Regional Forest Volume Estimation by Expanding LiDAR Samples Using Multi-Sensor Satellite Data

Accurate information regarding forest volume plays an important role in estimating afforestation, timber harvesting, and forest ecological services. Traditionally, operations on forest growing stock volume using field measurements are labor-intensive and time-consuming. Recently, remote sensing technology has emerged as a time-cost efficient method for forest inventory. In the present study, we have adopted three procedures, including samples expanding, feature selection, and results generation and evaluation. Extrapolating the samples from Light Detection and Ranging (LiDAR) scanning is the most important step in satisfying the requirement of sample size for nonparametric methods operation and result in accuracy improvement. Besides, mean decrease Gini (MDG) methodology embedded into Random Forest (RF) algorithm served as a selector for feature measure; afterwards, RF and K-Nearest Neighbor (KNN) were adopted in subsequent forest volume prediction. The results show that the retrieval of Forest volume in the entire area was in the range of 50–360 m3/ha, and the results from the two models show a better consistency while using the sample combination extrapolated by the optimal threshold value (2 × 10−4), leading to the best performances of RF (R2 = 0.618, root mean square error, RMSE = 43.641 m3/ha, mean absolute error, MAE = 33.016 m3/ha), followed by KNN (R2 = 0.617, RMSE = 43.693 m3/ha, MAE = 32.534 m3/ha). The detailed analysis that is discussed in the present paper clearly shows that expanding image-derived LiDAR samples helps in refining the prediction of regional forest volume while using satellite data and nonparametric models

The use of remotely sensed data for forest biomass monitoring : a case of forest sites in north-eastern Armenia

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesIn recent years there has been an increasing interest in the use of synthetic aperture radar (SAR) data and geospatial technologies for environmental monitoring․ Particularly, forest biomass evaluation was of high importance, as forests have a crucial role in global carbon emission. Within this study we evaluate the use of Sentinel 1 C-band multitemporal SAR data with combination of Alos Palsar L-band SAR and Sentinel 2 multispectral remote sensing (RS) data for mapping forest aboveground biomass (AGB) of dry subtropical forests in mountainous areas. Field observation from National Forest Inventory was used as a ground truth data. As the SAR data suffers greatly by the complex topography, a simple approach of aspect and slope information as forestry ancillary data was implemented directly in the regression model for the first time to mitigate the topography effect on radar backscattering value․ Dense time-series analysis allowed us to overcome the SAR saturation by the forest phenology and select the optimal C-band scene. Image texture measures of SAR data has been strongly related to the biomass distribution and has robustly contributed to the prediction․ Multilinear Stepwise Regression allowed to select and evaluate the most relevant variables for AGB. The prediction model combining RS with ancillary data explained the 62 % of variance with root-mean-square error of 56.6 t ha¯¹. The study also reveals that C-band SAR data on forest biomass prediction is limited due to their short wavelength. Further, the mountainous condition is a major constraint for AGB estimation. Additionally, this research demonstrates a positive outcome in forest AGB prediction with freely accessible RS data

Remote sensing technology applications in forestry and REDD+

Advances in close-range and remote sensing technologies are driving innovations in forest resource assessments and monitoring on varying scales. Data acquired with airborne and spaceborne platforms provide high(er) spatial resolution, more frequent coverage, and more spectral information. Recent developments in ground-based sensors have advanced 3D measurements, low-cost permanent systems, and community-based monitoring of forests. The UNFCCC REDD+ mechanism has advanced the remote sensing community and the development of forest geospatial products that can be used by countries for the international reporting and national forest monitoring. However, an urgent need remains to better understand the options and limitations of remote and close-range sensing techniques in the field of forest degradation and forest change. Therefore, we invite scientists working on remote sensing technologies, close-range sensing, and field data to contribute to this Special Issue. Topics of interest include: (1) novel remote sensing applications that can meet the needs of forest resource information and REDD+ MRV, (2) case studies of applying remote sensing data for REDD+ MRV, (3) timeseries algorithms and methodologies for forest resource assessment on different spatial scales varying from the tree to the national level, and (4) novel close-range sensing applications that can support sustainable forestry and REDD+ MRV. We particularly welcome submissions on data fusion

