High-resolution planet satellite imagery and multi-temporal surveys to predict risk of tree mortality in tropical eucalypt forestry.

Global high-resolution imagery is a well-assimilated technology in forest mapping. The release of the Norway's International Climate & Forests Initiative (NICFI) Planet tropical basemaps time-series starting in 2015 at a 4.77-m resolution represents a unique opportunity to forecast climate change consequences such as drought episodes. Using multi-temporal ground surveys over 144 plots and publicly available high-resolution Planet dove time-series imagery we evaluate forest mortality patterns driven by imaging spectroscopy methods in Mato Grosso (Brazil) over an area planted with eucalypts severely affected by the 2019 drought. Changes in vegetation indexes before and after the 2019 drought were modelled using the effective logistic regression modelling to explain variation in tree mortality between the surveys, the dependent variable. We aimed to straightforwardly model tree mortality using change vectors in Planet's image mosaics co-registering in time with the observed tree mortality measurements in the field. The results showed differences in Normalized Difference Vegetation Index (NDVI) as the most significant predictor variable under the effective logistic regression modelling performed. The efficacy of 80.98% in concordance pairs correctly classified represented 0.81 of area under the Receiver Operating Curve (ROC). The release of the 2015-2020 Planet imagery in the tropics at 4.77-m resolution represents a valuable dataset to better understand previous natural disturbances and a powerful technology to detect in advance, and monthly after September 2020, eucalypt areas prone to harmful and increasingly frequent water-stress episodes.

Optimizing invasive species management using mathematical programming to support stewardship of water and carbon-based ecosystem services.

Invasive species alter hydrologic processes at watershed scales, with impacts to biodiversity and the supporting ecosystem services. This effect is aggravated by climate change. Here, we integrated modelled hydrologic data, remote sensing products, climate data, and linear mixed integer optimization (MIP) to identify stewardship actions across space and time that can reduce the impact of invasive species. The study area is the windward coast of Hawai'i Island (USA) across which non-native strawberry guava occurrence varies from extremely dense stands in lower watershed reaches, to low densities in upper watershed forests. We focused on the removal of strawberry guava, an invader that exerts significant impacts on watershed condition. MIP analyses spatially optimized the assignment of effective management actions to increase water yield, generate revenue from enhanced freshwater services, and income from removed biomass. The hydrological benefit of removing guava, often marginal when considered in isolation, was financially quantified, and single- and multiobjective MIP formulations were then developed over a 10-year planning horizon. Optimization resulted in $2.27 million USD benefit over the planning horizon using a payment-for-ecosystem-services scheme. That value jumped to $4.67 million when allowing work schedules with overnight camping to reduce costs. Pareto frontiers of weighted pairs of management goals showed the benefit of clustering treatments over space and time to improve financial efficiency. Values of improved land-water natural capital using payment-for-ecosystem-services schemes are provided for several combinations of spatial, temporal, economical, and ecosystem services flows.

Monitoring Water-Soil Dynamics and Tree Survival Using Soil Sensors under a Big Data Approach.

The high importance of green urban planning to ensure access to green areas requires modern and multi-source decision-support tools. The integration of remote sensing data and sensor developments can contribute to the improvement of decision-making in urban forestry. This study proposes a novel big data-based methodology that combines real-time information from soil sensors and climate data to monitor the establishment of a new urban forest in semi-arid conditions. Water-soil dynamics and their implication in tree survival were analyzed considering the application of different treatment restoration techniques oriented to facilitate the recovery of tree and shrub vegetation in the degraded area. The synchronized data-capturing scheme made it possible to evaluate hourly, daily, and seasonal changes in soil-water dynamics. The spatial variation of soil-water dynamics was captured by the sensors and it highly contributed to the explanation of the observed ground measurements on tree survival. The methodology showed how the efficiency of treatments varied depending on species selection and across the experimental design. The use of retainers for improving soil moisture content and adjusting tree-watering needs was, on average, the most successful restoration technique. The results and the applied calibration of the sensor technology highlighted the random behavior of water-soil dynamics despite the small-scale scope of the experiment. The results showed the potential of this methodology to assess watering needs and adjust watering resources to the vegetation status using real-time atmospheric and soil data.

