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Geoprocessing of archival aerial photos and their scientific applications: A review

Kostrzewa Adam

Reports on Geodesy and Geoinformatics

2024

Vol. 118

1--16

Poland as well as other countries keep extensive collections of 20th and 21st-century aerial photos, which are underexploited compared to such other archival materials as satellite imagery. Meanwhile, they offer significant research potential in various areas, including urban development, land use changes, and long-term environmental monitoring. Archival photographs are detailed, often obtained every five to ten years, and feature high resolution, from 20 cm to 1 m. Their overlap can facilitate creating precise digital models that illustrate topography and land cover, which are essential variables in many scientific contexts. However, rapidly transforming these photographs into geographically accurate measurements of the Earth’s surface poses challenges. This article explores the obstacles in automating the processing of historical photographs and presents the main scientific research directions associated with these images. Recent advancements in enhancing workflows, including the development of modern digital photogrammetry tools, algorithms, and machine learning techniques are also discussed. These developments are crucial for unlocking the full potential of aerial photographs, making them easier accessible and valuable for a broader range of scientific fields. These underutilized photographs are increasingly recognized as vital in various research domains due to technological advancements. Integrating new methods with these historical images offers unprecedented opportunities for scientific discovery and historical understanding, bridging the past with the future through innovative research techniques.

Monitoring Vegetation Change Using Forest Cover Density Model

Andini Windi Tri, Nurlina Nurlina, Ridwan Ichsan

Ecological Engineering & Environmental Technology

2024

Vol. 25, iss. 9

369--378

The regional ecological environment has become a significant subject of study due to the dynamics of the ecosystem, which is represented by vegetation, under the influence of human activities. The objective of this research is to demonstrate the implementation and effectiveness of the forest canopy density (FCD) model in generating a map that illustrates changes in forest canopy density using multitemporal remote sensing data in Tabunio watershed. The methodology relies on vegetation index, including the normalized difference vegetation index (NDVI), shadow index (SI), and bare soil index (BI), to generate a composite vegetation index (CVI). FCD uses multitemporal remote sensing data from Landsat TM images from 2005 to 2020, which have been utilized to accomplish multisource categorization. The findings indicated that the vegetation coverage of the Tabunio watershed presented a predominant pattern of high coverage in the northeastern and eastern regions, whereas most areas of the western region had low coverage; (2) vegetation cover from 2005 to 2020 is dominated by sparse to very dense vegetation cover classes; (3) changes in vegetation cover over two decades are very significant. The expansion of plantation land in 2005 caused a lot of non-vegetated land, which gradually changed in the following year period along with plant growth. At the end of 2020, the percentage of very dense vegetation became increasingly dominant, which was around 42 percent. The results of the study indicate the three biophysical index (NDVI, SI, and BI) used in this model approach were appropriate for precisely discriminating across all canopy density classes, as seen by the overall producer’s accuracy of 81.3%. FCD model in multitemporal data can helps in the early identification of deforestation or forest degradation activities. Furthermore, the FCD model may have certain constraints, as it requires an understanding of ground conditions to establish threshold values for each class.