Comparison of functional and structural biodiversity using Sentinel-2 and airborne LiDAR data in agroforestry systems

Biodiversity plays a critical role in maintaining the health and stability of ecosystems. Biodiversity monitoring has traditionally been labor-intensive, prompting a shift towards remote sensing techniques for efficient and large-scale approaches. In this research, we explore the use of Sentinel-2 satellite data and airborne LiDAR data to evaluate and compare functional and structural biodiversity in agroforestry areas within two distinct ecoregions, namely the Montane forests ecoregion and the Victoria Basin forest-savanna mosaic ecoregion in Columbia and Tanzania, respectively. The aim of the study is to compare functional diversity and structural diversity across varying spatial scales and land cover types including trees, cropland and grassland, thereby addressing the correlation and divergence between functional and structural diversity in different ecological contexts. Our methodology involves integrating airborne LiDAR data to assess structural diversity and Sentinel-2 data to estimate functional diversity based on the proxies of three key functional traits, leaf chlorophyll content (CHL), leaf anthocyanin content (ANTH), and specific leaf area (SLA). We developed two novel functional diversity indices, ShannonF and GiniF, which are modified versions of the well-established Shannon index and Gini index. These novel indices effectively incorporate both functional richness and evenness into their calculations. Our results indicated a significant correlation between our proposed ShannonF index and Shannon index derived from LiDAR, with stronger correlations at larger spatial scales. This study demonstrated that trees exhibit higher biodiversity than grassland and cropland across both study areas, with particularly high biodiversity in Colombia’s Montane forests ecoregion. These findings underscore the potential of integrating satellite and airborne LiDAR data for comprehensive biodiversity assessment in agroforestry systems, offering valuable insights for global ecosystem management and conservation efforts

Conceptualizing Distal Drivers in Land Use Competition

This introductory chapter explores the notion of ‘distal drivers’ in land use competition. Research has moved beyond proximate causes of land cover and land use change to focus on the underlying drivers of these dynamics. We discuss the framework of telecoupling within human–environment systems as a first step to come to terms with the increasingly distal nature of driving forces behind land use practices. We then expand the notion of distal as mainly a measure of Euclidian space to include temporal, social, and institutional dimensions. This understanding of distal widens our analytical scope for the analysis of land use competition as a distributed process to consider the role of knowledge and power, technology, and different temporalities within a relational or systemic analysis of practices of land use competition. We conclude by pointing toward the historical and social contingency of land use competition and by acknowledging that this contingency requires a methodological–analytical approach to dynamics that goes beyond linear cause–effect relationships. A critical component of future research will be a better understanding of different types of feedback processes reaching from biophysical feedback loops to feedback produced by individual or institutional reflexivity

Analysis of a Landscape Intensely Modified by Agriculture in the Tietê–Jacaré Watershed, Brazil

From MDPI via Jisc Publications RouterHistory: accepted 2021-08-04, pub-electronic 2021-08-19Publication status: PublishedFunder: São Paulo Research Support Foundation; Grant(s): 2015/19918-3, 2018/00162-4, 660020, PR140015Anthropogenic actions influence landscapes, and the resulting mosaic is a mix of natural and anthropogenic elements that vary in size, shape, and pattern. Considering this, our study aimed to analyse the land use and land cover changes in the Tietê–Jacaré watershed (São Paulo state, Brazil), using the random forest (RF) algorithm and Sentinel-2 satellite data from 2016 to 2018 to detect landscape changes. By overlapping the environmental data and the proposed model evaluation, it was possible to observe the landscape structure, produce information about the state of this region, and assess the environmental responses to anthropic impacts. The land use and land cover analysis identified eight classes: exposed soil, citriculture, pasture, silviculture, sugar cane, urban area, vegetation, and water. The RF classification for the three years reached high accuracy with a kappa index of 0.87 in 2016, 0.85 in 2017, and 0.85 in 2018. The model developed was essential for the temporal analysis since it allowed us to comprehend the driving forces that act in this landscape and contribute to the discussions about their impacts over time. The results showed a predominance of agricultural activities over the three years, with approximately 900.000 ha (76% of the area), mainly covered by sugarcane cultivation

