DeepWheat: Estimating Phenotypic Traits from Crop Images with Deep Learning

In this paper, we investigate estimating emergence and biomass traits from color images and elevation maps of wheat field plots. We employ a state-of-the-art deconvolutional network for segmentation and convolutional architectures, with residual and Inception-like layers, to estimate traits via high dimensional nonlinear regression. Evaluation was performed on two different species of wheat, grown in field plots for an experimental plant breeding study. Our framework achieves satisfactory performance with mean and standard deviation of absolute difference of 1.05 and 1.40 counts for emergence and 1.45 and 2.05 for biomass estimation. Our results for counting wheat plants from field images are better than the accuracy reported for the similar, but arguably less difficult, task of counting leaves from indoor images of rosette plants. Our results for biomass estimation, even with a very small dataset, improve upon all previously proposed approaches in the literature.Comment: WACV 2018 (Code repository: https://github.com/p2irc/deepwheat_WACV-2018

Modeling β Diversity and Simpson’s Index Using Hyperion Reflectance in Vansda National Park, Gujarat

Global biodiversity is under threat due to increasing anthropogenic activities. Pressure on biodiversity is immense especially in rapidly developing countries like India.  In the present study, an attempt has been made to establish accurate relationships between Hyperion (EO1) reflectance spectra and measured β diversity index and Simpson’s index of the tropical moist deciduous forest of the study area. Developed accurate models can help in mapping and assessment of diversity at larger spatial scales. The efficiency of statistical modeling techniques including Partial Least Square (PLS) regression and Multiple Linear Regression (MLR), is demonstrated in this study (with maximum R2 of 0.74 and 0.73 for PLS and MLR respectively). A vegetation index (SR 1457/933) is introduced for β diversity estimation, yielding exceptional accuracy in model development and validation (with a maximum R2 of 0.63)

Concept to Practice of Geospatial-Information Tools to Assist Forest Management and Planning under Precision Forestry Framework: a review

Precision forestry is a new direction for better forest management. Precision forestry employs information technology and analytical tools to support economic, environmental and sustainable decision; the use of geospatial information tools enables highly repeatable measurements, actions and processes to manage and harvest forest stands, simultaneously allowing information linkages between production and wood supply chain, including resource managers and environmental community. In this report, we reviewed the most recent advances in the use of geospatial information technologies in forestry, and discussed their potential opportunities and challenges towards forest management and planning in the framework of precision forestry

Above-ground biomass estimation from LiDAR data using random forest algorithms

Random forest (RF) models were developed to estimate the biomass for the Pinus radiata species in a region of the Basque Autonomous Community where this species has high cover, using the National Forest Inventory, allometric equations and low-density discrete LiDAR data. This article explores the tuning for RF hyperparameters, obtaining two models with an R2 higher than 0.7 using 2-fold cross-validation. The models selected were applied in Orozko, a municipality with more than 5000 ha of this species, where the model predicts a biomass of 1.06–1.08 Mton, which is between 16–18 % higher than the biomass predicted by the Basque Government.The work reported in this paper was partially supported by FEDER funds for the MINECO project TIN2017-85827-P and project KK-202000044 of the Elkartek 2020 funding program of the Basque Government. Additional support comes from grant IT1284-19 of the Basque Autonomous Community

Above ground biomass and tree species richness estimation with airborne lidar in tropical Ghana forests

Estimates of forest aboveground biomass are fundamental for carbon monitoring and accounting; delivering information at very high spatial resolution is especially valuable for local management, conservationand selective logging purposes. In tropical areas, hosting large biomass and biodiversity resources whichare often threatened by unsustainable anthropogenic pressures, frequent forest resources monitoring isneeded. Lidar is a powerful tool to estimate aboveground biomass at fine resolution; however its applica-tion in tropical forests has been limited, with high variability in the accuracy of results. Lidar pulses scanthe forest vertical profile, and can provide structure information which is also linked to biodiversity. Inthe last decade the remote sensing of biodiversity has received great attention, but few studies focusedon the use of lidar for assessing tree species richness in tropical forests.This research aims at estimating aboveground biomass and tree species richness using discrete returnairborne lidar in Ghana forests. We tested an advanced statistical technique, Multivariate AdaptiveRegression Splines (MARS), which does not require assumptions on data distribution or on the rela-tionships between variables, being suitable for studying ecological variables.We compared the MARS regression results with those obtained by multilinear regression and foundthat both algorithms were effective, but MARS provided higher accuracy either for biomass (R2= 0.72)and species richness (R2= 0.64). We also noted strong correlation between biodiversity and biomass fieldvalues. Even if the forest areas under analysis are limited in extent and represent peculiar ecosystems, thepreliminary indications produced by our study suggest that instrument such as lidar, specifically usefulfor pinpointing forest structure, can also be exploited as a support for tree species richness assessment

