Information such as forest canopy height can be useful in assessing the health of a forest, but current measurement methods are not always feasible for large geographic regions or adaptable to different forest types. Monitoring from space could be a solution.
Forests function as more than a place for a peaceful retreat: trees take in and absorb carbon dioxide (a major greenhouse gas), a process also known as carbon sequestration, and are essential parts of water regulation, habitat provision and support most of the world. terrestrial biodiversity. Quantifying forest structure parameters such as canopy height, canopy cover, and tree density are essential for understanding forest ecosystem functions. Measuring these, on the other hand, can be tricky. Rather than relying on airborne laser scanning (ALS) measurements, which can be limited and expensive, researchers are looking for a more accessible way to obtain measurements of forests that are just as accurate and as accurate as the current widely used method.
Lidar (or LiDAR: light detection and ranging) is an active remote sensing technology capable of capturing three-dimensional information of the earth’s surface. Remote sensing itself refers to the scanning and monitoring of physical features of the earth using satellites or high-altitude aircraft. Airborne lidar is one of the most accurate tools for measuring canopy height, but it is poor when it comes to spatial coverage and affordability. A suitable replacement would have similar accuracy to airborne lidar and more flexibility to estimate canopy height over different forest types, elevation, topography, and canopy cover. Proposed replacement: spaceborne lidar. The study uses a satellite in space, ICESat-2, which has much greater potential for estimating canopy height on a larger scale. This reduces the need for less adaptable and more expensive technologies by allowing greater access to forest structure information.
The results were published in the Journal of Remote Sensing on July 5.
ICESat-2 uses an advanced laser to map the earth’s surface including measures of altitude and vegetation height. Canopy heights measured by ICESat-2, operated using the strong beam at night, are more consistent with airborne lidar data. ICESat-2 appears to have a high degree of accuracy when analyzing evergreen forests with dense canopy cover.
“The ICESat-2 satellite has proven to be a reliable and effective tool for measuring canopy height on a global scale, but a careful selection and calibration based on ICESat-2 data is necessary for canopy height estimation on a large scale,” said Nitant Rai. , a former graduate student at Mississippi State University and lead author of the study.
By continuing to develop ICESat-2 and combining it with other fine-resolution remote sensing methods, the researchers hope to achieve a wall-to-wall assessment of the details of canopy cover and how the forest is structured and monitor how this structure may change. over time. Already, ICESat-2 looks promising for monitoring forest recovery and detecting changes that could lead to forest health problems down the road.
“This study also demonstrates the importance of integrating different data sources for forest structure monitoring and highlights the applicability of ICESat-2 in forest understanding
structure on a global scale,” said Qin Ma, a professor from Nanjing Normal University and corresponding author of the study.
Information of this type can be integral to monitoring forests, which can help detect changes in the amount of carbon they store among other things, having a wider range of access across the globe. With increased monitoring, threats to forests or areas of deforestation can be addressed immediately. Ultimately, improved forest surveillance can help preserve ecosystems, which are integral to the safety and longevity of its inhabitants and surrounding areas.
Nitant Rai, Krishna Poudel, and Austin Himes of the Department of Forestry at Mississippi State University; Qin Ma of the School of Geography, the Key Laboratory of Virtual Geographic Environment at Nanjing Normal University and the Jiangsu Center for Collaborative Innovation in the Development and Application of Geographic Information Resources; and Qingmin Meng of the Department of Geosciences at Mississippi State University contributed to this research.
The McIntire-Stennis Project, the National Key Research and Development Program of China, the National Natural Science Foundation of China, and Nanjing Normal University made this research possible.
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