Using historical spy satellite photographs and recent remote sensing data to identify high-conservation-value forests

The potential of historical spy-satellite imagery to support research in ecology and conservation

Remote sensing data are important for assessing ecological change, but their value is often restricted by their limited temporal coverage. Major historical events that affected the environment, such as those associated with colonial history, World War II, or the Green Revolution are not captured by modern remote sensing. In the present article, we highlight the potential of globally available black-and-white satellite photographs to expand ecological and conservation assessments back to the 1960s and to illuminate ecological concepts such as shifting baselines, time-lag responses, and legacy effects. This historical satellite photography can be used to monitor ecosystem extent and structure, species' populations and habitats, and human pressures on the environment. Even though the data were declassified decades ago, their use in ecology and conservation remains limited. But recent advances in image processing and analysis can now unlock this research resource. We encourage the use of this opportunity to address important ecological and conservation questions.

Assessing the potential of remote sensing-based models to predict old-growth forests on large spatiotemporal scales

Old-growth forests provide a broad range of ecosystem services. However, due to poor knowledge of their spatiotemporal distribution, implementing conservation and restoration strategies is challenging. The goal of this study is to compare the predictive ability of socioecological factors and different sources of remotely sensed data that determine the spatiotemporal scales at which forest maturity attributes can be predicted. We evaluated various remotely sensed data that cover a broad range of spatial (from local to global) and temporal (from current to decades) extents, from Airborne Laser Scanning (ALS), aerial multispectral and stereo-imagery, Sentinel-1, Sentinel-2 and Landsat data. Using random forests, remotely sensed data were related to a forest maturity index available in 688 forest plots across four ranges of the French Alps. Each model also includes socioecological predictors related to topography, socioeconomy, pedology and climatology. We found that the different remotely sensed data provide information on the main forest structural characteristics as defined by ALS, except for Landsat, which has a too coarse resolution, and Sentinel-1, which responds differently to vegetation structure. The predictions were quite similar considering aerial remotely sensed data, on the one hand, and satellite remotely sensed data, on the other hand. Socioecological variables are the most important predictors compared to the remote sensing metrics. In conclusion, our results indicate that a wide range of remotely sensed data can be used to study old-growth forests beyond the use of ALS and despite different abilities to predict forest structure. Accounting for socioecological predictors is indispensable to avoid a significant loss of predictive accuracy. Remotely sensed data can allow for predictions to be made at different spatiotemporal resolutions and extents. This study paves the way to large-scale monitoring of forest maturity, as well as for retrospective analyses which will show to what extent predicted maturity change at different dates.

Networking the forest infrastructure towards near real-time monitoring - A white paper

Forests account for nearly 90 % of the world's terrestrial biomass in the form of carbon and they support 80 % of the global biodiversity. To understand the underlying forest dynamics, we need a long-term but also relatively high-frequency, networked monitoring system, as traditionally used in meteorology or hydrology. While there are numerous existing forest monitoring sites, particularly in temperate regions, the resulting data streams are rarely connected and do not provide information promptly, which hampers real-time assessments of forest responses to extreme climate events. The technology to build a better global forest monitoring network now exists. This white paper addresses the key structural components needed to achieve a novel meta-network. We propose to complement - rather than replace or unify - the existing heterogeneous infrastructure with standardized, quality-assured linking methods and interacting data processing centers to create an integrated forest monitoring network. These automated (research topic-dependent) linking methods in atmosphere, biosphere, and pedosphere play a key role in scaling site-specific results and processing them in a timely manner. To ensure broad participation from existing monitoring sites and to establish new sites, these linking methods must be as informative, reliable, affordable, and maintainable as possible, and should be supplemented by near real-time remote sensing data. The proposed novel meta-network will enable the detection of emergent patterns that would not be visible from isolated analyses of individual sites. In addition, the near real-time availability of data will facilitate predictions of current forest conditions (nowcasts), which are urgently needed for research and decision making in the face of rapid climate change. We call for international and interdisciplinary efforts in this direction.

Forest Habitat Fragmentation in Mountain Protected Areas Using Historical Corona KH-9 and Sentinel-2 Satellite Imagery

Forest habitat fragmentation is one of the global environmental issues of concern as a result of forest management practices and socioeconomic drivers. In this context, a constant evaluation of natural habitat conditions still remains a challenge in order to achieve a general image of the environmental state of a protected area for proper sustainable management. The purpose of our study was to evaluate the evolution of forest habitat in the last 40 years, focusing on Bucegi Natural Park, one of the most frequented protected areas in Romania, as relevant for highly human-impacted areas. Our approach integrates a historical panchromatic Corona KH-9 image from 1977 and present-day Sentinel-2 multispectral data from 2020 in order to calculate a series of spatial metrics that reveal changes in the pattern of the forest habitat and illustrate forest habitat fragmentation density. Object-based oriented analysis with supervised maximum likelihood classification was employed for the production of forest cover fragmentation maps. Ten landscape metrics were adapted to the analysis context, from patch statistics to proximity index. The results show a general growth of the forest surface but also an increase in habitat fragmentation in areas where tourism was developed. Fragmentation indices explain that larger and compact patches feature natural park protected forests after the spruce–fir secondary canopies were grown during the last 4–5 decades. The number of patches decreased to half, and their average size is double that of before. The method can be of extensive use for environmental monitoring in protected areas management and for understanding the environmental history connected to present-day problems that are to be fixed under rising human pressure.