Integrated LiDAR-supported valuation of biomass and litter in forest ecosystems. A showcase in Spain.

Belowground components (biomass and soils) can stock as much carbon as the aboveground component of forest ecosystems. In this study, we present a fully-integrated assessment of the biomass budget and the three pools evaluated: aboveground (AGBD) and belowground biomass in root systems (BGBD) and litter (LD). We turned National Forest Inventory data, airborne Light Detection and Ranging (LiDAR) data actionable to map three biomass compartments at 25-m resolution over more than 2.7 million ha of Mediterranean forests in the South-West of Spain. We assessed distributions and balanced among the three modelled components for the entire region of Extremadura and specifically for five representative forest types. Our results showed belowground biomass and litter represent an important 61 % of the AGBD stock. Among forest types, AGBD stocks were the dominant pool in pine-dominated areas while its lowers contribution was found over sparse oak forests. The three biomass pools estimated at the same resolution were used to produce ratio-based indicators to highlight areas where the contribution of belowground biomass and litter can exceed AGBD and where carbon-sequestration and conservation practices should acknowledge belowground-oriented carbon management. The recognition and valuation of biomass and carbon stocks beyond the AGBD is a must step forward that the scientific community must support in order to properly assess living components of the ecosystem such as root systems sustaining AGBD stocks and to value carbon-oriented ecosystem services related to soil-water dynamics and soil biodiversity. This study aims at enforcing a change of paradigm in forest carbon accounting, advocating for a better recognition and broader integration of living biomass in land carbon mapping.

The overlooked contribution of trees outside forests to tree cover and woody biomass across Europe.

Trees are an integral part in European landscapes, but only forest resources are systematically assessed by national inventories. The contribution of urban and agricultural trees to national-level carbon stocks remains largely unknown. Here we produced canopy cover, height and above-ground biomass maps from 3-meter resolution nanosatellite imagery across Europe. Our biomass estimates have a systematic bias of 7.6% (overestimation; R = 0.98) compared to national inventories of 30 countries, and our dataset is sufficiently highly resolved spatially to support the inclusion of tree biomass outside forests, which we quantify to 0.8 petagrams. Although this represents only 2% of the total tree biomass, large variations between countries are found (10% for UK) and trees in urban areas contribute substantially to national carbon stocks (8% for the Netherlands). The agreement with national inventory data, the scalability, and spatial details across landscapes, including trees outside forests, make our approach attractive for operational implementation to support national carbon stock inventory schemes.

Evaluation of mushroom production potential by combining spatial optimization and LiDAR-based forest mapping data.

High-resolution forest mapping technology is a powerful data source to assess the production capacity of forests regarding wood and non-wood ecosystem services. The study shows how to evaluate the potential benefits from forest management treatments devoted to increase mushroom supply. The study was developed in Central Spain, over a forest with important cultural and economic values attached to mushrooms. Airborne laser scanning (ALS), mushroom production models and mathematical programming as spatial optimization method are used to sequence, spatially and temporally, silviculture-oriented actions to enlarge mushroom provisioning. We present a tactical forest planning solution to incentivize mushroom yield driven by clustered silvicultural treatments applied to fine-grained segments derived from ALS data, and along a 5-year plan while embedding temporal and spatial dependencies. Mushroom yield can increase up to 18 % from current conditions if all area is treated. Our model integrates constraints to optimize the selection of segments yielding the highest benefits in terms of mushroom yield and timber removals during the treatments. The temporal sequencing was successful, so the annual interventions are scheduled aligned in space and in time to ease the actionability and realism of model outputs. The assessment of production potential is an informative, spatially and temporally explicit exercise to inform decision-makers on investment opportunities to enhance the supply of non-wood ecosystem services, tested with mushroom in this study but extendable to more non-wood ecosystem services.