Enhancing Accuracy in Historical Forest Vegetation Mapping in Yunnan with Phenological Features, and Climatic and Elevation Variables

Human activities have both positive and negative impacts on forests, altering the extent and composition of various forest vegetation types, and increasing uncertainty in ecological management. A detailed understanding of the historical distribution of forest vegetation is crucial for local conservation efforts. In this study, we integrated phenological features with climatic and terrain variables to enhance the mapping accuracy of forest vegetation in Yunnan. We mapped the historical distributions of five forest vegetation type groups and nine specific forest vegetation types for 2001, 2010, and 2020. Our findings revealed that: (1) rubber plantations can be effectively distinguished from other forest vegetation using phenological features, coniferous forests and broad-leaved forests can be differentiated using visible spectral bands, and environmental variables (temperature, precipitation, and elevation) are effective in differentiating forest vegetation types under varying climate conditions; (2) the overall accuracy and kappa coefficient increased by 14.845% and 20.432%, respectively, when climatic variables were combined with phenological features, and by 13.613% and 18.902%, respectively, when elevation was combined with phenological features, compared to using phenological features alone; (3) forest cover in Yunnan increased by 2.069 × 104 km2 (10.369%) between 2001 and 2020. This study highlights the critical role of environmental variables in improving the mapping accuracy of forest vegetation in mountainous regions.

Technical guidelines for participatory village mapping exercise

This document is meant for researchers, field research supervisors and enumerators who would like guidance on developing maps with the participation of local communities. It is part of the Global Comparative Study on REDD+ (GCS REDD+) conducted by CIFOR with funding from multiple donors.
Participatory mapping represents a way of documenting land use and tenure arrangements across regions. It is also a way to learn about local perceptions of the landscape, and local people’s perspectives of forests and land management. These technical guidelines are based on CIFOR’s long-term experience with the use of participatory mapping in research.
In GCS REDD+, the participatory mapping exercise is done at the village level during focus group discussions to learn about village boundaries, tenure, areas under dispute, access, and markets. Base maps are developed from satellite images prior to going to the field. Final maps are digitised so that areas corresponding to the different land uses, land cover, and land tenure categories can be extracted for subsequent analyses. The participatory maps are used to help develop a common understanding of a territory with villagers and other local stakeholders.

Mapping the information landscape of the United Nations Decade on Ecosystem Restoration Strategy

The strategy of the United Nations Decade on Ecosystem Restoration identifies three pathways for action for overcoming six global barriers thought to hamper upscaling. We evaluated 6,023 peer-reviewed and gray literature papers published over the last two decades to map the information landscape underlying the barriers and associated pathways for action across world regions, terrestrial ecosystem types, restorative interventions and their outcomes. Overall, the literature addressed more the financial and legislative barriers than the technical and research-related ones, supporting the view that social, economic and political factors hamper scaling up ecosystem restoration. Latin America, Africa, and North America were the most prominent regions in the literature, yet differed in the number of publications addressing each barrier. An overwhelming number of publications focused on forests (78%), while grasslands (6%), drylands (3%), and mangroves (2%) received less attention. Across the three pathways for action, the action lines on (1) promoting long-term ecosystem restoration actions and monitoring and (2) education on restoration were the most underrepresented in the literature. In general, restorative interventions assessed rendered positive outcomes except those of a political, legislative or financial nature which reported negative or inconclusive outcomes. Our indicative assessment reveals critical information gaps on barriers, pathways, and types of restorative interventions across world regions, particularly related to specific social issues such as education for ecosystem restoration. Finally, we call for refining “strength of evidence” assessment frameworks that can systematically appraise, synthesize and integrate information on traditional and practitioner knowledge as two essential components for improving decision-making in ecosystem restoration.

Towards spatially continuous mapping of soil organic carbon in croplands using multitemporal Sentinel-2 remote sensing