Modelling seagrass blue carbon stock in seagrass-mangrove habitats using remote sensing approach

Modelling seagrass blue carbon stocks are essential to complement the satellitebased remote sensing in detecting the underground seagrass carbon stocks. The green carbon initiatives have for long reported the detailed mapping and estimation procedural as well as the audit protocol of the global terrestrial carbon stocks. Research on the blue carbon mapping and its related modelling and estimation, on the other hand, is rarely if ever published as part of its importance is realised but remained scattered. Therefore, this study aimed at investigating blue carbon stocks in seagrass habitats by estimating the total carbon stored in seagrass using the satellite-based technique. The specific objectives are to : 1) assess and adapt some selected models for deriving seagrass total above-ground carbon (STAGC); 2) formulate new approach based-on selected models to combine with in-situ data, to model and estimate blue carbon stocks from seagrass total below-ground carbon (STBGC); 3) develop a novel technique using the selected models with soil organic carbon (SOC) to model and estimate the blue carbon stocks from seagrass total soil organic carbon (STSOC); and 4) integrate all the models (STAGC, STBGC, and STSOC) to produce a framework for the mapping and estimation of seagrass total blue carbon stock (STBCS). Suitable logistic functions were selected and applied on the satellite images to investigate seagrass, and soil carbon stocks along the seagrass meadows of Peninsular Malaysia (PM) coastline All the Landsat ETM+’s shortwave visible bands (blue, green, red) were employed for detecting and mapping seagrass stocks boundary within the coastline of PM. The derivation of STAGC was adopted from the existing bottom reflectance index (BRI) based technique via establishing a strong relationship between BRI with seagrass total aboveground biomass (STAGB). While for STBGC estimation, the STAGB^ (STAGB obtained from BRI image) were correlated with seagrass total below-ground biomass derived from insitu measurement (STBGB^^ro). Both these STAGB^ and STBGB^.^ro were converted into STAGC and STBGC using a conversion factor. Furthermore, the derivation of seagrass total soil organic carbon derived via laboratory test (STSOCi^b) was achieved through correlating BRI values with corresponding in-situ samples of soil organic carbon (SOC) obtained from the laboratory analysis by the Carbon-Hydrogen Nitrogen Sulphur (CHNS) analyser. These models were generated from the three major sample areas (Johor, Penang, and Terengganu), which were used to estimate the entire seagrass carbon stocks in the coastline of PM. The models revealed a robust correlation results for BRI versus STAGB (R2 = 0.962, p< 0.001), STAGB^, versus STBGB/A,wro (R2 = 0.933, p< 0.001,), and BRI and STSOC (R2 = 0 .989, p< 0.001) respectively. The STBCS for the whole seagrass meadows along the coastline of PM was finally realised, demonstrating a good agreement in accuracy assessment (Root Mean Square Error (RMSE) = +- <1MtC/ha\). It is, therefore, concluded that the new approach introduced by this research on STBGC and STSOC estimation was tested and proved significant on the entire STBCS quantification for the PM coastline. The contributions are critical to fast-track the United Nations Framework Convention on Climate Change (UNFCCC) agreement to report the STBCS contents. Hence, this study has managed to propose a new fundamental initiative for estimating STBCS for speedy realisation of 2020 agenda on targets 14.2 and 14.5 of United Nations’ Sustainable Development Goal 14th (life below the water)

Airborne and spaceborne remote sensing for assessment of forest structural attributes across tropical mosaic landscapes

