Historical forest biomass dynamics modelled with Landsat spectral trajectories

Acknowledgements National Forest Inventory data are available online, provided by Ministerio de Agricultura, Alimentación y Medio Ambiente (España). Landsat images are available online, provided by the USGS.Peer reviewedPostprin

Analysis of Site-dependent Pinus halepensis Mill. Defoliation Caused by ‘Candidatus Phytoplasma pini’ through Shape Selection in Landsat Time Series

High levels of ‘Candidatus Phytoplasma pini’ have produced extensive forest mortality on Pinus halepensis Mill forests in eastern Spain. This has led to the widespread levels of forest mortality. We used archival Landsat imagery and shapes algorithm implemented in the Google Earth Engine to explore the potential of the LandTrendr algorithm and its outputs, together with field observations, to analyze and predict the health status in P. halepensis stands affected by ‘Candidatus Phytoplasma pini’ in Andalusia (south-eastern Spain). We found that the Landsat time series algorithm (LandTrendr) has captured both long- and short-duration trends and changes in spectral reflectance related to phytoplasma disturbance in the Aleppo pine forest stands investigated. The normalized burn ratio (NBR) trends were positively associated with environmental variables: Annual precipitation, mean temperature, soil depth, percent base saturation and aspect. Environmental variables were tested for their contributions to the mapping of changes in Aleppo pine cover in the study area, as an empirical modeling approach to disturbance mapping in forests of south-eastern Spain. The methodology outlined in this paper has produced valuable results that indicate new possibilities for the use in forest management of remote-sensing technologies based on spectral trajectories associated with pest-diseases defoliation. Given the likely increase in pest risks in the forests of southern Europe, accurate assessment and map of pest outbreaks on forests will become increasingly important, both for research and for practical applications in forest management

The use of remotely sensed data and polish NFI plots for prediction of growing stock volume using different predictive methods

Forest growing stock volume (GSV) is an important parameter in the context of forest resource management. National Forest Inventories (NFIs) are routinely used to estimate forest parameters, including GSV, for national or international reporting. Remotely sensed data are increasingly used as a source of auxiliary information for NFI data to improve the spatial precision of forest parameter estimates. In this study, we combine data from the NFI in Poland with satellite images of Landsat 7 and 3D point clouds collected with airborne laser scanning (ALS) technology to develop predictive models of GSV. We applied an area-based approach using 13,323 sample plots measured within the second cycle of the NFI in Poland (2010–2014) with poor positional accuracy from several to 15 m. Four different predictive approaches were evaluated: multiple linear regression, k-Nearest Neighbours, Random Forest and Deep Learning fully connected neural network. For each of these predictive methods, three sets of predictors were tested: ALS-derived, Landsat-derived and a combination of both. The developed models were validated at the stand level using field measurements from 360 reference forest stands. The best accuracy (RMSE% = 24.2%) and lowest systematic error (bias% = −2.2%) were obtained with a deep learning approach when both ALS- and Landsat-derived predictors were used. However, the differences between the evaluated predictive approaches were marginal when using the same set of predictor variables. Only a slight increase in model performance was observed when adding the Landsat-derived predictors to the ALS-derived ones. The obtained results showed that GSV can be predicted at the stand level with relatively low bias and reasonable accuracy for coniferous species, even using field sample plots with poor positional accuracy for model development. Our findings are especially important in the context of GSV prediction in areas where NFI data are available but the collection of accurate positions of field plots is not possible or justified because of economic reasons

Mapping and Estimating Forest Stand Volume using Machine Learning Methods and Multi-Spectral Sentinel 2 Data

Sustainable forest management necessitates the mapping and estimation of forest stand attributes such as density, volume, basal area, and aboveground biomass. This study was conducted to explore the potential of geographic information systems (GIS), remote sensing, machine learning, and field inventories to estimate the forest stand volume of natural and plantation forests within watersheds in the Abra River Basin. The common machine learning regression techniques, which are random forest (RF), k-nearest neighbors (KNN), and support vector machines (SVM), were used to model and predict forest stand volume. The validation of the three machine learning methods showed that the best model to estimate and map forest stand volume is the RF algorithm (R2 = 0.42, RMSE = 0.40 m3/plot, MAE = 0.31 m3/plot). Topographic variables such as the Digital Elevation Model (DEM) and the spectral band Near Infrared (NIR) were the most important variables in predicting forest stand volume. The estimated forest stand volume using the RF model ranged from 33 to 115 m3/ha, with a mean of 59 m3/ha. The results of this study revealed that forest volume can be measured using freely available satellite data and machine learning techniques.&nbsp

Land Use/Land Cover Mapping Using Multitemporal Sentinel-2 Imagery and Four Classification Methods-A Case Study from Dak Nong, Vietnam

