heterogeneity of the vegetation conditions, and to model potential soil erosion. Data used included Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m NDVI acquired in the period 2000 to 2012; 30 m Landsat5 time series images acquired between November 2010 and June 2011; a 30 m digital elevation model (DEM); and ground observations (land cover and soil characteristics). Ground observations were cross tabulated and analysed under ISO 17025 laboratory procedures. Temporal NDVI was extracted directly from MODIS 250 m images to study the changes in seasonal vegetation at the region scale, while spatial NDVI was extracted by analysing Landsat 5 images at the field scale. NDVI extracted from Landsat images for a specific date, represented vegetation conditions for that simulation period. To compute potential soil erosion, we ran three simulations using the spatially explicit Fuzzy-based dynamic soil erosion model (FuDSEM) based on identified vegetative conditions, thanks to MODIS data. Input datasets included Landsat 5 NDVI, the slope, aspect, curvature and soil physical properties. Results of land cover presented sugarcane as the main land use, occupying 76% of the land scape. Results of NDVI analysis were consistent with crop management practices, illustrating a spatially heterogeneous land scape with varied vegetation conditions throughout the year. Results of the simulations were not significantly different for the different periods of the year. Out of simulations, we noted a homogeneous low erosion risk in areas with natural land cover with a global mean of 0.42. Medium to intense erosion risk in cropped areas was evident, with erosion risk varying from one pixel to the other. Simulation results suggest that crop management practices (planting and harvesting processes) are the drivers of erosion in sugar cane cultivated areas. (résumé d'auteur

Analysis of 1982-2006 sudano-sahelian vegetation dynamics using NOAA-AVHRR NDVI data and normalized rain-used efficiency

Land cover dynamic has to be taken into account to analyze changes in water resources, especially in vulnerable environment such as the Bani catchment in Mali. To study the land cover changes, we used NDVI AVHRR time series (1982-2006, 8 km spatial resolution), and monthly rainfall data from 65 stations. To interpret the NDVI trends in terms of land cover changes, we had to eliminate the inter-annual rainfall variability. We used the concept of the Rain Use Efficiency (RUE) which is the ratio between NDVI (a proxy of the Net Primary Production) and precipitation. RUE and rainfall were calculated and modeled on a 0.5° x 0.5° grid scale. For each cell we normalized the evolution of the RUE through time (RUE_cor), and calculated its trend over the 25 years period. The results indicate that RUE_cor is stable or in light increase for most of the grid cells. In areas where water is not a limiting factor of NPP, this trend is positively correlated to the fraction of cropped area changes, as determined from a couple of Landsat images acquired during a similar period. However, RUE is a complex concept and further investigations are needed to consolidate our results and conclusions. (Résumé d'auteur

Winter Conditions Influence Biological Responses of Migrating Hummingbirds

Conserving biological diversity given ongoing environmental changes requires the knowledge of how organisms respond biologically to these changes; however, we rarely have this information. This data deficiency can be addressed with coordinated monitoring programs that provide field data across temporal and spatial scales and with process-based models, which provide a method for predicting how species, in particular migrating species that face different conditions across their range, will respond to climate change. We evaluate whether environmental conditions in the wintering grounds of broad-tailed hummingbirds influence physiological and behavioral attributes of their migration. To quantify winter ground conditions, we used operative temperature as a proxy for physiological constraint, and precipitation and the normalized difference vegetation index (NDVI) as surrogates of resource availability. We measured four biological response variables: molt stage, timing of arrival at stopover sites, body mass, and fat. Consistent with our predictions, we found that birds migrating north were in earlier stages of molt and arrived at stopover sites later when NDVI was low. These results indicate that wintering conditions impact the timing and condition of birds as they migrate north. In addition, our results suggest that biologically informed environmental surrogates provide a valuable tool for predicting how climate variability across years influences the animal populations

TLS-bridged co-prediction of tree-level multifarious stem structure variables from worldview-2 panchromatic imagery: a case study of the boreal forest

