Set of tools for detecting and analyzing Airborne Laser Scanning-derived Tropical Forest Canopy Gaps. Details were published in Silva and others (2019) <doi:10.1111/2041-210X.13211>.

The Clutter model is a significant forest growth simulation tool. Grounded on individual trees and comprehensively considering factors such as competition among trees and the impact of environmental elements on growth, it can accurately reflect the growth process of forest stands. It can be applied in areas like forest resource management, harvesting planning, and ecological research. With the help of the Clutter model, people can better understand the dynamic changes of forests and provide a scientific basis for rational forest management and protecting the ecological environment. This R package can effectively realize the construction of forest growth and harvest models based on the Clutter model and achieve optimized forest management.References: Farias A, Soares C, Leite H et al(2021)<doi:10.1007/s10342-021-01380-1>. Guera O, Silva J, Ferreira R, et al(2019)<doi:10.1590/2179-8087.038117>.

Assessing forest ecosystem health is an effective way for forest resource management.The national forest health evaluation system at the forest stand level using analytic hierarchy process, has a high application value and practical significance. The package can effectively and easily realize the total assessment process, and help foresters to further assess and management forest resources.

Extensive global and small-area estimation procedures for multiphase
    forest inventories under the design-based Monte-Carlo approach are provided.
    The implementation has been published in the Journal of Statistical Software (<doi:10.18637/jss.v097.i04>) 
    and includes estimators for simple and cluster sampling
    published by Daniel Mandallaz in 2007 (<doi:10.1201/9781584889779>),
    2013 (<doi:10.1139/cjfr-2012-0381>, <doi:10.1139/cjfr-2013-0181>,
    <doi:10.1139/cjfr-2013-0449>, <doi:10.3929/ethz-a-009990020>)
    and 2016 (<doi:10.3929/ethz-a-010579388>). It provides point estimates,
    their external- and design-based variances and confidence intervals, as well as a
    set of functions to analyze and visualize the produced estimates.
    The procedures have also been optimized for the use of remote sensing data
    as auxiliary information, as demonstrated in 2018 by Hill et al. (<doi:10.3390/rs10071052>).