Improving land cover classification using genetic programming for feature construction

Batista, J. E., Cabral, A. I. R., Vasconcelos, M. J. P., Vanneschi, L., & Silva, S. (2021). Improving land cover classification using genetic programming for feature construction. Remote Sensing, 13(9), [1623]. https://doi.org/10.3390/rs13091623Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.publishersversionpublishe

Analysis of Land Use/Land Cover Change Impacts Upon Ecosystem Services in Montane Tropical Forest of Rwanda: Forest Carbon Assessment and REDD+ Preparedness

Changes in forest cover especially changes within tropical forests, affect global climate change, together with ecosystems and forest carbon. Forests play a key role in both carbon emission and carbon sequestration. Efforts to reduce emissions through reduced deforestation and degradation of forests have become a common discussion among scientists and politicians under the auspices of the United Nations Programme on Reducing Emissions from Deforestation and Forest Degradation (UN-REDD Programme). This dissertation research assessed the impacts of land use land cover change upon ecosystem services from a protected area focusing on forest carbon distribution and vegetation mapping using remote sensing and geographical information systems (GIS). I also assessed Rwanda’s preparedness in the United Nations global program, Reducing Emissions from Deforestation and forest Degradation, Measuring, Monitoring, Reporting, and Verifying (REDD+MMRV). I carried out research in Nyungwe National Park (NNP), one of four National Parks of Rwanda. NNP is a montane tropical forest located in the Albertine Rift, one of the most biodiverse places in central and east Africa. I used remote sensing and field data collection from December 2011 and July 2012 in the western part of the Park to assess distribution and quantities of aboveground (ABG) forest carbon using generalized allometric functions. Using Landsat data together with 2009 high resolution color orthophotos and groundtruthing, I analyzed land cover changes between 1986 and 2011 for NNP. The land-use land cover change analysis showed that between 1986 and 1995 there was a minor increase in forest cover from 53% to 58% while from 1995-2003 a substantial decrease in forest cover occurred. Between 2003 and 2011 was a period of recovery with forest cover increasing by 59%. Vegetation analysis based on a 2009 Park biodiversity survey yielded 13 vegetation communities based on dominant and co-dominant species. Macaranga kilimandscharica was found to be dominant in three communities, representing 42% of the Park, and co-dominant in one community, representing 7% of the Park. While ~50% of the Park is secondary forest, the change in protection status has had a positive impact upon forest cover change within the Park. . Assessment of REDD+-MMRV readiness revealed that Rwanda has higher capacity and readiness in remote sensing and GIS than in forest inventory and carbon pools inventory. Lack of data to support development of emission models is a major problem at the national level which needs to be addressed