Intensified human activities can augment soil organic carbon (SOC) losses from the world’s croplands, making SOC a highly dynamic parameter both in space and time. Sentinel-2 spectral imagery is well placed to capture the spatiotemporal variability of SOC, but its capability has only been demonstrated for agricultural regions mostly located in Europe. Furthermore, most studies so far only used single-date images that resulted in spatially non-continuous SOC maps, hampering their ability to aid multiscale SOC assessments. Here, we aim to achieve spatially continuous mapping of SOC in croplands, by creating multitemporal bare soil composites that maximize cropland coverage in two regions of varying agroecosystems and landscape structure in the Northeast China Chernozem region and the Belgian Loam Belt. Bare soil pixels were extracted via spectral index thresholding that excluded contaminated pixels from external perturbance. Multitemporal soil composites were then obtained by averaging over multiple single-date bare soil images that were selected within pre-determined optimal time-windows, corresponding to the region-specific crop sowing periods when best possible surface conditions were expected. Results show that the optimal time-window filter ensured selective inclusion of single-date images that themselves yielded stable and robust SOC predictions across multiple years. Spectral-based models developed from multitemporal composites consistently produced better or similar prediction accuracies than single-date images for both study regions (R2: 0.52–0.62; RMSE: 0.17–0.21 g 100 g−1), while also achieved maximum cropland coverage (>82 %). Bootstrap modelling demonstrated that SOC mapping via multitemporal Sentinel-2 data was associated with small uncertainties. Investigations into the significant spectral bands that contributed to the prediction of SOC suggested that, regardless of the study regions, the physical relationship between spectral bands and SOC that predominantly exists for laboratory spectra is largely translated into Sentinel-2 platforms. This study highlights the widespread applicability of multitemporal Sentinel-2 remote sensing for effective and high-resolution SOC mapping, in order to detect localized soil degradation as well as to inform regional cropland management in diverse agroecosystems.

Comparing the prediction performance, uncertainty quantification and extrapolation potential of regression kriging and random forest while accounting for soil measurement errors

Geostatistics and machine learning have been extensively applied for modelling and predicting the spatial distribution of continuous soil variables. In addition to providing predictions, both techniques quantify the uncertainty associated with the predictions, although geostatistics is more developed in this respect. Despite the increased use of these techniques, most algorithms ignore that the soil measurements are not error-free. Recently, concern has also arisen about the extrapolation risk of these techniques, be it in geographic space, feature space, or both. In this paper, regression kriging (RK) and random forest (RF) were compared with respect to their ability to deliver accurate predictions and quantify prediction uncertainties, while accounting for measurement errors in the soil data. The sensitivity of results of both models to soil measurement errors was also evaluated, as well as their spatial extrapolation potential. This was done for a case study in Cameroon where soil pH, clay and organic carbon were mapped from measurements obtained using both conventional and proximal soil sensing methods. The results showed that both models produced comparable ranges and maps of predicted values for the soil properties of interest. Compared to RF, RK outperformed RF by presenting generally a higher Model Efficiency Coefficient (MEC), lower Root Mean Squared Error (RMSE) values and better extrapolation performance. The improvement in RMSE was about 10, 12 and 2 % while the improvement in MEC was on average 5, 22 and 1 % for pH, clay and SOC, respectively Overestimation of the local uncertainty observed for RK was larger than that of RF as shown by accuracy plots, indicating that prediction uncertainties were better quantified by the RF model. Better extrapolation performance was obtained with RK that derived better predictions than RF at unsampled locations as shown by cross-validation metrics and scatter plots, particularly when RK and RF were used for spatial extrapolation. The effects of incorporating measurement errors were not significant both for the predictions and for the prediction uncertainties due to the fact that most calibration data had the same measurement error variance. Model comparison should go beyond common validation metrics that only evaluate prediction accuracy but must also account for the ability to quantify prediction uncertainty at unsampled locations.

Participatory mapping of ecosystem services across a gradient of agricultural intensification in West Kalimantan, Indonesia

Agrarian change affects the supply and demand of ecosystem services (ES) by reducing the extent of natural ecosystems. Agricultural intensification can lead to changes in land covers and livelihood opportunities and it remains unclear how such changes align or misalign with the desires of local communities. Using participatry mapping, we assessed ES uses and desires of Indigenous people and local communities provided by different land cover types along a gradient of agricultural intensification (forest subsistence, agroforestry mosaic, and monoculture and market-dependence) in West Kalimantan, Indonesia. We found that mapped ES use diversity was highest in the forest-dependent zone and lowest near monoculture agricultural systems. The expressed ES uses and desires varied greatly among land cover types amidst loss of old-growth forest and greater reliance on secondary forest and shrub land. The spatial analysis showed that high priority areas of ES use was related to access in the landscape, demonstrating the importance of attending to place-based social values in ES assessments. From this study, we call for a people-centric spatial modelling approach to address the divergence of social and cultural ES values associated with land covers under different intensification contexts. Participatory mapping clarifies the ES desires of local communities, which state policy often fails to address. We recommend a place specific management strategy to reduce ES trade-offs of specific land use practices, which are currently apparent with agrarian change in Indonesia and relevant for other tropical developing countries.

