Tallo: A global tree allometry and crown architecture database.

Data capturing multiple axes of tree size and shape, such as a tree's stem diameter, height and crown size, underpin a wide range of ecological research-from developing and testing theory on forest structure and dynamics, to estimating forest carbon stocks and their uncertainties, and integrating remote sensing imagery into forest monitoring programmes. However, these data can be surprisingly hard to come by, particularly for certain regions of the world and for specific taxonomic groups, posing a real barrier to progress in these fields. To overcome this challenge, we developed the Tallo database, a collection of 498,838 georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. These data were collected at 61,856 globally distributed sites, spanning all major forested and non-forested biomes. The majority of trees in the database are identified to species (88%), and collectively Tallo includes data for 5163 species distributed across 1453 genera and 187 plant families. The database is publicly archived under a CC-BY 4.0 licence and can be access from: https://doi.org/10.5281/zenodo.6637599. To demonstrate its value, here we present three case studies that highlight how the Tallo database can be used to address a range of theoretical and applied questions in ecology-from testing the predictions of metabolic scaling theory, to exploring the limits of tree allometric plasticity along environmental gradients and modelling global variation in maximum attainable tree height. In doing so, we provide a key resource for field ecologists, remote sensing researchers and the modelling community working together to better understand the role that trees play in regulating the terrestrial carbon cycle.

Integration of high-resolution optical and SAR satellite remote sensing datasets for aboveground biomass estimation in subtropical pine forest, Pakistan.

In this study, we investigate stand-alone and combined Pleiades high-resolution passive optical and ALOS PALSAR active Synthetic Aperture Radar (SAR) satellite imagery for aboveground biomass (AGB) estimation in subtropical mountainous Chir Pine (Pinus roxburghii) forest in Murree Forest Division, Punjab, Pakistan. Spectral vegetation indices (NDVI, SAVI, etc.) and sigma nought HV-polarization backscatter dB values are derived from processing optical and SAR datasets, respectively, and modeled against field-measured AGB values through various regression models (linear, nonlinear, multi-linear). For combination of multiple spectral indices, NDVI, TNDVI, and MSAVI2 performed the best with model R2/RMSE values of 0.86/47.3 tons/ha. AGB modeling with SAR sigma nought dB values gives low model R2 value of 0.39. The multi-linear combination of SAR sigma nought dB values with spectral indices exhibits more variability as compared with the combined spectral indices model. The Leave-One-Out-Cross-Validation (LOOCV) results follow closely the behavior of the model statistics. SAR data reaches AGB saturation at around 120-140 tons/ha, with the region of high sensitivity around 50-130 tons/ha; the SAR-derived AGB results show clear underestimation at higher AGB values. The models involving only spectral indices underestimate AGB at low values (< 60 tons/ha). This study presents biomass estimation maps of the Chir Pine forest in the study area and also the suitability of optical and SAR satellite imagery for estimating various biomass ranges. The results of this work can be utilized towards environmental monitoring and policy-level applications, including forest ecosystem management, environmental impact assessment, and performance-based REDD+ payment distribution.

Decadal land cover change dynamics in Bhutan.

Land cover (LC) is one of the most important and easily detectable indicators of change in ecosystem services and livelihood support systems. This paper describes the decadal dynamics in LC changes at national and sub-national level in Bhutan derived by applying object-based image analysis (OBIA) techniques to 1990, 2000, and 2010 Landsat (30 m spatial resolution) data. Ten LC classes were defined in order to give a harmonized legend land cover classification system (LCCS). An accuracy of 83% was achieved for LC-2010 as determined from spot analysis using very high resolution satellite data from Google Earth Pro and limited field verification. At the national level, overall forest increased from 25,558 to 26,732 km(2) between 1990 and 2010, equivalent to an average annual growth rate of 59 km(2)/year (0.22%). There was an overall reduction in grassland, shrubland, and barren area, but the observations were highly dependent on time of acquisition of the satellite data and climatic conditions. The greatest change from non-forest to forest (277 km(2)) was in Bumthang district, followed by Wangdue Phodrang and Trashigang, with the least (1 km(2)) in Tsirang. Forest and scrub forest covers close to 75% of the land area of Bhutan, and just over half of the total area (51%) has some form of conservation status. This study indicates that numerous applications and analyses can be carried out to support improved land cover and land use (LCLU) management. It will be possible to replicate this study in the future as comparable new satellite data is scheduled to become available.

Development of 2010 national land cover database for the Nepal.

Land cover and its change analysis across the Hindu Kush Himalayan (HKH) region is realized as an urgent need to support diverse issues of environmental conservation. This study presents the first and most complete national land cover database of Nepal prepared using public domain Landsat TM data of 2010 and replicable methodology. The study estimated that 39.1% of Nepal is covered by forests and 29.83% by agriculture. Patch and edge forests constituting 23.4% of national forest cover revealed proximate biotic interferences over the forests. Core forests constituted 79.3% of forests of Protected areas where as 63% of area was under core forests in the outside protected area. Physiographic regions wise forest fragmentation analysis revealed specific conservation requirements for productive hill and mid mountain regions. Comparative analysis with Landsat TM based global land cover product showed difference of the order of 30-60% among different land cover classes stressing the need for significant improvements for national level adoption. The online web based land cover validation tool is developed for continual improvement of land cover product. The potential use of the data set for national and regional level sustainable land use planning strategies and meeting several global commitments also highlighted.

