Exploring Spectral Data, Change Detection Information and Trajectories for Land Cover Monitoring over a Fire-Prone Area of Portugal

Alves, A.; Moraes, D.; Barbosa, B.; Costa, H.; Moreira, F.; Benevides, P.; Caetano, M. and Campagnolo, M. (2023). Exploring Spectral Data, Change Detection Information and Trajectories for Land Cover Monitoring over a Fire-Prone Area of Portugal. In Proceedings of the 9th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM; ISBN 978-989-758-649-1; ISSN 2184-500X, SciTePress, pages 87-97. DOI: 10.5220/0011993100003473---This research was conducted under the collaboration contract DGT-ISA 261/2021 with funding from Compete2020 (POCI-05-5762-FSE-000368), supported by the European Social Fund, and Centro Exploring Spectral Data, Change Detection Information and Trajectories for Land Cover Monitoring over a Fire-Prone Area of Portugal 95 de Investigação em Gestão de Informação (MagIC), Project UIDB/00239/2020 (Forest Research Centre), both supported by the Portuguese Foundation for Science and Technology (FCT)Land use/land cover (LULC) change detection and classification in maps based on automated data processing are becoming increasingly sophisticated in Earth Observation (EO). There is a growing number of annual maps available, with diverse but related production structures consisting primarily of classification and post-classification phases, the latter of which deals with inaccuracies of the first. The methodology production of the “Carta de Ocupação do Solo conjuntural” (COSc), a thematic land cover map of continental Portugal produced by the Directorate-General for Territory (DGT) mostly based on Sentinel-2 images classification, includes a semi-automatic phase of correction that combines expert knowledge and ancillary data in if-then-else rules validated by photointerpretation. Although this approach reduces misclassifications from an initial Random Forest (RF) prediction map, improving consistency between years and compliance with ecological succession, requires a lot of time-consuming semi-automatic procedures. This work evaluates the relevance of exploring an additional set of variables for automatic classification over disturbance-prone areas. A multitemporal dataset with 124 variables was analysed using data dimensionality reduction techniques, resulting in the identification of 35 major explanatory indicators, which were then used as inputs for RF classification with cross-validation. The estimated importance of the explanatory variables shows that composites of spectral bands, which are already included in the current COSc workflow, in conjunction with the inclusion of additional data namely, historical land cover information and change detection coefficients, from the Continuous Change Detection and Classification (CCDC) algorithm, are relevant for predicting land cover classes after disturbance. Since map updating is a more challenging task for disturbed pixels, we focused our analysis on locations where COSc indicated potential land cover change. Nonetheless, the overall classification accuracy for our experiments was 72.34 % which is similar to the accuracy of COSc for this region of Portugal. The findings suggest new variables that could improve future COSc maps.publishersversionpublishe

Thirty years of land cover and fraction cover changes over the Sudano-Sahel using landsat time series

Historical land cover maps are of high importance for scientists and policy makers studying the dynamic character of land cover change in the Sudano-Sahel, including anthropogenic and climatological drivers. Despite its relevance, an accurate high resolution record of historical land cover maps is currently lacking over the Sudano-Sahel. In this study, 30 m resolution historically consistent land cover and cover fraction maps are provided over the Sudano-Sahel for the period 1986–2015. These land cover/cover fraction maps are achieved based on the Landsat archive preprocessed on Google Earth Engine and a random forest classification/regression model, while historical consistency is achieved using the hidden Markov model. Using these historical maps, a multitude of variability in the dynamic Sudano-Sahel region over the past 30 years is revealed. On the one hand, Sahel-wide cropland expansion and the re-greening of the Sahel is observed in the discrete land cover classification. On the other hand, subtle changes such as forest degradation are detected based on the cover fraction maps. Additionally, exploiting the 30 m spatial resolution, fine-scale changes, such as smallholder or subsistence farming, can be detected. The historical land cover/cover fraction maps presented in this study are made available via an open-access platform

Tropical deforestation modelling : a comparative analysis of different predictive approaches. The case study of Peten, Guatemala.

The frequent use of predictive models for analysing of complex, natural or artificial, phenomena is changing the traditional approaches to environmental and hazard problems. The continuous improvement of computer performances allows more detailed numerical methods, based on space-time discretisation, to be developed and run for a predictive modeling of complex real systems, reproducing the way their spatial patterns evolve and pointing out the degree of simulation accuracy. In this contribution we present an application of several models (Geomatics, Neural Networks, Land Cover Modeler and Dinamica EGO) in a tropical training area of Peten, Guatemala. During the last decades this region, included into the Biosphere Maya reserve, has known a fast demographic raise and a subsequent uncontrolled pressure on its own geo-resources; the test area can be divided into several sub-regions characterized by different land use dynamics. Understand and quantify these differences permits a better approximation of real system; moreover we have to consider all the physic, socio-economic parameters which will be of use for represent the complex and sometime at random, human impact. Because of the absence of detailed data for our test area, nearly all information were derived from the image processing of 41 ETM+, TM and SPOT scenes; we pointed out the past environmental dynamics and we built the Input layers for the predictive models. The data from 1998 and 2000 were used during the calibration to simulate the Land Cover changes in 2003, selected as reference date for the validation. The basic statistics permit to highlight the qualities or the weaknesses for each model on the different sub-regions

Monitoring of Compliance in Western Australian Conservation Contracts

Contracting with private landholders for labor towards production of environmental services (payment for actions) or the environmental services themselves (payment for outcomes) is reliant on the environmental organization’s ability to monitor and assess the environmental outcomes provided. Inaccurate and costly assessment reduces the cost effectiveness of the contract. Different assessment technologies will have different impacts on the cost effectiveness and optimal contracting choice of the environmental organization. The paper compares the influence of field assessment by a local expert, and remote assessment via satellite imagery, on the optimal contracting decision for the Western Australian wheat belt.conservation, environmental, compliance, monitoring, enforcement, environmental regulation, Crop Production/Industries, Environmental Economics and Policy,

Toward the production of spatiotemporally consistent annual land cover maps using Sentinel-2 time series

Land cover (LC) maps generated by the classification of remote-sensing (RS) data allow for monitoring Earth processes and the dynamics of objects and phenomena. For accurate LC variability quantification in environmental monitoring, maps need to be spatiotemporally consistent, continually updated, and indicate permanent changes. However, producing frequent and spatiotemporally consistent LC maps is challenging because it involves balancing the need for temporal consistency with the risk of missing real changes. In this work, we propose a scalable and semiautomatic method for generating annual LC maps with labels that are consistently applied from one year to the next. It uses a Transformer deep-learning (DL) model as a classifier, which is trained on satellite time series (TS) of images using high performance computing (HPC). The trained model can generate stable maps by shifting the prediction window along the temporal direction. The effectiveness of the proposed approach is tested qualitatively and quantitatively on a multiannual Sentinel-2 dataset acquired over a three-year period in a study area located in the southern Italian Alps.</p

Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey

Time Series Classification and Extrinsic Regression are important and challenging machine learning tasks. Deep learning has revolutionized natural language processing and computer vision and holds great promise in other fields such as time series analysis where the relevant features must often be abstracted from the raw data but are not known a priori. This paper surveys the current state of the art in the fast-moving field of deep learning for time series classification and extrinsic regression. We review different network architectures and training methods used for these tasks and discuss the challenges and opportunities when applying deep learning to time series data. We also summarize two critical applications of time series classification and extrinsic regression, human activity recognition and satellite earth observation