@article{LatifiHeurich2019,
  author    = {Latifi, Hooman and Heurich, Marco},
  title     = {Multi-scale remote sensing-assisted forest inventory: a glimpse of the state-of-the-art and future prospects},
  series = {Remote Sensing},
  volume    = {11},
  journal   = {Remote Sensing},
  number    = {11},
  issn      = {2072-4292},
  doi       = {10.3390/rs11111260},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197358},
  year      = {2019},
  abstract  = {Advances in remote inventory and analysis of forest resources during the last decade have reached a level to be now considered as a crucial complement, if not a surrogate, to the long-existing field-based methods. This is mostly reflected in not only the use of multiple-band new active and passive remote sensing data for forest inventory, but also in the methodic and algorithmic developments and/or adoptions that aim at maximizing the predictive or calibration performances, thereby minimizing both random and systematic errors, in particular for multi-scale spatial domains. With this in mind, this editorial note wraps up the recently-published Remote Sensing special issue "Remote Sensing-Based Forest Inventories from Landscape to Global Scale", which hosted a set of state-of-the-art experiments on remotely sensed inventory of forest resources conducted by a number of prominent researchers worldwide.},
  language  = {en}
}
@article{AtaeeMaghsoudiLatifietal.2019,
  author    = {Ataee, Mohammad Sadegh and Maghsoudi, Yasser and Latifi, Hooman and Fadaie, Farhad},
  title     = {Improving estimation accuracy of growing stock by multi-frequency SAR and multi-spectral data over Iran's heterogeneously-structured broadleaf Hyrcanian forests},
  series = {Forests},
  volume    = {10},
  journal   = {Forests},
  number    = {8},
  issn      = {1999-4907},
  doi       = {10.3390/f10080641},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197212},
  year      = {2019},
  abstract  = {Via providing various ecosystem services, the old-growth Hyrcanian forests play a crucial role in the environment and anthropogenic aspects of Iran and beyond. The amount of growing stock volume (GSV) is a forest biophysical parameter with great importance in issues like economy, environmental protection, and adaptation to climate change. Thus, accurate and unbiased estimation of GSV is also crucial to be pursued across the Hyrcanian. Our goal was to investigate the potential of ALOS-2 and Sentinel-1's polarimetric features in combination with Sentinel-2 multi-spectral features for the GSV estimation in a portion of heterogeneously-structured and mountainous Hyrcanian forests. We used five different kernels by the support vector regression (nu-SVR) for the GSV estimation. Because each kernel differently models the parameters, we separately selected features for each kernel by a binary genetic algorithm (GA). We simultaneously optimized R\(^2\) and RMSE in a suggested GA fitness function. We calculated R\(^2\), RMSE to evaluate the models. We additionally calculated the standard deviation of validation metrics to estimate the model's stability. Also for models over-fitting or under-fitting analysis, we used mean difference (MD) index. The results suggested the use of polynomial kernel as the final model. Despite multiple methodical challenges raised from the composition and structure of the study site, we conclude that the combined use of polarimetric features (both dual and full) with spectral bands and indices can improve the GSV estimation over mixed broadleaf forests. This was partially supported by the use of proposed evaluation criterion within the GA, which helped to avoid the curse of dimensionality for the applied SVR and lowest over estimation or under estimation.},
  language  = {en}
}
@article{KhareLatifiRossietal.2019,
  author    = {Khare, Siddhartha and Latifi, Hooman and Rossi, Sergio and Ghosh, Sanjay Kumar},
  title     = {Fractional cover mapping of invasive plant species by combining very high-resolution stereo and multi-sensor multispectral imageries},
  series = {Forests},
  volume    = {10},
  journal   = {Forests},
  number    = {7},
  issn      = {1999-4907},
  doi       = {10.3390/f10070540},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197250},
  year      = {2019},
  abstract  = {Invasive plant species are major threats to biodiversity. They can be identified and monitored by means of high spatial resolution remote sensing imagery. This study aimed to test the potential of multiple very high-resolution (VHR) optical multispectral and stereo imageries (VHRSI) at spatial resolutions of 1.5 and 5 m to quantify the presence of the invasive lantana (Lantana camara L.) and predict its distribution at large spatial scale using medium-resolution fractional cover analysis. We created initial training data for fractional cover analysis by classifying smaller extent VHR data (SPOT-6 and RapidEye) along with three dimensional (3D) VHRSI derived digital surface model (DSM) datasets. We modelled the statistical relationship between fractional cover and spectral reflectance for a VHR subset of the study area located in the Himalayan region of India, and finally predicted the fractional cover of lantana based on the spectral reflectance of Landsat-8 imagery of a larger spatial extent. We classified SPOT-6 and RapidEye data and used the outputs as training data to create continuous field layers of Landsat-8 imagery. The area outside the overlapping region was predicted by fractional cover analysis due to the larger extent of Landsat-8 imagery compared with VHR datasets. Results showed clear discrimination of understory lantana from upperstory vegetation with 87.38\% (for SPOT-6), and 85.27\% (for RapidEye) overall accuracy due to the presence of additional VHRSI derived DSM information. Independent validation for lantana fractional cover estimated root-mean-square errors (RMSE) of 11.8\% (for RapidEye) and 7.22\% (for SPOT-6), and R\(^2\) values of 0.85 and 0.92 for RapidEye (5 m) and SPOT-6 (1.5 m), respectively. Results suggested an increase in predictive accuracy of lantana within forest areas along with increase in the spatial resolution for the same Landsat-8 imagery. The variance explained at 1.5 m spatial resolution to predict lantana was 64.37\%, whereas it decreased by up to 37.96\% in the case of 5 m spatial resolution data. This study revealed the high potential of combining small extent VHR and VHRSI- derived 3D optical data with larger extent, freely available satellite data for identification and mapping of invasive species in mountainous forests and remote regions.},
  language  = {en}
}
@article{LatifiValbuena2019,
  author    = {Latifi, Hooman and Valbuena, Ruben},
  title     = {Current trends in forest ecological applications of three-dimensional remote sensing: Transition from experimental to operational solutions?},
  series = {Forests},
  volume    = {10},
  journal   = {Forests},
  number    = {10},
  issn      = {1999-4907},
  doi       = {10.3390/f10100891},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193282},
  year      = {2019},
  abstract  = {The alarming increase in the magnitude and spatiotemporal patterns of changes in composition, structure and function of forest ecosystems during recent years calls for enhanced cross-border mitigation and adaption measures, which strongly entail intensified research to understand the underlying processes in the ecosystems as well as their dynamics. Remote sensing data and methods are nowadays the main complementary sources of synoptic, up-to-date and objective information to support field observations in forest ecology. In particular, analysis of three-dimensional (3D) remote sensing data is regarded as an appropriate complement, since they are hypothesized to resemble the 3D character of most forest attributes. Following their use in various small-scale forest structural analyses over the past two decades, these sources of data are now on their way to be integrated in novel applications in fields like citizen science, environmental impact assessment, forest fire analysis, and biodiversity assessment in remote areas. These and a number of other novel applications provide valuable material for the Forests special issue "3D Remote Sensing Applications in Forest Ecology: Composition, Structure and Function", which shows the promising future of these technologies and improves our understanding of the potentials and challenges of 3D remote sensing in practical forest ecology worldwide.},
  language  = {en}
}