Due to urbanization and population growth, the degradation of natural forests and associated biodiversity are now widely recognized as a global environmental concern. Hence, there is an urgent need for rapid assessment and monitoring of biodiversity on priority using state-of-art tools and technologies. The main purpose of this research article is to develop and implement a new methodological approach to characterize biological diversity using spatial model developed during the study viz. Spatial Biodiversity Model (SBM). The developed model is scale, resolution and location independent solution for spatial biodiversity richness modelling. The platform-independent computation model is based on parallel computation. The biodiversity model based on open-source software has been implemented on R statistical computing platform. It provides information on high disturbance and high biological richness areas through different landscape indices and site specific information (e.g. forest fragmentation (FR), disturbance index (DI) etc.). The model has been developed based on the case study of Indian landscape; however it can be implemented in any part of the world. As a case study, SBM has been tested for Uttarakhand state in India. Inputs for landscape ecology are derived through multi-criteria decision making (MCDM) techniques in an interactive command line environment. MCDM with sensitivity analysis in spatial domain has been carried out to illustrate the model stability and robustness. Furthermore, spatial regression analysis has been made for the validation of the output.

由于城市化和人口增长，天然林的退化和相关生物多样性现在已被广泛认为是全球环境问题。因此，迫切需要使用最先进的工具和技术对生物多样性进行快速评估和监测。该研究文章的主要目的是制定和实施新的方式方法用在研究过程中发展空间模型来描述生物多样性即。空间生物多样性模型（SBM）。所开发的模型是空间生物多样性丰富度建模的规模，分辨率和位置无关的解决方案。平台无关的计算模型基于并行计算。在R统计计算平台上实现了基于开源软件的生物多样性模型。它通过不同的景观指数和特定地点的信息（例如森林破碎度（FR），干扰指数（DI）等）提供有关高干扰度和高生物丰富度地区的信息。该模型是基于印度景观的案例研究开发的；但是它可以在世界任何地方实施。作为案例研究，SBM已在印度的北阿坎德邦进行了测试。景观生态学的输入是通过交互式命令行环境中的多标准决策（MCDM）技术得出的。MCDM在空间域中进行了灵敏度分析，以说明模型的稳定性和鲁棒性。此外，已经进行了空间回归分析以验证输出。