Half a century of forest cover change along the Latvian-Russian border captured by object-based image analysis of Corona and Landsat TM/OLI data

• Published in: Remote sensing of environment 2020-11, Vol.249, p.112010, Article 112010
• Main Authors: Rendenieks, Zigmars, Nita, Mihai D., Nikodemus, Oļģerts, Radeloff, Volker C.
• Format: Article
• Language: English
• Subjects: Abandonment; Afforestation; Agricultural land; Agricultural land abandonment; Agricultural production; Corona; Corona imagery; Corridors; Cultivation; Deforestation; Forest mapping; Forests; Habitat fragmentation; Image analysis; Image classification; Image processing; Image segmentation; Land cover; Land use; Landsat; Landsat satellites; Latvia; Mechanization; Mosaics; Parameter sensitivity; Photogrammetry; Population decline; Population density; Remote sensing; Rural land use; Rural populations; Russia; Satellite imagery; Satellite images; Sensitivity analysis; Subsidies; Workflow
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2020.112010

After 1991, major events, such as the collapse of socialism and the transition to market economies, caused land use change across the former USSR and affected forests in particular. However, major land use changes may have occurred already during Soviet rule, but those are largely unknown and difficult to map for large areas because 30-m Landsat data is not available prior to the 1980s. Our goal was to analyze the rates and determinants of forest cover change from 1967 to 2015 along the Latvian-Russian border, and to develop an object-based image analysis approach to compare forest cover based on declassified Corona spy satellite images from 1967 with that derived from Landsat 5 TM and Landsat 8 OLI images from 1989/1990 and 2014/2015. We applied Structure-from-Motion photogrammetry to orthorectify and mosaic the scanned Corona images, and extracted forest cover from Corona and Landsat mosaics using object-based image analysis in eCognition and expert classification. In a sensitivity analysis, we tested how the scale parameters for the segmentation affected the accuracy of the change maps. We analyzed forest cover and forest patterns for our full study area of 22,209 km2, and applied propensity score matching approach to identify three Latvian-Russian pairs of 15 × 15 km cells, which we compared. We attained overall classification accuracies of 92% (Latvia) and 93% (Russia) for the forest/non-forest change maps of 1967–1989, and 91% (Latvia) and 93% (Russia) for 1989–2015, and our results were robust in regards to the segmentation scale parameter. Sample-based forest cover gain from 1967 to 1989 differed notably between the two countries (18.5% in Latvia and 23.6% in Russia), but was generally much higher prior to 1989 than from 1989 to 2015 (8.7% in Latvia and 9.7% in Russia). Furthermore, we found rapid de-fragmentation of forest cover, where forest core area increased, and proportions of isolated patches and forest corridors decreased, and this was particularly pronounced in Russia. Our findings highlight the need to study Soviet-time land cover and land use change, because rural population declines and major policy decisions such as the collectivization of agricultural production, merging of farmlands and agricultural mechanization led already during Soviet rule to widespread abandonment and afforestation of remote farmlands. After 1991, government subsidies for farming declined rapidly in both countries, but in Latvia, new financial aid from the EU be