Development of a convolutional neural network to accurately detect land use and land cover.

The detection and modeling of Land Use and Land Cover (LULC) play pivotal roles in natural resource management, environmental modeling and assessment, and ecological connectivity management. However, addressing LULCC detection and modeling constitutes a complex data-driven process. In the present study, a Convolutional Neural Network (CNN) is employed due to its great potential in image classification. The development of these tools applies the deep learning method. A methodology has been developed that classifies the set of land uses in a natural area of special protection. This study area covers the Sierra del Cando (Galicia, northwest Spain), considered by the European Union as a Site of Community Interest and integrated in the Natura 2000 Network. The results of the CNN model developed show an accuracy of 91 % on training dataset and 88 % on test dataset. In addition, the model was tested on images of the study area, both from Sentinel-2 and PNOA. Despite some confusion especially in the residential class due to the characteristics in this area, CNNs prove to be a powerful classification tool.•Classifications based on a CNN model•LULC are classified into 10 different classes•Training and test accuracy are 91 % and 88 %, respectively.

Geospatial mapping of carbon estimates for forested areas using the InVEST model and Sentinel-2: A case study in Galicia (NW Spain).

CO2 emissions have increased exponentially in recent years, so measuring and quantifying carbon sequestration is a step towards sustainable forest management and combating climate change. The overall goal of this study is to develop an accurate model for estimating carbon storage and sequestration for forest areas of the Atlantic Biogeographic Region. Specifically, the modelling and field sampling are carried out in the municipality of Baiona (Galicia, NW Spain), which was selected as a representative biome of this region. The methodology consists of carrying out two object-based image analysis (OBIA) classifications in spring and autumn to observe possible stocks of seasonal differences. Two carbon storage and sequestration models are built up (model 1 and model 2): model 1 for forest areas only and model 2 including all other land cover in the study area. Sentinel-2 geospatial data for 2021, Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) tools and geographic information systems (GIS) are used. A Kappa index of 0.92 is obtained for both classifications, thus ruling out any notable seasonal differences in the images used. The results from both models indicate that it is land covers associated with forest uses which store the most carbon in the study area, accounting for >50 % more than the other land covers. It is concluded that the methodology and data used are very useful for quantifying ecosystem services, which will help the governance of the region by implementing measures to mitigate some of the effects of climate change and help to create silvicultural models for the sustainable management of the Atlantic Biogeographic Region.