Estimation of Aboveground Biomass in Agroforestry Systems over Three Climatic Regions in West Africa Using Sentinel-1, Sentinel-2, ALOS, and GEDI Data

Agroforestry systems (AFS) offer viable solutions for climate change because of the aboveground biomass (AGB) that is maintained by the tree component. Therefore, spatially explicit estimation of their AGB is crucial for reporting emission reduction efforts, which can be enabled using remote sensing (RS) data and methods. However, multiple factors including the spatial distributions within the AFS, their structure, their composition, and their variable extents hinder an accurate RS-assisted estimation of the AGB across AFS. The aim of this study is to (i) evaluate the potential of spaceborne optical, SAR and LiDAR data for AGB estimations in AFS and (ii) estimate the AGB of different AFS in various climatic regions. The study was carried out in three climatic regions covering Côte d’Ivoire and Burkina Faso. Two AGB reference data sources were assessed: (i) AGB estimations derived from field measurements using allometric equations and (ii) AGB predictions from the GEDI level 4A (L4A) product. Vegetation indices and texture parameters were generated from optical (Sentinel-2) and SAR data (Sentinel-1 and ALOS-2) respectively and were used as predictors. Machine learning regression models were trained and evaluated by means of the coefficient of determination (R2) and the RMSE. It was found that the prediction error was reduced by 31.2% after the stratification based on the climatic conditions. For the AGB prediction, the combination of random forest algorithm and Sentinel-1 and -2 data returned the best score. The GEDI L4A product was applicable only in the Guineo-Congolian region, but the prediction error was approx. nine times higher than the ground truth. Moreover, the AGB level varied across AFS including cocoa (7.51 ± 0.6 Mg ha−1) and rubber (7.33 ± 0.33 Mg ha−1) in the Guineo-Congolian region, cashew (13.78 ± 0.98 Mg ha−1) and mango (12.82 ± 0.65 Mg ha−1) in the Guinean region. The AFS farms in the Sudanian region showed the highest AGB level (6.59 to 82.11 Mg ha−1). AGB in an AFS was mainly determined by the diameter (R2 = 0.45), the height (R2 = 0.13) and the tree density (R2 = 0.10). Nevertheless, RS-based estimation of AGB remain challenging because of the spectral similarities between AFS. Therefore, spatial assessment of the prediction uncertainties should complement AGB maps in AFS.

Stakeholder Mapping in the Andhra Pradesh Engagement Landscape

The stakeholder mapping survey was conducted between December 2021 and March 2022 in the Exemplar Landscapes of Ananthapuramu, West Godavari and Alluri Sitharama Raju (ASR) to understand the stakeholders working on natural farming or restoration in the landscape including how they relate to each other.

How to: design for context. Getting clear on geography, power, and governance

So you’ve mapped existing and past experiences with MSPs in your landscape, and decided that you want to implement one. Now it’s time to home in on context – the resources, actors, governance arrangements, power structures and relationships, and conflicts that exist within, or affect, the landscape in question.

Modeling the Spatial Distribution of Soil Organic Carbon and Carbon Stocks in the Casanare Flooded Savannas of the Colombian Llanos

Flooded savannas are valuable and extensive ecosystems in South America, but not widely studied. In this study, we quantify the spatial distribution of soil organic carbon (SOC) content and stocks in the Casanare flooded savannas. We sampled 80 sites at two soil-depth intervals (0-10 and 10-30 cm), where SOC values ranged from 0.41% in the surface and 0.23% in the sub-surface of drier soils to over 14.50% and 7.51%, in soils that experienced seasonal flooding. Spatial predictions of SOC were done through two digital soil mapping (DSM) approaches: Expert-Knowledge (EK) and Random-Forest (RF). Although both approaches performed well, EK was slightly superior at predicting SOC. Covariates derived from vegetation cover, topography, and soil properties were identified as key drivers in controlling its distribution. Total SOC stocks were 55.07 Mt with a mean density of 83.1±24.3 t·ha-1 in the first 30 cm of soil, with 12.3% of this located in areas that experience long periods of flooding (semi-seasonal savannas) , which represented only 7.9% of the study area (664,752 ha). Although the study area represents only 15% of the total area of the Casanare department, the intensive pressure of human development could result in the reduction of its SOC stocks and the release of important amounts of greenhouse gases into the atmosphere. At regional level, the impact of a large-scale land use conversions of the flooded Llanos del Orinoco ecosystem area (15 Mha) could transform this area in a future source of important global emissions if correct decisions are not taken regarding the land management of the region.

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