In forest ecosystem studies, tree stem structure variables (SSVs) proved to be an essential kind of parameters, and now simultaneously deriving SSVs of as many kinds as possible at large scales is preferred for enhancing the frontier studies on marcoecosystem ecology and global carbon cycle. For this newly emerging task, satellite imagery such as WorldView-2 panchromatic images (WPIs) is used as a potential solution for co-prediction of tree-level multifarious SSVs, with static terrestrial laser scanning (TLS) assumed as a ‘bridge’. The specific operation is to pursue the allometric relationships between TLS-derived SSVs and WPI-derived feature parameters, and regression analyses with one or multiple explanatory variables are applied to deduce the prediction models (termed as Model1s and Model2s). In the case of Picea abies, Pinus sylvestris, Populus tremul and Quercus robur in a boreal forest, tests showed that Model1s and Model2s for different tree species can be derived (e.g. the maximum R2 = 0.574 for Q. robur). Overall, this study basically validated the algorithm proposed for co-prediction of multifarious SSVs, and the contribution is equivalent to developing a viable solution for SSV-estimation upscaling, which is useful for large-scale investigations of forest understory, macroecosystem ecology, global vegetation dynamics and global carbon cycle.This work was financially supported in part by the National Natural Science Foundation of China [grant numbers 41471281 and 31670718] and in part by the SRF for ROCS, SEM, China. (41471281 - National Natural Science Foundation of China; 31670718 - National Natural Science Foundation of China; SRF for ROCS, SEM, China)http://www-tandfonline-com.ezproxy.bu.edu/doi/abs/10.1080/17538947.2016.1247473?journalCode=tjde20Published versio

The effect of residential urban greenness on allergic respiratory diseases in youth: A narrative review

Background: Environmental exposures across the life course may be a contributor to the increased worldwide prevalence of respiratory and allergic diseases occurring in the last decades. Asthma and rhinoconjunctivitis especially contribute to the global burden of disease. Greenness has been suggested to have beneficial effects in terms of reduction of occurrence of allergic respiratory diseases. However, the available evidence of a relationship between urban greenness and childhood health outcomes is not yet conclusive. The current review aimed at investigating the current state of evidence, exploring the relationship between children's exposure to residential urban greenness and development of allergic respiratory diseases, jointly considering health outcomes and study design. Methods: The search strategy was designed to identify studies linking urban greenness exposure to asthma, rhinoconjunctivitis, and lung function in children and adolescents. This was a narrative review of literature following PRISMA guidelines performed using electronic search in databases of PubMed and Embase (Ovid) from the date of inception to December 2018. Results: Our search strategy identified 2315 articles; after exclusion of duplicates (n = 701), 1614 articles were screened. Following review of titles and abstracts, 162 articles were identified as potentially eligible. Of these, 148 were excluded following full-text evaluation, and 14 were included in this review. Different methods for assessing greenness exposure were found; the most used was Normalized Difference Vegetation Index. Asthma, wheezing, bronchitis, rhinoconjunctivitis, allergic symptoms, lung function, and allergic sensitization were the outcomes assessed in the identified studies; among them, asthma was the one most frequently investigated. Conclusions: The present review showed inconsistencies in the results mainly due to differences in study design, population, exposure assessment, geographic region, and ascertainment of outcome. Overall, there is a suggestion of an association between urban greenness in early life and the occurrence of allergic respiratory diseases during childhood, although the evidence is still inconsistent. It is therefore hard to draw a conclusive interpretation, so that the understanding of the impact of greenness on allergic respiratory diseases in children and adolescents remains difficult

Detection of irrigation inhomogeneities in an olive grove using the NDRE vegetation index obtained from UAV images

We have developed a simple photogrammetric method to identify heterogeneous areas of irrigated olive groves and vineyard crops using a commercial multispectral camera mounted on an unmanned aerial vehicle (UAV). By comparing NDVI, GNDVI, SAVI, and NDRE vegetation indices, we find that the latter shows irrigation irregularities in an olive grove not discernible with the other indices. This may render the NDRE as particularly useful to identify growth inhomogeneities in crops. Given the fact that few satellite detectors are sensible in the red-edge (RE) band and none with the spatial resolution offered by UAVs, this finding has the potential of turning UAVs into a local farmer’s favourite aid tool.Peer ReviewedPostprint (published version

Mapping evapotranspiration variability over a complex oasis-desert ecosystem based on automated calibration of Landsat 7 ETM+ data in SEBAL

