Multiscale analysis of tree cover and aboveground carbon stocks in pinyon-juniper woodlands

Image-based vegetation analysis of desertified area by using a combination of ImageJ and Photoshop software

Fractional vegetation cover (FVC) is a crucial indicator to estimate degradation and desertification for grasslands. However, traditional small-scale FVC analysis methods, such as visual estimation and point-sampling, are cumbersome and imprecise. Innovative methods like image-based FVC analysis methods, while accurate, face challenges such as complex analytical procedures and the necessary training for operations. Therefore, in this study, a combined application of ImageJ and Photoshop was employed to achieve a more effective analysis of FVC values in desertification areas. Our results showed that the FVC results obtained by combination of Photoshop and ImageJ were dependable and precise (R2 > 0.98), demonstrating equivalency to results obtained through either visual estimation or Photoshop-based methods. Furthermore, even in the face of background interference and varied shooting angles, the combination of ImageJ and Photoshop software was still able to maintain a low error rate when analyzing FVC values (average error rate =  - 2.6%). In conclusion, the imaged-based combined FVC analysis method employed in our research was an effective, precise, and efficient technique for analyzing small-scale FVC, promising substantial improvement over conventional methods.

Short-Term Recovery of the Aboveground Carbon Stock in Iberian Shrublands at the Extremes of an Environmental Gradient and as a Function of Burn Severity

The degree to which burn severity influences the recovery of aboveground carbon density (ACD) of live pools in shrublands remains unclear. Multitemporal LiDAR data was used to evaluate ACD recovery three years after fire in shrubland ecosystems as a function of burn severity immediately after fire across an environmental and productivity gradient in the western Mediterranean Basin. Two large mixed-severity wildfires were assessed: an Atlantic site, dominated by resprouter shrubs and located at the most productive extreme of the gradient, and a Mediterranean site, dominated by obligate seeders and located at the less productive extreme. Initial assessment of burn severity was performed using the differenced Normalized Burn Ratio index computed from Landsat imagery. Thresholds for low and high burn severity categories were established using the Composite Burn Index (CBI). LiDAR canopy metrics were calibrated with field measurements of mean shrub height and cover at plot level in a post-fire situation. Pre-fire and post-fire ACD estimates, and their ratio (ACDr) to calculate carbon stock recovery, were computed from the predictions of LiDAR grid metrics at landscape level using shrubland allometric relationships. Overall, ACDr decreased both with high burn severity and low productivity, although the burn severity impact was not homogeneous within the gradient. In the Atlantic site, ACDr was similar under low and high burn severity, whereas it decreased with burn severity in the Mediterranean site. These results suggest that carbon cycling models could be biased by not accounting for both fire severity and species composition of shrublands under different environmental conditions.

Monitoring Carbon Stock and Land-Use Change in 5000-Year-Old Juniper Forest Stand of Ziarat, Balochistan, through a Synergistic Approach

The Juniper forest reserve of Ziarat is one of the biggest Juniperus forests in the world. This study assessed the land-use changes and carbon stock of Ziarat. Different types of carbon pools were quantified in terms of storage in the study area in tons/ha i.e., above ground, soil, shrubs and litter. The Juniper species of this forest is putatively called Juniperus excelsa Beiberstein. To estimate above-ground biomass, different allometric equations were applied. Average above ground carbon stock of the forest was estimated as 8.34 ton/ha, 7.79 ton/ha and 8.4 ton/ha using each equation. Average carbon stock in soil, shrubs and litter was calculated as 24.35 ton/ha, 0.05 ton/ha and 1.52 ton/ha, respectively. Based on our results, soil carbon stock in the Juniper forest of Ziarat came out to be higher than the living biomass. Furthermore, the spatio-temporal classified maps for Ziarat showed that forest area has significantly decreased, while agricultural and barren lands increased from 1988 to 2018. This was supported by the fact that estimated carbon stock also showed a decreasing pattern between the evaluation periods of 1988 to 2018. Furthermore, the trend for land use and carbon stock was estimated post 2018 using a linear prediction model. The results corroborate the assumption that under a business as usual scenario, it is highly likely that the Juniperus forest will severely decline.