Modelling Stand Variables of Beech Coppice Forest Using Spectral Sentinel-2A Data and the Machine Learning Approach

Background and Purpose: Coppice forests have a particular socio-economic and ecological role in forestry and environmental management. Their production sustainability and spatial stability become imperative for forestry sector as well as for local and global communities. Recently, integrated forest inventory and remotely sensed data analysed with non-parametrical statistical methods have enabled more detailed insight into forest structural characteristics. The aim of this research was to estimate forest attributes of beech coppice forest stands in the Sarajevo Canton through the integration of inventory and Sentinel S2A satellite data using machine learning methods. Materials and Methods: Basal area, mean stand diameter, growing stock and total volume data were determined from the forest inventory designed for represented stands of coppice forests. Spectral data were collected from bands of Sentinel S2A satellite image, vegetation indices (difference, normalized difference and ratio vegetation index) and biophysical variables (fraction of absorbed photosynthetically active radiation, leaf area index, fraction of vegetation cover, chlorophyll content in the leaf and canopy water content). Machine learning rule-based M5 model tree (M5P) and random forest (RF) methods were used for forest attribute estimation. Predictor subset selection was based on wrapping assuming M5P and RF learning schemes. Models were developed on training data subsets (402 sample plots) and evaluations were performed on validation data subsets (207 sample plots). Performance of the models was evaluated by the percentage of the root mean squared error over the mean value (rRMSE) and the square of the correlation coefficient between the observed and estimated stand variables. Results and Conclusions: Predictor subset selection resulted in a varied number of predictors for forest attributes and methods with their larger contribution in RF (between 8 and 11). Spectral biophysical variables dominated in subsets. The RF resulted in smaller errors for training sets for all attributes than M5P, while both methods delivered very high errors for validation sets (rRMSE above 50%). The lowest rRMSE of 50% was obtained for stand basal area. The observed variability explained by the M5P and RF models in training subsets was about 30% and 95% respectively, but those values were lower in test subsets (below 12%) but still significant. Differences of the sample and modelled forest attribute means were not significant, while modelled variability for all forest attributes was significantly lower (p<0.01). It seems that additional information is needed to increase prediction accuracy, so stand information (management classes, site class, soil type, canopy closure and others), new sampling strategy and new spectral products could be integrated and examined in further more complex modelling of forest attributes

Mapping Spatial Variations of Structure and Function Parameters for Forest Condition Assessment of the Changbai Mountain National Nature Reserve

Forest condition is the baseline information for ecological evaluation and management. The National Forest Inventory of China contains structural parameters, such as canopy closure, stand density and forest age, and functional parameters, such as stand volume and soil fertility. Conventionally forest conditions are assessed through parameters collected from field observations, which could be costly and spatially limited. It is crucial to develop modeling approaches in mapping forest assessment parameters from satellite remote sensing. This study mapped structure and function parameters for forest condition assessment in the Changbai Mountain National Nature Reserve (CMNNR). The mapping algorithms, including statistical regression, random forests, and random forest kriging, were employed with predictors from Advanced Land Observing Satellite (ALOS)-2, Sentinel-1, Sentinel-2 satellite sensors, digital surface model of ALOS, and 1803 field sampled forest plots. Combined predicted parameters and weights from principal component analysis, forest conditions were assessed. The models explained spatial dynamics and characteristics of forest parameters based on an independent validation with all r values above 0.75. The root mean square error (RMSE) values of canopy closure, stand density, stand volume, forest age and soil fertility were 4.6%, 33.8%, 29.4%, 20.5%, and 14.3%, respectively. The mean assessment score suggested that forest conditions in the CMNNR are mainly resulted from spatial variations of function parameters such as stand volume and soil fertility. This study provides a methodology on forest condition assessment at regional scales, as well as the up-to-date information for the forest ecosystem in the CMNNR