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

Near real-time change detection system using Sentinel-2 and machine learning: a test for Mexican and Colombian forests

The commitment by over 100 governments covering over 90% of the world’s forests at the COP26 in Glasgow to end deforestation by 2030 requires more effective forest monitoring systems. The near real-time (NRT) change detection of forest cover loss enables forest landowners, government agencies and local communities to monitor natural and anthropogenic disturbances in a much timelier fashion than the thematic maps that are released every year. NRT deforestation alerts enable the establishment of more up-to-date forest inventories and rapid responses to unlicensed logging. The Copernicus Sentinel-2 satellites provide operational Earth observation (EO) data from multi-spectral optical/near-infrared wavelengths every five days at a global scale and at 10 m resolution. The amount of acquired data requires cloud computing or high-performance computing for ongoing monitoring systems and an automated system for processing, analyzing and delivering the information promptly. Here, we present a Sentinel-2-based NRT change detection system, assess its performance over two study sites, Manantlán in Mexico and Cartagena del Chairá in Colombia, and evaluate the forest changes that occurred in 2018. An independent validation with very high-resolution PlanetScope (~3 m) and RapidEye (~5 m) data suggests that the proposed NRT change detection system can accurately detect forest cover loss (> 87%), other vegetation loss (> 76%) and other vegetation gain (> 71%). Furthermore, the proposed NRT change detection system is designed to be attuned using in situ data. Therefore, it is scalable to larger regions, entire countries and even continents

Analysing the spatial pattern of deforestation and degradation in miombo woodland: methodological issues and practical solutions

Although much emphasis has been given to the analysis of continuous forest conversion in tropical regions, our understanding in detecting, mapping and interpreting the spatial pattern of woodland deforestation and degradation is still limited. This thesis focuses on two factors contributing to this limitation: uncertainties in retrieving woodland change from remote sensing imagery, and the complex processes that may cause woodland deforestation and degradation. Firstly, I investigate approaches to minimising uncertainty in ALOS PALSAR-derived biomass maps by modifying a widely used processing chain, with the aim of provide recommendations for producing radar-based biomass maps with reduced uncertainty. Secondly, to further improve the retrieval of woody biomass from ALOS PALSAR imagery, the semi-empirical Water Cloud Model (WCM) is introduced to account for backscattering from soil. In wooded areas with low canopy (such as the miombo woodland which dominates the study area) the effect from soil moisture on the received backscattered signal is critical. Thirdly, based on the biomass maps retrieved from the refined radar-remote-sensing-based methodology discussed above, the influence of driving variables of the woodland deforestation and degradation, and how they alter the spatial patterns of these two processes, are analysed. The threshold for defining woodland deforestation and degradation in terms of biomass loss intensity is generated through integration of radar-based biomass loss maps, an optical forest cover change map and fieldwork investigation. Multi-linear model simulations of the spatial variation of deforestation and degradation events were constructed at a district and 1 km resolution respectively to rank the relative importance of driving variables. Results suggest that biomass-backscatter relationships based on plots of approximately 1 ha, and processed with high resolution DEMs, are needed for low uncertainty biomass maps using ALOS PALSAR data. Although plots sizes of 0.1 - 0.5 ha lead to large uncertainties, aggregating 0.1 ha plots into larger calibration sites shows some promise even in hilly terrain, potentially opening up the use of common forest inventory data to calibrate remote-sensing-based biomass retrieval models. Such relationships appear to hold across the miombo woodland ecoregion, which implies that there is a consistent relationship at least in the miombo woodland. From this I infer that random error, different processing methods and fitting techniques, and data from small plots are the source of the differences in the savanna biomass-backscatter relationships seen in the literature. The interpreted WCM presented in this study for L-band backscatter at HV polarisation improves biomass retrieval for areas with a biomass value less than 15 tC/ha (or 0.025 m2/m2 in backscatter). Use of the WCM also results in better quality regional biomass mosaics. This is because the WCM helped to improve the correlation of biomass estimation for overlay areas by reducing bias between adjacent paths, especially the bias introduced by changes in soil moisture conditions between different acquisition dates for different paths. Result shows that active and combined soil moisture datasets (from the Climate Change Initiative Soil Moisture Dataset) can be used as effective soil moisture proxies in the WCM for biomass retrieval. This quantitative analysis on the driving variables of woodland deforestation and degradation suggests that large uncertainty exists in modelling the occurrence of deforestation and degradation, especially at a 1 km scale. The spatial patterns of woodland deforestation and degradation differ in terms of shape, size, intensity, and location. Agriculture-related driving variables account for most of the explained variance in deforestation, whereas for degradation, distance to settlements also plays an important role. Deforestation happens regardless of the original biomass levels, while degradation is likely to happen at high biomass areas. The sizes of degradation events are significantly smaller than those of deforestation events, with 90% of deforestation events sharing boundaries with degradation events. This thesis concludes by outlining the importance and difficulties in integrating ‘distal’ (underlying) drivers in modelling the spatial dynamics of deforestation and degradation. Further work on the causal connection between deforestation and degradation is also needed. The processing chain and biomass retrieval models presented in this study could be used to support monitoring and analysis of biomass change elsewhere in the tropics, and should be compatible with data derived from ALOS-2 and the future SAOCOM and BIOMASS satellite missions