Measuring impacts of community forestry program through repeat photography and satellite remote sensing in the Dolakha district of Nepal.

During the 1990's community-based forest management gained momentum in Nepal. This study systematically evaluates the impacts that this had on land cover change and other associated aspects during the period 1990-2010 using repeat photography and satellite imagery in combination with interviews with community members. The results of the study clearly reflect the success of community-based forest management in the Dolakha district of the mid-hills of Nepal: during the study period, the rate of conversion of sparse forest into dense forest under community-based management was found to be between 1.13% and 3.39% per year. Similarly, the rate of conversion of non-forest area into forest was found to be between 1.11% and 1.96% per year. Community-based forest management has resulted in more efficient use of forest resources, contributed to a decline in the use of slash-and-burn agricultural practices, reduced the incidence of forest fires, spurred tree plantation, and encouraged the conservation and protection of trees on both public and private land. The resulting reclamation of forest in landside areas and river banks and the overall improvement in forest cover in the area has reduced flash floods and associated landslides.

Global drought monitoring with big geospatial datasets using Google Earth Engine.

Drought or dryness occurs due to the accumulative effect of certain climatological and hydrological variables over a certain period. Droughts are studied through numerically computed simple or compound indices. Vegetation condition index (VCI) is used for observing the change in vegetation that causes agricultural drought. Since the land surface temperature has minimum influence from cloud contamination and humidity in the air, so the temperature condition index (TCI) is used for studying the temperature change. Dryness or wetness of soil is a major indicator for agriculture and hydrological drought and for that purpose, the index, soil moisture condition index (SMCI), is computed. The deviation of precipitation from normal is a major cause for meteorological droughts and for that purpose, precipitation condition index (PCI) is computed. The years when the indices escalated the dryness situation to severe and extreme are pointed out in this research. Furthermore, an interactive dashboard is generated in the Google Earth Engine (GEE) for users to compute the said indices using country boundary, time period, and ecological mask of their choice: Agriculture Drought Monitoring. Apart from global results, three case studies of droughts (2002 in Australia, 2013 in Brazil, and 2019 in Thailand) computed via the dashboard are discussed in detail in this research.

Synergizing community-based forest monitoring with remote sensing: a path to an effective REDD+ MRV system.

BACKGROUND: The reliable monitoring, reporting and verification (MRV) of carbon emissions and removals from the forest sector is an important part of the efforts on reducing emissions from deforestation and forest degradation (REDD+). Forest-dependent local communities are engaged to contribute to MRV through community-based monitoring systems. The efficiency of such monitoring systems could be improved through the rational integration of the studies at permanent plots with the geospatial technologies. This article presents a case study of integrating community-based measurements at permanent plots at the foothills of central Nepal and biomass maps that were developed using GeoEye-1 and IKONS satellite images. RESULTS: The use of very-high-resolution satellite-based tree cover parameters, including crown projected area (CPA), crown density and crown size classes improves salience, reliability and legitimacy of the community-based survey of 0.04% intensity at the lower cost than increasing intensity of the community-based survey to 0.14% level (2.5 USD/ha vs. 7.5 USD/ha). CONCLUSION: The proposed REDD+ MRV complementary system is the first of its kind and demonstrates the enhancement of information content, accuracy of reporting and reduction in cost. It also allows assessment of the efficacy of community-based forest management and extension to national scale.

Mapping forests in monsoon Asia with ALOS PALSAR 50-m mosaic images and MODIS imagery in 2010.

Extensive forest changes have occurred in monsoon Asia, substantially affecting climate, carbon cycle and biodiversity. Accurate forest cover maps at fine spatial resolutions are required to qualify and quantify these effects. In this study, an algorithm was developed to map forests in 2010, with the use of structure and biomass information from the Advanced Land Observation System (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) mosaic dataset and the phenological information from MODerate Resolution Imaging Spectroradiometer (MOD13Q1 and MOD09A1) products. Our forest map (PALSARMOD50 m F/NF) was assessed through randomly selected ground truth samples from high spatial resolution images and had an overall accuracy of 95%. Total area of forests in monsoon Asia in 2010 was estimated to be ~6.3 × 10(6 )km(2). The distribution of evergreen and deciduous forests agreed reasonably well with the median Normalized Difference Vegetation Index (NDVI) in winter. PALSARMOD50 m F/NF map showed good spatial and areal agreements with selected forest maps generated by the Japan Aerospace Exploration Agency (JAXA F/NF), European Space Agency (ESA F/NF), Boston University (MCD12Q1 F/NF), Food and Agricultural Organization (FAO FRA), and University of Maryland (Landsat forests), but relatively large differences and uncertainties in tropical forests and evergreen and deciduous forests.