Mapping Natural Forest Remnants with Multi-Source and Multi-Temporal Remote Sensing Data for More Informed Management of Global Biodiversity Hotspots

Global terrestrial biodiversity hotspots (GBH) represent areas featuring exceptional concentrations of endemism and habitat loss in the world. Unfortunately, geospatial data of natural habitats of the GBHs are often outdated, imprecise, and coarse, and need updating for improved management and protection actions. Recent developments in satellite image availability, combined with enhanced machine learning algorithms and computing capacity, enable cost-efficient updating of geospatial information of these already severely fragmented habitats. This study aimed to develop a more accurate method for mapping closed canopy evergreen natural forest (CCEF) of the Eastern Arc Mountains (EAM) ecoregion in Tanzania and Kenya, and to update the knowledge on its spatial extent, level of fragmentation, and conservation status. We tested 1023 model possibilities stemming from a combination of Sentinel-1 (S1) and Sentinel-2 (S2) satellite imagery, spatial texture of S1 and S2, seasonality derived from Landsat-8 time series, and topographic information, using random forest modelling approach. We compared the best CCEF model with existing spatial forest products from the EAM through independent accuracy assessment. Finally, the CCEF model was used to estimate the fragmentation and conservation coverage of the EAM. The CCEF model has moderate accuracy measured in True Skill Statistic (0.57), and it clearly outperforms other similar products from the region. Based on this model, there are about 296,000 ha of Eastern Arc Forests (EAF) left. Furthermore, acknowledging small forest fragments (1-10 ha) implies that the EAFs are more fragmented than previously considered. Currently, the official protection of EAFs is disproportionally targeting well-studied mountain blocks, while less known areas and small fragments are underrepresented in the protected area network. Thus, the generated CCEF model should be used to design updates and more informed and detailed conservation allocation plans to balance this situation. The results highlight that spatial texture of S2, seasonality, and topography are the most important variables describing the EAFs, while spatial texture of S1 increases the model performance slightly. All in all, our work demonstrates that recent developments in Earth observation allows significant enhancements in mapping, which should be utilized in areas with outstanding biodiversity values for better forest and conservation planning.Peer reviewe

Assessing and mapping of carbon in biomass and soil of mangrove forest and competing land uses in the Philippines