High-resolution, accurate, and updated forest structure maps are urgently required for the implementation of REDD+, payment of ecosystem services, and other climate change mitigation strategies in tropical countries. The collection of forest inventory data is usually labor intensive and costly, and remote sites can be difficult to access. Remote sensing data, for example airborne laser scanning (ALS), hyperspectral imagery, and Landsat data, complement field-based forest inventories and provide high-resolution, accurate, and spatially explicit data for mapping forest structural attributes. However, issues such as the effect of topography, pulse density, and the single and combined use of various remote sensing data on forest structural attributes prediction warrant further research. The main objective of this thesis was to assess airborne and spaceborne remote sensing techniques for modeling forest structural attributes across a montane forest landscape in the Taita Hills, Kenya. The sub-objectives focused on a) the effect of the topographic normalization of Landsat images on fractional cover (Fcover) prediction, aboveground biomass (AGB), and forest structural heterogeneity modeling using ALS and other remote sensing data and b) the analysis of the maps of forest structural attributes. In Study I, the effect of topographic normalization on ALS-based Fcover modeling was evaluated using common vegetation indices and spectral-temporal metrics based on a Landsat time series (LTS). The results demonstrate that the fit of the Fcover models did not improve after topographic normalization in the case of ratio-based vegetation indices (Normalized Difference Vegetation Index, NDVI; reduced simple ratio, RSR) or tasseled cap (TC) greenness; however, the fit improved in the case of brightness and wetness, particularly in the period of the lowest sun elevation. However, if TC indices are preferred, then topographic normalization using a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) is recommended. In Study II, field-based AGB estimates are modeled by ALS data and a multiple linear regression. The plot-level AGB was modeled with a coefficient of determination (R2) of 0.88 and a root mean square error (RMSE) of 52.9 Mg ha-1. Furthermore, the determinants for AGB spatial distribution are examined using geospatial data and statistical modeling. The AGB patterns are controlled mainly by mean annual precipitation (MAP), the distribution of croplands, and slope, which collectively explained 69.8% of the AGB variation. Study III investigated whether the fusion of ALS with LTS and hyperspectral data, or stratification of the plots to the forest and non-forest classes, improves AGB modeling. According to the results, the prediction model based on ALS data only provides accurate models even without stratification. However, using ALS and HS data together, and employing an additional forest classification for stratification, improves the model accuracy considerably in the studied landscape. Finally, in Study IV, the potential of single and combined ALS and LTS data in modeling forest structural heterogeneity (the Gini coefficient of tree size) was assessed, and the difference between three forest remnants and forest types is evaluated based on predicted maps. If the LTS metrics were included in the models, then ALS data with lower pulse density yield similar accuracy to more expensive, high pulse-density data. Furthermore, the GC map presents forest structural heterogeneity patterns at the landscape scale a

Quantifying Aboveground Biomass in a Tropical Forest Using a Lidar Waveform Weighted Allometric Model

Our knowledge of the distribution and amount of terrestrial above ground biomass (AGB) has increased using lidar technology. Recent advancements in satellite lidar has enabled global mapping of forest biomass and structure. However, there are large biases in satellite lidar estimates which impacts our understanding of carbon dynamics, particularly in tropical forests. Ni-Meister et al. (2022) developed a lidar full waveform weighted height-based allometric model which produced very good results in temperate deciduous/conifer forest in the continental US. The purpose of this study was to evaluate this biomass model in an African tropical forest using the Land Vegetation and Ice Sensor (LVIS) lidar system. The results were compared with field measured AGB derived from a generalized pan-topical AGB equation (Chave et al. 2014). Our analysis shows that the biomass model outperforms two regression based biomass models using LVIS and small footprint lidar data. It performs very well (R2=0.84, RMSE=55.67), producing similar results to the best fitted RH empirical model (R2=0.87, RMSE=49.02). However, the biomass model outperforms the RH model when including the wood density parameter from field data (R2=0.91, RMSE=40.47). The height scaling exponent estimated using site-based allometric relationships from individual tree structure and literature data matches well with the optimal height scaling exponent through fitting the model prediction and field data. Testing in a disturbed/young forest site indicates a slight larger scaling exponent and provide much more accurate AGB estimates for young stands. This result implies that the allometric relationships might be different for young and mature forest stands even for the same forest species. The larger scaling exponent for young stands than mature stands also suggests strong AGBD and height dependence for young stands than mature stands. Our model captures the nature of AGBD dependence on height and crown size structure features. The large returns shown in waveforms for mature trees suggests large dependence ABGD on crown size properties for mature forest stands. Our assessment results that this biomass model can be expanded to estimate AGB density in tropical forest biomes using the GEDI satellite lidar data with good accuracies