Information on land use and land cover (LULC) including forest cover is important for the development of strategies for land planning and management. Satellite remotely sensed data of varying resolutions have been an unmatched source of such information that can be used to produce estimates with a greater degree of confidence than traditional inventory estimates. However, use of these data has always been a challenge in tropical regions owing to the complexity of the biophysical environment, clouds, and haze, and atmospheric moisture content, all of which impede accurate LULC classification. We tested a parametric classifier (logistic regression) and three non-parametric machine learning classifiers (improved k-nearest neighbors, random forests, and support vector machine) for classification of multi-temporal Sentinel 2 satellite imagery into LULC categories in Dak Nong province, Vietnam. A total of 446 images, 235 from the year 2017 and 211 from the year 2018, were pre-processed to gain high quality images for mapping LULC in the 6516 km(2) study area. The Sentinel 2 images were tested and classified separately for four temporal periods: (i) dry season, (ii) rainy season, (iii) the entirety of the year 2017, and (iv) the combination of dry and rainy seasons. Eleven different LULC classes were discriminated of which five were forest classes. For each combination of temporal image set and classifier, a confusion matrix was constructed using independent reference data and pixel classifications, and the area on the ground of each class was estimated. For overall temporal periods and classifiers, overall accuracy ranged from 63.9% to 80.3%, and the Kappa coefficient ranged from 0.611 to 0.813. Area estimates for individual classes ranged from 70 km(2) (1% of the study area) to 2200 km(2) (34% of the study area) with greater uncertainties for smaller classes.Peer reviewe

Assessment of Different Remote Sensing Data for Forest Structural Attributes Estimation in the Hyrcanian forests

Aim of study: The objective of the study was the comparative assessment of various spatial resolutions of optical satellite imagery including Landsat-TM, ASTER, and Quickbird data to estimate the forest structure attributes of Hyrcanian forests, Golestan province, northernIran.Material and methods: The 112 square plots with area of0.09 ha were measured using a random cluster sampling method and then stand volume, basal area, and tree stem density were computed using measured data. After geometric and atmospheric corrections of images, the spectral attributes from original and different synthetic bands were extracted for modelling. The statistical modelling was performed using CART algorithm. Performance assessment of models was examined using the unused validation plots by RMSE and bias measures.Main Results: The results showed that model of Quickbird data for stand volume, basal area, and tree stem density had a better performance compared to ASTER and TM data. However, estimations by ASTER and TM imagery had slightly similar results for all three parameters.Research highlights: This study exposed that the high-resolution satellite data are more useful for forest structure attributes estimation in the Hyrcanian broadleaves forests compared with medium resolution images without consideration of images costs. However, regarding to be free of the most medium resolution data such as ASTER and TM,ETM+ or OLI images, these data can be used with slightly similar results.  Keywords: Forest structure attributes; quickbird; ASTER; TM; CART algorithm; Hyrcanian forests

Operationalization of Remote Sensing Solutions for Sustainable Forest Management

The great potential of remote sensing technologies for operational use in sustainable forest management is addressed in this book, which is the reprint of papers published in the Remote Sensing Special Issue “Operationalization of Remote Sensing Solutions for Sustainable Forest Management”. The studies come from three continents and cover multiple remote sensing systems (including terrestrial mobile laser scanning, unmanned aerial vehicles, airborne laser scanning, and satellite data acquisition) and a diversity of data processing algorithms, with a focus on machine learning approaches. The focus of the studies ranges from identification and characterization of individual trees to deriving national- or even continental-level forest attributes and maps. There are studies carefully describing exercises on the case study level, and there are also studies introducing new methodologies for transdisciplinary remote sensing applications. Even though most of the authors look forward to continuing their research, nearly all studies introduced are ready for operational use or have already been implemented in practical forestry

Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.

With the demise of the Soviet Union and the transition to a market economy starting in the 1990s, Mongolia has been experiencing dramatic changes resulting in social and economic disparities and an increasing strain on its natural resources. The situation is exacerbated by a changing climate, the erosion of forestry related administrative structures, and a lack of law enforcement activities. Mongolia’s forests have been afflicted with a dramatic increase in degradation due to human and natural impacts such as overexploitation and wildfire occurrences. In addition, forest management practices are far from being sustainable. In order to provide useful information on how to viably and effectively utilise the forest resources in the future, the gathering and analysis of forest related data is pivotal. Although a National Forest Inventory was conducted in 2016, very little reliable and scientifically substantiated information exists related to a regional or even local level. This lack of detailed information warranted a study performed in the Thunkel taiga area in 2017 in cooperation with the GIZ. In this context, we hypothesise that (i) tree species and composition can be identified utilising the aerial imagery, (ii) tree height can be extracted from the resulting canopy height model with accuracies commensurate with field survey measurements, and (iii) high-resolution satellite imagery is suitable for the extraction of tree species, the number of trees, and the upscaling of timber volume and basal area based on the spectral properties. The outcomes of this study illustrate quite clearly the potential of employing UAV imagery for tree height extraction (R2 of 0.9) as well as for species and crown diameter determination. However, in a few instances, the visual interpretation of the aerial photographs were determined to be superior to the computer-aided automatic extraction of forest attributes. In addition, imagery from various satellite sensors (e.g. Sentinel-2, RapidEye, WorldView-2) proved to be excellently suited for the delineation of burned areas and the assessment of tree vigour. Furthermore, recently developed sophisticated classifying approaches such as Support Vector Machines and Random Forest appear to be tailored for tree species discrimination (Overall Accuracy of 89%). Object-based classification approaches convey the impression to be highly suitable for very high-resolution imagery, however, at medium scale, pixel-based classifiers outperformed the former. It is also suggested that high radiometric resolution bears the potential to easily compensate for the lack of spatial detectability in the imagery. Quite surprising was the occurrence of dark taiga species in the riparian areas being beyond their natural habitat range. The presented results matrix and the interpretation key have been devised as a decision tool and/or a vademecum for practitioners. In consideration of future projects and to facilitate the improvement of the forest inventory database, the establishment of permanent sampling plots in the Mongolian taigas is strongly advised.2021-06-0