Tree Cover Estimation in Global Drylands from Space Using Deep Learning

Accurate tree cover mapping is of paramount importance in many fields, from biodiversity conservation to carbon stock estimation, ecohydrology, erosion control, or Earth system modelling. Despite this importance, there is still uncertainty about global forest cover, particularly in drylands. Recently, the Food and Agriculture Organization of the United Nations (FAO) conducted a costly global assessment of dryland forest cover through the visual interpretation of orthoimages using the Collect Earth software, involving hundreds of operators from around the world. Our study proposes a new automatic method for estimating tree cover using artificial intelligence and free orthoimages. Our results show that our tree cover classification model, based on convolutional neural networks (CNN), is 23% more accurate than the manual visual interpretation used by FAO, reaching up to 79% overall accuracy. The smallest differences between the two methods occurred in the driest regions, but disagreement increased with the percentage of tree cover. The application of CNNs could be used to improve and reduce the cost of tree cover maps from the local to the global scale, with broad implications for research and management.

The positive carbon stocks-biodiversity relationship in forests: co-occurrence and drivers across five subclimates

Carbon storage in forests and its ability to offset global greenhouse gas emissions, as well as biodiversity and its capacity to support ecosystem functions and services, are often considered separately in landscape planning. However, the potential synergies between them are currently poorly understood. Identifying the spatial patterns and factors driving their co-occurrence across different climatic zones is critical to more effectively conserve forest ecosystems at the regional level. Here, we integrated information of National Forest Inventories and Breeding Bird Atlases across Europe and North America (Spain and Quebec, respectively), covering five subclimates (steppe, dry Mediterranean, humid Mediterranean, boreal, and temperate). In particular, this study aimed to (1) determine the spatial patterns of both forest carbon stocks and biodiversity (bird richness, tree richness, and overall biodiversity) and the factors that influence them; (2) establish the relationships between forest carbon stocks and biodiversity; and (3) define and characterize the areas of high (hotspots) and low (coldspots) values of carbon and biodiversity, and ultimately quantify their spatial overlap. Our results show that the factors affecting carbon and biodiversity vary between regions and subclimates. The highest values of carbon and biodiversity were found in northern Spain (humid Mediterranean subclimate) and southern Quebec (temperate subclimate) where there was more carbon as climate conditions were less limiting. High density and structural diversity simultaneously favored carbon stocks, tree, and overall biodiversity, especially in isolated and mountainous areas, often associated with steeper slopes and low accessibility. In addition, the relationship between carbon stocks and biodiversity was positive in both regions and all subclimates, being stronger where climate is a limiting factor for forest growth. The spatial overlap between hotspots of carbon and biodiversity provides an excellent opportunity for landscape planning to maintain carbon stocks and conserve biodiversity. The variables positively affecting carbon and biodiversity were also driving the hotspots of both carbon and biodiversity, emphasizing the viability of "win-win" solutions. Our results highlight the need to jointly determine the spatial patterns of ecosystem services and biodiversity for an effective and sustainable planning of forest landscapes that simultaneously support conservation and mitigate climate change.

Twentieth century carbon stock changes related to Piñon-Juniper expansion into a black sagebrush community

BACKGROUND

Increases in the spatial extent and density of woody plants relative to herbaceous species have been observed across many ecosystems. These changes can have large effects on ecosystem carbon stocks and therefore are of interest for regional and national carbon inventories and for potential carbon sequestration or management activities. However, it is challenging to estimate the effect of woody plant encroachment on carbon because aboveground carbon stocks are very heterogeneous spatially and belowground carbon stocks exhibit complex and variable responses to changing plant cover. As a result, estimates of carbon stock changes with woody plant cover remain highly uncertain. In this study, we use a combination of plot- and remote sensing-based techniques to estimate the carbon impacts of piñon and juniper (PJ) encroachment in SE Utah across a variety of spatial scales with a specific focus on the role of spatial heterogeneity in carbon estimates.

