Analyse du programme de développement durable Proambiente à Juina-MT

Rapport de terrainCe texte est le rapport d'études de l'analyse du programme de développement durable Proambiente implanté dans la commune de Juina au Mato Grosso, Brésil

Особенности развития гаптофитовых и динофитовых водорослей в олигоценовых бассейнах Северного Перитетиса

На основании изучения наннопланктона и диноцист проведены палеоэкологические реконструкции разных типов олигоценовых бассейнов Северного Перитетиса. Установлены ассоциации фитопланктона морских глубоководных, мелководных, относительно холодно- и тепловодных, а также лагунных палеобассейнов. Гаптофитовые (наннопланктон) присутствуют только в карбонатных прослоях пород, наиболее благоприятные условия для их развития были в Карпатском бассейне кросненского типа, в бассейнах самого раннего рюпеля Германии и юга Украины. Диноцисты представлены во всех типах олигоценовых бассейнов. Установлены корреляционные уровни по нанно- и динопланктону, позволяющие обосновать нижнюю и верхнюю границы олигоцена и уровень опреснения в середине рюпеля.Вивчення нанопланктону та диноцист з різних типів олігоценових басейнів Північного Перитетіса дозволило провести палеоекологічні реконструкції. Встановлені асоціації фітопланктону морських глибоководних, прибережно-мілководних відносно холодно- и тепловодних, лагунних, напівізольованих з ендеміками. Нанопланктон виявлено лише в карбонатних прошарках, найбільш сприятливі умови для його розвитку були у Карпатському басейні кросненського типу, у самому ранньому рюпелі Німеччини та півдня України. Диноцисти виявлені у всіх типах олігоценових басейнів. Встановлені корелятивні рівні за нано- та динопланктоном, які дозволяють обґрунтувати нижню і верхню границі олігоцену та рівень розпріснення в середині рюпелю.A study of nannofossils and dinocysts from different types of Oligocene basins of the Northern Peri-Thetys resulted in paleoecological reconstructions. The following phytoplankton associations were recognized: cold-water; littoral shallow-water relatively cold- and warm-water; lagoon; semi-isolated with endemics. Nannofossils were present in carbonaceous sediments, with more favorable conditions for them being in the Carpathian basin of Krosnensky type, in the earliest Rupelian of Germany and Southern Ukraine. Dinocysts were identified in all types of Oligocene basins. Correlation levels identified by nannofossils and dinocysts allowed us to substantiate the lower and upper Oligocene boundaries and degree of desalination in the Middle Rupelian

Assessing the optimal preprocessing steps of MODIS time series to map cropping systems in Mato Grosso, Brazil

The adoption of new cropping practices such as integrated Crop-Livestock systems (iCL) aims at improving the land use sustainability of the agricultural sector in the Brazilian Amazon. The emergence of such integrated systems, based on crop and pasture rotations over and within years, challenges the remote sensing community who needs to implement accurate and efficient methods to process satellite image time series (SITS) in order to come up with a monitoring protocol. These methods generally include a SITS preprocessing step which can be time consuming. The aim of this study is to assess the importance of preprocessing operations such as temporal smoothing and computation of phenological metrics on the mapping of main cropping systems (i.e. pasture, single cropping, double cropping and iCL), with a special emphasis on the iCL class. The study area is located in the state of Mato Grosso, an important producer of agriculture commodities located in the Southern Brazilian Amazon. SITS were composed of a set of 16-day composites of MODIS Vegetation Indices (MOD13Q1 product) covering a one year period between 2014 and 2015. Two widely used classifiers, i.e. Random Forest (RF) and Support Vector Machine (SVM), were tested using five data sets issued from a same SITS but with different preprocessing levels: (i) raw NDVI; (ii) raw NDVI + raw EVI; (iii) smoothed NDVI; (iv) NDVI-derived phenometrics; (v) raw NDVI + phenometrics. Both RF and SVM classification results showed that the “raw NDVI + raw EVI” data set achieved the highest performance (RF OA = 0.96, RF Kappa = 0.94, SVM OA = 0.95, SVM Kappa = 0.93), followed closely by the “raw NDVI” and the “raw NDVI + phenometrics” datasets. The “NDVI-derived phenometrics” alone achieved the lowest accuracies (RF OA = 0.58 and SVM OA = 0.66). Considering that the implementation of preprocessing steps is computationally expensive and does not provide significant gains in terms of classification accuracy, we recommend to use raw vegetation indices for mapping cropping practices in Mato Grosso, including the integrated Crop-Livestock systems

Monitoring land use changes around the indigenous lands of the Xingu basin in Mato Grosso, Brazil

International audienceIndigenous lands represent an efficient way to protect indigenous communities and environment in Brazil. However, these lands are also highly affected y the land use changes occuring in its surroundings. We quantified the land use changes in the Xingu basin based on MODIS EVI data between 2000 and 2006. We estimated the deforested area inside and outside the indigenous lands, the crop expansion and intensification around the protected areas. Our results indicate that, even if indigenous lands are efficient to limit deforestation (97.5% of deforestation is outside the indigenous lands), crop expansion and intensification (double crop systems) are increasing rapidly, what may imply pollution of headwaters of the Xingu river which crosses the protected area

Landscape drivers of mammal habitat use and richness in a protected area and its surrounding agricultural lands

Protected areas (PAs) are key to conserving biodiversity and ecosystem services globally, but their effectiveness increasingly depends on the ability of the surrounding agricultural areas to support biodiversity and secure connectivity at the landscape level. This requires monitoring the broader multi-use landscapes in which PAs exist and identifying the landscape characteristics that support rich, functional wildlife communities. Here, we investigated the species richness and habitat use patterns of a mammal community in relation to different landscape variables and land use and land cover (LULC) types in a PA and its surrounding agricultural lands in the Cerrado. We first used a hierarchical multi-species occupancy model with input camera trap data and eight landscape variables (vegetation productivity, phenology, and heterogeneity, distance to water, roads and settlements, and the PA, slope, and elevation) to estimate the species richness and habitat use of 29 mammal species across the landscape. We then analyzed the relationships between the species richness and habitat use and the landscape variables at the site level, as well as the distribution of species at the landscape level in relation to the different natural and agricultural LULC types

The Earth Observation Data for Habitat Monitoring (EODHaM) system

To support decisions relating to the use and conservation of protected areas and surrounds, the EU-funded BIOdiversity multi-SOurce monitoring System: from Space TO Species (BIO_SOS) project has developed the Earth Observation Data for HAbitat Monitoring (EODHaM) system for consistent mapping and monitoring of biodiversity. The EODHaM approach has adopted the Food and Agriculture Organization Land Cover Classification System (LCCS) taxonomy and translates mapped classes to General Habitat Categories (GHCs) from which Annex I habitats (EU Habitats Directive) can be defined. The EODHaM system uses a combination of pixel and object-based procedures. The 1st and 2nd stages use earth observation (EO) data alone with expert knowledge to generate classes according to the LCCS taxonomy (Levels 1 to 3 and beyond). The 3rd stage translates the final LCCS classes into GHCs from which Annex I habitat type maps are derived. An additional module quantifies changes in the LCCS classes and their components, indices derived from earth observation, object sizes and dimensions and the translated habitat maps (i.e., GHCs or Annex I). Examples are provided of the application of EODHaM system elements to protected sites and their surrounds in Italy, Wales (UK), the Netherlands, Greece, Portugal and India