Fragmented ecosystems of the desiccated Aral Sea seek answers to the profound local hydrologically- and water-related problems. Particularly, in the Small Aral Sea Basin (SASB), these problems are associated with low precipitation, increased temperature, land use and evapotranspiration (ET) changes. Here, the utility of high-resolution satellite dataset is employed to model the growing season dynamic of near-surface fluxes controlled by the advective effects of desert and oasis ecosystems in the SASB. This study adapted and applied the sensible heat flux calibration mechanism of Surface Energy Balance Algorithm for Land (SEBAL) to 16 clear-sky Landsat 7 ETM+ dataset, following a guided automatic pixels search from surface temperature T-s and Normalized Difference Vegetation Index NDVI (). Results were comprehensively validated with flux components and actual ET (ETa) outputs of Eddy Covariance (EC) and Meteorological Station (KZL) observations located in the desert and oasis, respectively. Compared with the original SEBAL, a noteworthy enhancement of flux estimations was achieved as follows: - desert ecosystem ETa R-2 = 0.94; oasis ecosystem ETa R-2 = 0.98 (P < 0.05). The improvement uncovered the exact land use contributions to ETa variability, with average estimates ranging from 1.24 mm to 6.98 mm . Additionally, instantaneous ET to NDVI (ETins-NDVI) ratio indicated that desert and oasis consumptive water use vary significantly with time of the season. This study indicates the possibility of continuous daily ET monitoring with considerable implications for improving water resources decision support over complex data-scarce drylands

Satellite remote sensing reveals a positive impact of living oyster reefs on microalgal biofilm development

Satellite remote sensing (RS) is routinely used for the large-scale monitoring of microphytobenthos (MPB) biomass in intertidal mudflats and has greatly improved our knowledge of MPB spatio-temporal variability and its potential drivers. Processes operating on smaller scales however, such as the impact of benthic macrofauna on MPB development, to date remain underinvestigated. In this study, we analysed the influence of wild Crassostrea gigas oyster reefs on MPB biofilm development using multispectral RS. A 30-year time series (1985-2015) combining high-resolution (30 m) Landsat and SPOT data was built in order to explore the relationship between C. gigas reefs and MPB spatial distribution and seasonal dynamics, using the normalized difference vegetation index (NDVI). Emphasis was placed on the analysis of a before-after control-impact (BACI) experiment designed to assess the effect of oyster killing on the surrounding MPB biofilms. Our RS data reveal that the presence of oyster reefs positively affects MPB biofilm development. Analysis of the historical time series first showed the presence of persistent, highly concentrated MPB patches around oyster reefs. This observation was supported by the BACI experiment which showed that killing the oysters (while leaving the physical reef structure, i.e. oyster shells, intact) negatively affected both MPB biofilm biomass and spatial stability around the reef. As such, our results are consistent with the hypothesis of nutrient input as an explanation for the MPB growth-promoting effect of oysters, whereby organic and inorganic matter released through oyster excretion and biodeposition stimulates MPB biomass accumulation. MPB also showed marked seasonal variations in biomass and patch shape, size and degree of aggregation around the oyster reefs. Seasonal variations in biomass, with higher NDVI during spring and autumn, were consistent with those observed on broader scales in other European mudflats. Our study provides the first multi-sensor RS satellite evidence of the promoting and structuring effect of oyster reefs on MPB biofilms

Modelling fire occurrence at regional scale. Does vegetation phenology matter?

Through its influence on biomass production, climate controls fuel availability affecting at the same time fuel moisture and flammability, which are the main determinants for fire ignition and propagation. Knowing the role of fuel phenology on fire ignition patterns is hence a key issue for fire prevention, detection, and development of mitigation strategies. The objective of this study is to quantify, at coarse scale, the role of the vegetation seasonal dynamics on fire ignition patterns of the National Park of Cilento, Vallo di Diano and Alburni (southern Italy) during 2000-2013. We applied a habitat suitability model to compare the multitemporal NDVI profiles at the locations of fire occurrence (the used habitat) with the NDVI profiles of the entire study area (the available habitat). Results demonstrated that, from May to October, wildfires occur preferentially at sites where the remotely-sensed NDVI observations have on average lower values than the available habitat. On the other hand, in the period November-April, wildfires tend to occur at sites where the corresponding NDVI observations have higher values than the available habitat. From a practical viewpoint, the proposed method can be implemented using many different ecogeographical variables simultaneously, thus integrating remotely sensed imagery with socioeconomic data, land cover, physiography or any landscape features that are thought to influence fire occurrence in the study area