Mangrove forests provide many ecosystem goods and services, and are important carbon (C) sinks in the tropics. Yet, land use conversions in mangroves still continue, especially in Southeast Asia. Carbon stocks in biomass and soil as well as the soil emissions of greenhouse gases (GHG) are important parameters to quantify, monitor and map in mangrove area, and are vital inputs for assessing the impact of mangrove conversion on C budget. This study was conducted in a section of tropical intertidal zone in Honda Bay, Philippines, with the following objectives: 1) evaluate the biomass C stocks in mangrove forests and land uses that replaced mangroves, 2) examine the potential of Sentinel satellite radar and multispectral imagery for mapping the aboveground biomass in mangrove area, 3) investigate the soil C stocks and the potential of GIS-based Ordinary Kriging for mapping the C stocks in mangrove soil, and 4) assess the soil fluxes of greenhouse gases and the potential of Ordinary Kriging for mapping the soil GHG fluxes. I used intensive field assessments, combined with laboratory analysis, remote sensing and GIS methods, to achieve the above objectives. To address the first objective, the biomass C stocks of the study land uses were quantified. Their relationships with selected canopy variables were also evaluated. Results reveal that for mangrove forests, the mean biomass was 22.4 to 178.1 Mg ha-1, which store 10 to 80 MgC ha-1 or 47.9 MgC ha-1, on average. Leaf Area Index significantly correlated with mangrove biomass C. In contrast, the biomass C stock of the land uses that replaced mangroves was, on average, 97% less than that in mangrove forests, ranging from zero in salt pond and cleared mangrove, 0.04 Mg C ha-1 in abandoned aquaculture ponds, to 5.7 Mg C ha-1 in the coconut plantation. C losses in biomass from conversion were estimated at 46.5 Mg C ha-1, on average. For the second objective, the potential of Sentinel imagery for the retrieval and predictive mapping of aboveground biomass in mangrove area was evaluated. I used both Sentinel SAR and multispectral imagery. Biomass prediction models were developed through linear regression and Machine Learning algorithms, each from SAR backscatter data, multispectral bands, vegetation indices, and canopy biophysical variables. The results show that the model based on biophysical variable Leaf Area Index (LAI) derived from Sentinel-2 was more accurate in predicting the overall aboveground biomass. However, the SAR-based model was more accurate in predicting the biomass in the usually deficient-to-low vegetation cover replacement land uses. These models had 0.82 to 0.83 correlation/agreement of observed and predicted value. Overall, Sentinel-1 SAR and Sentinel-2 multispectral imagery can provide satisfactory results in the retrieval and predictive mapping of aboveground biomass in mangrove area. In the third objective, the soil C stocks of the study land uses were quantified to estimate C losses in soil owing to conversion. I also evaluated the potential of GIS-based Ordinary Kriging for predictive mapping of the soil C stock distribution in the entire study site. On average, the soil C stock of mangrove forests was 851.9 MgC ha-1 while that of their non-forest competing land uses was less than half at 365.15 MgC ha-1. Aquaculture, salt pond and cleared mangrove had comparable C stocks (453.6, 401, 413 MgC ha-1, respectively) and coconut plantation had the least (42.2 MgC ha-1). Overall, C losses in soil owing to land use conversion in mangrove ranged from 398 to 809 MgC ha-1 (mean: 486.8 MgC ha-1) or a decline of 57% in soil C stock, on average. It was possible to map the site-scale spatial distribution of soil C stock and predict their values with 85% overall certainty using Ordinary Kriging approach. To achieve the fourth objective, the soil fluxes of CO2, CH4 and N2O in the study land uses were investigated using static chamber method. I also evaluated the potential of GIS-based Ordinary Kriging for predictive mapping of the soil GHG fluxes in the entire study site. Results show that the emissions of CO2 and CH4 were higher in mangrove forests by 2.6 and 6.6 times, respectively, while N2O emissions were lower by 34 times compared to the average of non-forest land uses. CH4 and N2O emissions accounted for 0.59% and 0.04% of the total emissions in mangrove forest as compared to 0.23% and 3.07% for non-forest land uses, respectively. Site-scale soil GHG flux distribution could be mapped with 75% to 83% accuracy using Ordinary Kriging. This study has shown that C losses in biomass and soil arising from mangrove conversion are substantial (63%; 571 MgC ha-1). Moreover, mangrove conversion heavily altered the soil-atmosphere fluxes of GHG, increasing the N2O fluxes by 34 times. The use of Sentinel imagery for biomass mapping, as well as the application of Ordinary Kriging for soil mapping of C stocks and GHG fluxes, offer good potentials for mangrove area monitoring. This study advances current knowledge on the C stocks and soil GHG fluxes in mangrove area and the C emissions owing to mangrove conversion. The mapping techniques presented here contribute to advancing the knowledge for mapping the biomass and soil attributes in mangrove ecosystem

Predicting forest cover in distinct ecosystems: the potential of multi-source sentinel-1 and -2 data fusion