Large Area Aboveground Biomass and Carbon Stock Mapping in Woodlands in Mozambique with L-Band Radar: Improving Accuracy by Accounting for Soil Moisture Effects Using the Water Cloud Model

Soil moisture effects limit radar-based aboveground biomass carbon (AGBC) prediction accuracy as well as lead to stripes between adjacent paths in regional mosaics due to varying soil moisture conditions on different acquisition dates. In this study, we utilised the semi-empirical water cloud model (WCM) to account for backscattering from soil moisture in AGBC retrieval from L-band radar imagery in central Mozambique, where woodland ecosystems dominate. Cross-validation results suggest that (1) the standard WCM effectively accounts for soil moisture effects, especially for areas with AGBC ≤ 20 tC/ha, and (2) the standard WCM significantly improved the quality of regional AGBC mosaics by reducing the stripes between adjacent paths caused by the difference in soil moisture conditions between different acquisition dates. By applying the standard WCM, the difference in mean predicted AGBC for the tested path with the largest soil moisture difference was reduced by 18.6%. The WCM is a valuable tool for AGBC mapping by reducing prediction uncertainties and striping effects in regional mosaics, especially in low-biomass areas including African woodlands and other woodland and savanna regions. It is repeatable for recent L-band data including ALOS-2 PALSAR-2, and upcoming SAOCOM and NISAR data. View Full-Tex

Improving Forest Baseline Maps in Tropical Wetlands Using GEDI-Based Forest Height Information and Sentinel-1

Remote Sensing-based global Forest/Non-Forest (FNF) masks have shown large inaccuracies in tropical wetland areas. This limits their applications for deforestation monitoring and alerting in which they are used as a baseline for mapping new deforestation. In radar-based deforestation monitoring, for example, moisture dynamics in unmasked non-forest areas can lead to false detections. We combined a GEDI Forest Height product and Sentinel-1 radar data to improve FNF masks in wetland areas in Gabon using a Random Forest model. The GEDI Forest Height, together with texture metrics derived from Sentinel-1 mean backscatter values, were the most important contributors to the classification. Quantitatively, our mask outperformed existing global FNF masks by increasing the Producer’s Accuracy for the non-forest class by 14%. The GEDI Forest Height product by itself also showed high accuracies but contained Landsat artifacts. Qualitatively, our model was best able to cleanly uncover non-forest areas and mitigate the impact of Landsat artifacts in the GEDI Forest Height product. An advantage of the methodology presented here is that it can be adapted for different application needs by varying the probability threshold of the Random Forest output. This study stresses that, in any application of the suggested methodology, it is important to consider the UA/PA trade-off and the effect it has on the classification. The targeted improvements for wetland forest mapping presented in this paper can help raise the accuracy of tropical deforestation monitoring

