Forest structure parameter extraction using SPOT-7 satellite data by object- and pixel-based classification methods Naimeh Rahimizadeh & Sasan Babaie Kafaky & Mahmod Reza Sahebi & Asadollah Mataji Received: 4 August 2019 /Accepted: 3 December 2019 # Springer Nature Switzerland AG 2019 Abstract Using satellite data to extract forest structure mapping parameters assists forest management. In this research, structural parameters including species, densi- ty, canopy, and gaps were extracted from SPOT-7 satel- lite data over Hyrcanian forests (Iran). A detailed ground inventory was initially conducted, over 12 × 1 ha (100 m × 100 m) plots, in which tree coordinates were plotted, using a differential global positioning system (DGPS), along with data on tree species, diameter-at- breast-height and height, as well as canopy dimensions, and canopy gap shapes, sizes, and positions, for each plot. Then, spectral transformations, vegetation indices, and simple spectral ratios were extracted from SPOT-7 data, and a supervised, pixel-based classification meth- od and a support-vector machine algorithm were used to classify and determine tree species types. In addition, canopy tree borders and gaps were classified, using an object-based method, and tree densities per unit area were determined, using the canopy gravity center. Fi- nally, the original ground data was used to perform an accuracy assessment on the extracted information, with the results showing that forest type could be determined with 95% accuracy and a Kappa coefficient of 0.8. Canopy and gap coverage achieved an overall accuracy of 91% (Kappa coefficient: 0.7), and tree densities per hectare were determined, on average, to be 47 trees fewer than reality. In conclusion, we have shown that forest structural parameters could be extracted, with good accuracy, using a combination of pixel- and object-based methods applied to SPOT-7 imaging. Keywords Fagus orientalis . Hyrcanian forest . Object- based classification . SPOT-7 . Support-vector machine algorithm Introduction Current forest conditions and future forestry operations have been determined by identifying forest structure (Koch et al. 2006). In fact, selecting suitable forestry operations and mapping forest stand structures are im- portant methods for preserving biological diversity and the dynamics and sustainability of the forest (Awad 2018; Lu et al. 2017; Molinier et al. 2016; Pratihast et al. 2014; Hudak et al. 2006; Soenen et al. 2009). There are many forest structure mapping parameters, including density, type of species, height, diameter-at- breast-height (DBH), the spatial pattern of trees, cano- pies, gaps, etc., and using traditional methods for their accurate calculation is both costly and time-consuming (Bayat et al. 2019; Piermattei et al. 2019). Measurement has also been mainly limited to particular plots (sample plots) and thus could not provide continuous spatial and Environ Monit Assess (2020) 192:43 https://doi.org/10.1007/s10661-019-8015-x N. Rahimizadeh : S. Babaie Kafaky (*) : A. Mataji Department of environmental and Natural Resources, Science and Research branch - Islamic Azad University, Tehran, Iran e-mail: s_babaie@srbiau.ac.ir e-mail: s.babaie47@yahoo.com M. R. Sahebi Geodesy & Geomatics engineering faculty, K.N.Toosi University of Technology, Tehran, Iran