Estimating forest structure in a tropical forest using field measurements, a synthetic model and discrete return lidar data

Tropical forests are huge reservoirs of terrestrial carbon and are experiencing rapid degradation and deforestation. Understanding forest structure proves vital in accurately estimating both forest biomass and also the natural disturbances and remote sensing is an essential method for quantification of forest properties and structure in the tropics. Our objective is to examine canopy vegetation profiles formulated from discrete return LIght Detection And Ranging (lidar) data and examine their usefulness in estimating forest structural parameters measured during a field campaign. We developed a modeling procedure that utilized hypothetical stand characteristics to examine lidar profiles. In essence, this is a simple method to further enhance shape characteristics from the lidar profile. In this paper we report the results comparing field data collected at La Selva, Costa Rica (10° 26′ N, 83° 59′ W) and forest structure and parameters calculated from vegetation height profiles and forest structural modeling. We developed multiple regression models for each measured forest biometric property using forward stepwise variable selection that used Bayesian information criteria (BIC) as selection criteria. Among measures of forest structure, ranging from tree lateral density, diameter at breast height, and crown geometry, we found strong relationships with lidar canopy vegetation profile parameters. Metrics developed from lidar that were indicators of height of canopy were not significant in estimating plot biomass (p-value = 0.31, r2 = 0.17), but parameters from our synthetic forest model were found to be significant for estimating many of the forest structural properties, such as mean trunk diameter (p-value = 0.004, r2 = 0.51) and tree density (p-value = 0.002, r2 = 0.43). We were also able to develop a significant model relating lidar profiles to basal area (p-value = 0.003, r2 = 0.43). Use of the full lidar profile provided additional avenues for the prediction of field based forest measure parameters. Our synthetic canopy model provides a novel method for examining lidar metrics by developing a look-up table of profiles that determine profile shape, depth, and height. We suggest that the use of metrics indicating canopy height derived from lidar are limited in understanding biomass in a forest with little variation across the landscape and that there are many parameters that may be gleaned by lidar data that inform on forest biometric properties

Long-term Landsat-based monthly burned area dataset for the Brazilian biomes using Deep Learning

Fire is a significant agent of landscape transformation on Earth, and a dynamic and ephemeral process that is challenging to map. Difficulties include the seasonality of native vegetation in areas affected by fire, the high levels of spectral heterogeneity due to the spatial and temporal variability of the burned areas, distinct persistence of the fire signal, increase in cloud and smoke cover surrounding burned areas, and difficulty in detecting understory fire signals. To produce a large-scale time-series of burned area, a robust number of observations and a more efficient sampling strategy is needed. In order to overcome these challenges, we used a novel strategy based on a machine-learning algorithm to map monthly burned areas from 1985 to 2020 using Landsat-based annual quality mosaics retrieved from minimum NBR values. The annual mosaics integrated year-round observations of burned and unburned spectral data (i.e., RED, NIR, SWIR-1, and SWIR-2), and used them to train a Deep Neural Network model, which resulted in annual maps of areas burned by land use type for all six Brazilian biomes. The annual dataset was used to retrieve the frequency of the burned area, while the date on which the minimum NBR was captured in a year, was used to reconstruct 36 years of monthly burned area. Results of this effort indicated that 19.6% (1.6 million km2) of the Brazilian territory was burned from 1985 to 2020, with 61% of this area burned at least once. Most of the burning (83%) occurred between July and October. The Amazon and Cerrado, together, accounted for 85% of the area burned at least once in Brazil. Native vegetation was the land cover most affected by fire, representing 65% of the burned area, while the remaining 35% burned in areas dominated by anthropogenic land uses, mainly pasture. This novel dataset is crucial for understanding the spatial and long-term temporal dynamics of fire regimes that are fundamental for designing appropriate public policies for reducing and controlling fires in Brazil

Mind the gap: reconciling tropical forest carbon flux estimates from earth observation and national reporting requires transparency

Background: The application of different approaches calculating the anthropogenic carbon net flux from land, leads to estimates that vary considerably. One reason for these variations is the extent to which approaches consider forest land to be “managed” by humans, and thus contributing to the net anthropogenic flux. Global Earth Observation (EO) datasets characterising spatio-temporal changes in land cover and carbon stocks provide an independent and consistent approach to estimate forest carbon fluxes. These can be compared against results reported in National Greenhouse Gas Inventories (NGHGIs) to support accurate and timely measuring, reporting and verification (MRV). Using Brazil as a primary case study, with additional analysis in Indonesia and Malaysia, we compare a Global EO-based dataset of forest carbon fluxes to results reported in NGHGIs. Results: Between 2001 and 2020, the EO-derived estimates of all forest-related emissions and removals indicate that Brazil was a net sink of carbon (− 0.2 GtCO2yr−1), while Brazil’s NGHGI reported a net carbon source (+ 0.8 GtCO2yr−1). After adjusting the EO estimate to use the Brazilian NGHGI definition of managed forest and other assumptions used in the inventory’s methodology, the EO net flux became a source of + 0.6 GtCO2yr−1, comparable to the NGHGI. Remaining discrepancies are due largely to differing carbon removal factors and forest types applied in the two datasets. In Indonesia, the EO and NGHGI net flux estimates were similar (+ 0.6 GtCO2 yr−1), but in Malaysia, they differed in both magnitude and sign (NGHGI: -0.2 GtCO2 yr−1; Global EO: + 0.2 GtCO2 yr−1). Spatially explicit datasets on forest types were not publicly available for analysis from either NGHGI, limiting the possibility of detailed adjustments. Conclusions: By adjusting the EO dataset to improve comparability with carbon fluxes estimated for managed forests in the Brazilian NGHGI, initially diverging estimates were largely reconciled and remaining differences can be explained. Despite limited spatial data available for Indonesia and Malaysia, our comparison indicated specific aspects where differing approaches may explain divergence, including uncertainties and inaccuracies. Our study highlights the importance of enhanced transparency, as set out by the Paris Agreement, to enable alignment between different approaches for independent measuring and verification