Effects of Supplementary Kelp Feeding on the Growth, Gonad Yield, and Nutritional and Organoleptic Quality of Subadult Sea Urchin (Strongylocentrotus intermedius) with Soya Lecithin Intake History

A 23-week feeding experiment was conducted to investigate the effects of supplementary kelp feeding on the growth, gonad development, and nutritional and sensory properties of sea urchin (Strongylocentrotus intermedius) with soya lecithin (SL) intake history. The feeding experiment was divided into experimental phase I and phase II. During phase I, 48 subadult sea urchins (initial weight: 6.28 ± 0.07 g) were fed one of the feeds with different levels of SL (0%, 1.6%, 3.2%) or kelp (Saccharina japonica) for 12 weeks. Then, all sea urchins were fed kelp for the next 11 weeks during the phase II. Each diet was randomly allocated to six cages of sea urchins. The results of phase I showed that weight gain rate (WGR), gonadosomatic index (GSI), gonad sensory properties (color and texture), and essential amino acid (EAA) contents were not significantly affected by SL level in the feed groups. High level (3.2%) of SL suppressed gonad development of S. intermedius with retarded gametogenesis in the 3.2% SL group (stage Ⅱ) compared to those fed 0% and 1.6% SL groups (stage Ⅲ). Sea urchins fed dry feeds exhibited significantly lower WGR and values of color (redness and yellowness) and texture (hardness and gumminess) but higher contents of EAA in the gonads than those fed kelp. The n-3/n-6 polyunsaturated fatty acid (PUFA) and eicosapentaenoic acid (EPA) of gonads in the groups fed with dry feeds showed no significant differences, but were significantly lower than that of kelp group. At the end of phase II, the gonad yellowness and EPA content of gonads in all dry feed groups were significantly increased by supplementary kelp feeding, with a higher increase observed in S. intermedius with SL intake history, while arachidonic acid (ARA) content was significantly improved by supplementary kelp feeding in S. intermedius with SL intake history. Gonad texture was improved to some extent by supplementary kelp feeding. These results indicated that S. intermedius fed dry feeds showed significantly higher GSI and EAA but poorer organoleptic quality and lower n-3/n-6 PUFA and EPA than those fed kelp. Kelp supplementary feeding improved the fatty acid value and organoleptic quality of gonads, especially for the sea urchins with SL intake history

Sentinel-1 SAR Backscatter Analysis Ready Data Preparation in Google Earth Engine

Sentinel-1 satellites provide temporally dense and high spatial resolution synthetic aperture radar (SAR) imagery. The open data policy and global coverage of Sentinel-1 make it a valuable data source for a wide range of SAR-based applications. In this regard, the Google Earth Engine is a key platform for large area analysis with preprocessed Sentinel-1 backscatter images available within a few days after acquisition. To preserve the information content and user freedom, some preprocessing steps (e.g., speckle filtering) are not applied on the ingested Sentinel-1 imagery as they can vary by application. In this technical note, we present a framework for preparing Sentinel-1 SAR backscatter Analysis-Ready-Data in the Google Earth Engine that combines existing and new Google Earth Engine implementations for additional border noise correction, speckle filtering and radiometric terrain normalization. The proposed framework can be used to generate Sentinel-1 Analysis-Ready-Data suitable for a wide range of land and inland water applications. The Analysis Ready Data preparation framework is implemented in the Google Earth Engine JavaScript and Python APIs. View Full-Tex