RESULTS

At a plot scale (300 m2) areas piñon juniper (PJ) encroached areas had 0.26 kg C m-2 less understory vegetation carbon compared to un-encroached sites. This lower amount of carbon was offset by an average of 1.82 kg C m-2 higher carbon in PJ vegetation and 0.50 kg m-2 of C in PJ surface-litter carbon. Soil mineral carbon stocks were unaffected by woody plant cover and density. Aboveground carbon stocks were highly dependent on PJ vegetation density. At a 300 m2 plot-scale, plots with low and high density of PJ forest had 1.40 kg C m-2 and 3.69 kg m-2 more carbon than the un-encroached plot. To examine how these 300 m2 variations influence landscape scale C estimates, historical and contemporary aerial photos were analyzed to develop forest density maps in order to estimate above ground PJ associated C stock changes in a 25 ha area. This technique yielded an average estimate of 1.43 kg m-2 of C accumulation with PJ encroachment. Combining this estimate with analysis of tree growth increments from dendrochronologies, we estimate that these PJ stands are accumulating aboveground C at an annual rate of 0.02 kg C m-2 with no slowing of this rate in healthy PJ. This result is in contrast to what has been observed in large areas of drought related PJ mortality, where C accumulation has ceased.

CONCLUSIONS

These results illustrate that the encroachment of PJ forests in SE Utah over the last century has resulted in a large (and ongoing) accumulation of carbon in PJ trees and surface litter. However, the magnitude of the increase depends to on the density of vegetation across the landscape and the health of forest stands. Both management activities that remove forest carbon and forest mortality due to drought or wildfire have the potential to quickly reverse the multi-decadal accumulation of carbon in these stands.

Environmental filtering and land-use history drive patterns in biomass accumulation in a mediterranean-type landscape

Aboveground biomass (AGB) reflects multiple and often undetermined ecological and land-use processes, yet detailed landscape-level studies of AGB are uncommon due to the difficulty in making consistent measurements at ecologically relevant scales. Working in a protected mediterranean-type landscape (Jasper Ridge Biological Preserve, California, USA), we combined field measurements with remotely sensed data from the Carnegie Airborne Observatory's light detection and ranging (lidar) system to create a detailed AGB map. We then developed a predictive model using a maximum of 56 explanatory variables derived from geologic and historic-ownership maps, a digital elevation model, and geographic coordinates to evaluate possible controls over currently observed AGB patterns. We tested both ordinary least-squares regression (OLS) and autoregressive approaches. OLS explained 44% of the variation in AGB, and simultaneous autoregression with a 100-m neighborhood improved the fit to an r2 = 0.72, while reducing the number of significant predictor variables from 27 variables in the OLS model to 11 variables in the autoregressive model. We also compared the results from these approaches to a more typical field-derived data set; we randomly sampled 5% of the data 1000 times and used the same OLS approach each time. Environmental filters including incident solar radiation, substrate type, and topographic position were significant predictors of AGB in all models. Past ownership was a minor but significant predictor, despite the long history of conservation at the site. The weak predictive power of these environmental variables, and the significant improvement when spatial autocorrelation was incorporated, highlight the importance of land-use history, disturbance regime, and population dynamics as controllers of AGB.

Applications of remote sensing to alien invasive plant studies

Biological invasions can affect ecosystems across a wide spectrum of bioclimatic conditions. Therefore, it is often important to systematically monitor the spread of species over a broad region. Remote sensing has been an important tool for large-scale ecological studies in the past three decades, but it was not commonly used to study alien invasive plants until the mid 1990s. We synthesize previous research efforts on remote sensing of invasive plants from spatial, temporal and spectral perspectives. We also highlight a recently developed state-of-the-art image fusion technique that integrates passive and active energies concurrently collected by an imaging spectrometer and a scanning-waveform light detection and ranging (LiDAR) system, respectively. This approach provides a means to detect the structure and functional properties of invasive plants of different canopy levels. Finally, we summarize regional studies of biological invasions using remote sensing, discuss the limitations of remote sensing approaches, and highlight current research needs and future directions.