Woody aboveground biomass mapping of the brazilian savanna with a multi-sensor and machine learning approach

The tropical savanna in Brazil known as the Cerrado covers circa 23% of the Brazilian territory, but only 3% of this area is protected. High rates of deforestation and degradation in the woodland and forest areas have made the Cerrado the second-largest source of carbon emissions in Brazil. However, data on these emissions are highly uncertain because of the spatial and temporal variability of the aboveground biomass (AGB) in this biome. Remote-sensing data combined with local vegetation inventories provide the means to quantify the AGB at large scales. Here, we quantify the spatial distribution of woody AGB in the Rio Vermelho watershed, located in the centre of the Cerrado, at a high spatial resolution of 30 metres, with a random forest (RF) machine-learning approach. We produced the first high-resolution map of the AGB for a region in the Brazilian Cerrado using a combination of vegetation inventory plots, airborne light detection and ranging (LiDAR) data, and multispectral and radar satellite images (Landsat 8 and ALOS-2/PALSAR-2). A combination of random forest (RF) models and jackknife analyses enabled us to select the best remote-sensing variables to quantify the AGB on a large scale. Overall, the relationship between the ground data from vegetation inventories and remote-sensing variables was strong (R2 = 0.89), with a root-mean-square error (RMSE) of 7.58 Mg ha−1 and a bias of 0.43 Mg ha−1

Zoneamento geoambiental como subsídio aos projetos de reforma agrária. Estudo de caso: assentamento rural Pirituba II (SP)

Os projetos de reforma agrária apresentam dificuldades de planejamento para uso e ocupação da terra. Esses problemas afetam a qualidade de vida das famílias, a produtividade e a sustentabilidade ambiental. Isso se deve à carência de estudos interdisciplinares detalhados de diagnósticos e zoneamentos ambientais para implantação, desenvolvimento e gestão desses assentamentos. Assim, o objetivo principal desse estudo é estabelecer o zoneamento geoambiental no assentamento rural Pirituba II (Itapeva/Itaberá/SP) e analisar o uso desse instrumento para melhorar os projetos de reforma agrária que visem a sustentabilidade socioambiental. Para isso, foram realizadas fotointerpretação de fotos aéreas (escala 1:25.000) e verificações em campo para detalhar as informações ambientais básicas de drenagem, geologia estrutural, de unidades fisiográficas, e pedológicas. Essas informações permitiram a compreensão da evolução e dinâmica da paisagem. A partir da caracterização das unidades fisiográficas colúvio-aluvionares da área foram estabelecidos os fatores e processos endógenos e exógenos que resultaram na formação das paisagens. Isto permitiu estabelecer as zonas geoambientais (unidades aloestratigráficas). Essas foram divididas em subzonas geoambientais pela análise estrutural e fisiográfica, para posteriormente determinar as potencialidades e limitações de tais unidades. Dessa forma, mapas temáticos foram elaborados quanto à: suscetibilidade à erosão, indicação de áreas para proteção ambiental e adequação a culturas anuais. A aplicação do zoneamento geoambiental no assentamento Pirituba II forneceu um estudo detalhado e integrado do meio físico para planejamento local visando a sustentabilidade socioambiental. Portanto, esse zoneamento pode ser uma ferramenta útil para a gestão territorial e melhoria dos projetos de reforma agrária.The environmental diagnostic studies that aim planning for land reform settlements are few and still present some gaps. These affect the life quality of families, productivity and environmental sustentability. Geoenvironmental zoning is based on the integration of physical aspects, and for this reason it may contribute with information that will be used for the environmental analysis of these settlements. The aim of the present study is to evaluate the geoenvironmental zoning applied to the Pirituba II Settlement (Itapeva/Itaberá/SP) as a reliable tool and instrument for the definition of lines that can help in the sustainable implementation of land reform projects, as much by the social view as by the environmental focus. For this the drainage, structural geology, physiographic unities and pedological basic environmental information were detailed through field and laboratory works (aerial photointerpretation). This information have enabled better undestanding of the landscape dynamic and evolution. Physiographic characterization for colluvial and alluvial units of the studied area permitted to establish the factors and processes, both endogenetic and geomorphic, that resulted in the landscape formation. The geoenvironmental zoning was defined by this purpose, which generate subdividing operations into structural and physiographic analysis, for as much as the potentiality and limitation determination of them as entities. The following thematic maps were obtained, therefore: erosion vulnerability, environmental protection indication and agricultural annual rotation. The results of the geoenvironmental zoning work in the Pirituba II Settlement allowed the definition of environmental planning detailed strategies in agreement with sustainable reality.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP

Detection of large-scale forest canopy change in pan-tropical humid forests 2000-2009 with the SeaWinds Ku-band scatterometer

We analyzed the 10-year record (1999–2009) of SeaWinds Ku-band microwave backscatter from humid tropical forest regions in South America, Africa, and Indonesia/Malaysia. While backscatter was relatively stable across much of the region, it declined by 1–2 dB in areas of known large-scale deforestation, and increased by up to 1–2 dB in areas of secondary forest or plantation forest growth and in major metropolitan areas. The reduction in backscatter over 142 18.5 km × 18.5 km blocks of tropical forest was correlated with gross forest cover loss (as determined from Landsat data analysis) (R = −0.78); this correlation improved when restricted to humid tropical forest blocks in South America with high initial forest cover (R = −0.93, n = 22). This study shows that scatterometer-based analyses can provide an important geophysical data record leading to robust identification of the spatial patterns and timing of large-scale change in tropical forests. The coarse spatial resolution of SeaWinds (∼10 km) makes it unsuitable for mapping deforestation at the scale of land-use activity. However, due to a combination of instrument stability, sensitivity to canopy change and insensitivity to atmospheric effects, and straight-forward data processing, Ku-band scatterometery can provide a fully independent assessment of large-scale tropical forest canopy dynamics which may complement the interpretation of higher resolution optical remote sensing

Improving estimations of GHG emissions and removals from land use change and forests in Brazil

Brazil ranks fifth in greenhouse gas emissions globally due to land use change. As a signatory to the Paris Agreement, Brazil must periodically report its GHG emissions as well as present mitigation targets set in the Nationally Determined Contribution (NDC). The SEEG Brazil Initiative (Greenhouse Gas Emission and Removal Estimating System) generates independent estimates of GHG emissions and removals since 2013, and in 2020, the estimation method for the land use change sector has been improved. This study aimed to (1) present these methodological advancements, including the spatial allocation of annual emissions and removals due to land use change (LUC) in Brazil at a 30 m spatial scale, and (2) explore the emission and removal patterns observed in Brazil from 1990 to 2019. The method presented here is built upon—but improves—the approach used by Brazil’s official National Inventories to estimate GHG emissions and removals. The improvements presented here include exploring emissions to the municipality level and using an annual updated time series of land use and land cover maps. Estimated greenhouse gas emissions from the LUC sector ranged from 687 Mt of CO _2 e in 2011 to a peak of 2150 Mt of CO _2 e in 2003. In 2010, removals nearly offset gross emissions in the sector, with a net emission of 116 Mt of CO _2 e. The trend observed in recent years was an increase in emissions, decreasing Brazil’s likelihood of meeting its NDC targets. Emission profiles vary across the country, but in every biome, the conversion of primary native vegetation is the predominant transition type. If Brazil managed to curb deforestation, the total GHG emissions from the land use change sector would decrease by 96%, mitigating around 44% of total emissions

The DegraBase: A Database of Proteolysis in Healthy and Apoptotic Human Cells*

Proteolysis is a critical post-translational modification for regulation of cellular processes. Our lab has previously developed a technique for specifically labeling unmodified protein N termini, the α-aminome, using the engineered enzyme, subtiligase. Here we present a database, called the DegraBase (http://wellslab.ucsf.edu/degrabase/), which compiles 8090 unique N termini from 3206 proteins directly identified in subtiligase-based positive enrichment mass spectrometry experiments in healthy and apoptotic human cell lines. We include both previously published and unpublished data in our analysis, resulting in a total of 2144 unique α-amines identified in healthy cells, and 6990 in cells undergoing apoptosis. The N termini derive from three general categories of proteolysis with respect to cleavage location and functional role: translational N-terminal methionine processing (∼10% of total proteolysis), sites close to the translational N terminus that likely represent removal of transit or signal peptides (∼25% of total), and finally, other endoproteolytic cuts (∼65% of total). Induction of apoptosis causes relatively little change in the first two proteolytic categories, but dramatic changes are seen in endoproteolysis. For example, we observed 1706 putative apoptotic caspase cuts, more than double the total annotated sites in the CASBAH and MEROPS databases. In the endoproteolysis category, there are a total of nearly 3000 noncaspase nontryptic cleavages that are not currently reported in the MEROPS database. These studies significantly increase the annotation for all categories of proteolysis in human cells and allow public access for investigators to explore interesting proteolytic events in healthy and apoptotic human cells