UAV-Based Mapping of Banana Land Area for Village-Level Decision-Support in Rwanda

Crop monitoring is crucial to understand crop production changes, agronomic practice decision-support, pests/diseases mitigation, and developing climate change adaptation strategies. Banana, an important staple food and cash crop in East Africa, is threatened by Banana Xanthomonas Wilt (BXW) disease. Yet, there is no up-to-date information about the spatial distribution and extent of banana lands, especially in Rwanda, where banana plays a key role in food security and livelihood. Therefore, delineation of banana-cultivated lands is important to prioritize resource allocation for optimal productivity. We mapped the spatial extent of smallholder banana farmlands by acquiring and processing high-resolution (25 cm/px) multispectral unmanned aerial vehicles (UAV) imageries, across four villages in Rwanda. Georeferenced ground-truth data on different land cover classes were combined with reflectance data and vegetation indices (NDVI, GNDVI, and EVI2) and compared using pixel-based supervised multi-classifiers (support vector models-SVM, classification and regression trees-CART, and random forest–RF), based on varying ground-truth data richness. Results show that RF consistently outperformed other classifiers regardless of data richness, with overall accuracy above 95%, producer’s/user’s accuracies above 92%, and kappa coefficient above 0.94. Estimated banana farmland areal coverage provides concrete baseline for extension-delivery efforts in terms of targeting banana farmers relative to their scale of production, and highlights opportunity to combine UAV-derived data with machine-learning methods for rapid landcover classification.

MAPPING AND CHANGE DETECTION OF MANGROVES AROUND MUMBAI USING REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEMS (GIS)

The study focuses on the mangroves in two districts namely, Mumbai and Mumbai Suburban. Mumbai, a coastal megacity, is a financial capital of the country with high population density. Mumbai is facing depletion of coastal resources due to land scarcity and large developmental projects. Thus, it is important to monitor these resources accurately and protect the stakeholders’ interest. Cloud-free satellite images of IRS P6 LISS III of 2004 and 2013 were procured from National Remote Sensing Centre (NRSC), Hyderabad. Two bands of visible and one band of NIR were utilized for landcover classification. Supervised Classification with Maximum Likelihood Estimator was used for the classification. The images were classified into various landcovers classes namely, Dense Mangroves, Sparse Mangroves and Others. Two software’s namely, ERDAS Imagine and GRAM++ were used for landcover classification and change detection analysis. It was observed that the total mangrove area in Mumbai in 2004 and 2013 was 50.52 square kilometers and 48.7 square kilometers respectively. In the year 2004 and 2013, contribution of sparse mangroves in the study area was 72.31 % and 87.06% respectively.

Myrtaceae floristic survey and vegetation distribution in a central portion of Chapada Diamantina, Brazil

Myrtaceae is an important family in the neotropics, being highlighted for its relevance in a wide range of vegetations, including those found within the Espinhaço Mountain Range (EMR). The main goal of the present work was to analyze Myrtaceae floristic composition and vegetation distribution in the municipality of Mucugê, Chapada Diamantina, Bahia. Specimens were analyzed in herbaria, two field expeditions were carried out and a landcover classification was performed through remote sensing. The compiled dataset presented 438 records with valid taxonomical identification, from which 374 records were seen by the authors in herbaria. Nine genera and 66 species of Myrtaceae were found, representing the largest Myrtaceae diversity recorded in Chapada Diamantina so far. Four vegetation classes were detected in the landcover classification (campo rupestre, cerrado sensu lato, caatinga sensu lato, and evergreen forest), encompassing 12 phytophysiognomies validated in situ. Comparisons on species diversity within different classes of vegetation were made by overlapping the vegetation classification and Myrtaceae records, also highlighting a directional sampling effort, being the areas threatened by the agriculture expansion both subsampled and poorly known.

OPERATIONAL PIPELINE FOR A GLOBAL CLOUD-FREE MOSAIC AND CLASSIFICATION OF SENTINEL-2 IMAGES

Global Earth observation from satellite images is an active research topic, driven by numerous applications, such as telecommunications, defence, natural hazard monitoring and urban management. The recently launched twin Sentinel-2 satellites acquire 13-band optical data with a 2–5 day revisit time, freely available for any use, and thus very valuable for global Earth observation. In this paper, we present a completely automatic operational chain for a global cloud-free mosaic and classification of Sentinel-2 images. The proposed pipeline enables producing the world 10-m cloud-free mosaic, and a 6-class landcover classification map.