The fusion of microwave and optical data sets is expected to provide great potential for the derivation of forest cover around the globe. As Sentinel-1 and Sentinel-2 are now both operating in twin mode, they can provide an unprecedented data source to build dense spatial and temporal high-resolution time series across a variety of wavelengths. This study investigates (i) the ability of the individual sensors and (ii) their joint potential to delineate forest cover for study sites in two highly varied landscapes located in Germany (temperate dense mixed forests) and South Africa (open savanna woody vegetation and forest plantations). We used multi-temporal Sentinel-1 and single time steps of Sentinel-2 data in combination to derive accurate forest/non-forest (FNF) information via machine-learning classifiers. The forest classification accuracies were 90.9% and 93.2% for South Africa and Thuringia, respectively, estimated while using autocorrelation corrected spatial cross-validation (CV) for the fused data set. Sentinel-1 only classifications provided the lowest overall accuracy of 87.5%, while Sentinel-2 based classifications led to higher accuracies of 91.9%. Sentinel-2 short-wave infrared (SWIR) channels, biophysical parameters (Leaf Area Index (LAI), and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)) and the lower spectrum of the Sentinel-1 synthetic aperture radar (SAR) time series were found to be most distinctive in the detection of forest cover. In contrast to homogenous forests sites, Sentinel-1 time series information improved forest cover predictions in open savanna-like environments with heterogeneous regional features. The presented approach proved to be robust and it displayed the benefit of fusing optical and SAR data at high spatial resolution

Automated machine learning driven stacked ensemble modelling for forest aboveground biomass prediction using multitemporal sentinel-2 data

Modelling and large-scale mapping of forest aboveground biomass (AGB) is a complicated, challenging and expensive task. There are considerable variations in forest characteristics that creates functional disparity for different models and needs comprehensive evaluation. Moreover, the human-bias involved in the process of modelling and evaluation affects the generalization of models at larger scales. In this paper, we present an automated machine learning (AutoML) framework for modelling, evaluation and stacking of multiple base models for AGB prediction. We incorporate a hyperparameter optimization procedure for automatic extraction of targeted features from multitemporal Sentinel-2 data that minimizes human-bias in the proposed modelling pipeline. We integrate the two independent frameworks for automatic feature extraction and automatic model ensembling and evaluation. The results suggest that the extracted target-oriented features have excessive contribution of red-edge and short-wave infrared spectrum. The feature importance scale indicates a dominant role of summer based features as compared to other seasons. The automated ensembling and evaluation framework produced a stacked ensemble of base models that outperformed individual base models in accurately predicting forest AGB. The stacked ensemble model delivered the best scores of R2 cv = 0.71 and RMSE = 74.44 Mgha-1 . The other base models delivered R2 cv and RMSE ranging between 0.38–0.66 and 81.27– 109.44 Mg ha-1 respectively. The model evaluation metrics indicated that the stacked ensemble model was more resistant to outliers and achieved a better generalization. Thus, the proposed study demonstrated an effective automated modelling pipeline for predicting AGB by minimizing human-bias and deployable over large and diverse forest area

Changes in mangrove carbon stocks and exposure to Sea Level Rise (SLR) under future climate scenarios

Mangrove ecosystems are threatened by a variety of anthropogenic changes, including climate change. The main aim of this research is to quantify the spatial variation in the different mangrove carbon stocks, aboveground carbon (AGC), belowground carbon (BGC), and soil carbon (SOC), under future climate scenarios. Additionally, we sought to identify the magnitude of sea-level rise (SLR) exposure with the view of identifying the mangrove regions most likely to face elevated inundation. Different representative concentration pathways (RCPs) ranging from the most optimistic (RCP 2.6) to medium emissions (RCP 4.5) and the most pessimistic (RCP 8.5) were considered for 2070. We used the Marine Ecoregions of the World (MEOW), a biogeographical classification of coastal ecosystems, to quantify the variation in future carbon stocks at a regional scale and identify areas of potential carbon stock losses and gains. Here, we showed that the mangroves of Central and Western Indo-Pacific islands (Andamans, Papua New Guinea, and Vanuatu), the west African coast, and northeastern South America will be the worst hit and are projected to affect all three carbon stocks under all future scenarios. For instance, the Andaman ecoregion is projected to have an 11–25% decline in SOC accumulation, while the Western Indo-Pacific realm is projected to undergo the sharpest declines, ranging from 10% to 12% under all three scenarios. Examples of these areas are those in Amazonia and the eastern part of South Asia (such as in the Northern Bay of Bengal ecoregion). Based on these findings, conservation management of mangroves can be conducted