A new Global Agro-Environmental Stratification (GAES)

Mücher, Sander; Simone, Lorenzo De; Kramer, H.; Wit, Allard de; Roupioz, Laure; Hazeu, G.W.; Boogaard, H.L.; Schuiling, Rini; Fritz, Steffen; Latham, J.; Cormont, A.

doi:10.18174/400815

Cited by 7 publications

(5 citation statements)

References 28 publications

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“…For a better parameterisation of the relationship between biomass and cNDVI, different stratifications with a more biophysical rationale were tested. First, we used the Global Agro-Environmental Stratification (GAES [39]). The GAES is derived from the segmentation of 13 input layers with 1 km spatial resolution, aiming to stratify agricultural production zones according to the region's agro-environmental characteristics, including climate, altitude, irrigation, production, phenology, growing cycles, crop type, and field size parameters.…”

Section: Linear Regression Model and Spatial Aggregation Levelsmentioning

confidence: 99%

Phenology-Based Biomass Estimation to Support Rangeland Management in Semi-Arid Environments

et al. 2017

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Livestock plays an important economic role in Niger, especially in the semi-arid regions, while being highly vulnerable as a result of the large inter-annual variability of precipitation and, hence, rangeland production. This study aims to support effective rangeland management by developing an approach for mapping rangeland biomass production. The observed spatiotemporal variability of biomass production is utilised to build a model based on ground and remote sensing data for the period 2001 to 2015. Once established, the model can also be used to estimate herbaceous biomass for the current year at the end of the season without the need for new ground data. The phenology-based seasonal cumulative Normalised Difference Vegetation Index (cNDVI), computed from 10-day image composites of the Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI data, was used as proxy for biomass production. A linear regression model was fitted with multi-annual field measurements of herbaceous biomass at the end of the growing season. In addition to a general model utilising all available sites for calibration, different aggregation schemes (i.e., grouping of sites into calibration units) of the study area with a varying number of calibration units and different biophysical meaning were tested. The sampling sites belonging to a specific calibration unit of a selected scheme were aggregated to compute the regression. The different aggregation schemes were evaluated with respect to their predictive power. The results gathered at the different aggregation levels were subjected to cross-validation (cv), applying a jackknife technique (leaving out one year at a time). In general, the model performance increased with increasing model parameterization, indicating the importance of additional unobserved and spatially heterogeneous agro-ecological effects (which might relate to grazing, species composition, optical soil properties, etc.) in modifying the relationship between cNDVI and herbaceous biomass at the end of the season. The biophysical aggregation scheme, the calibration units for which were derived from an unsupervised ISODATA classification utilising 10-day NDVI images taken between January 2001 and December 2015, showed the best performance in respect to the predictive power (R 2 cv = 0.47) and the cross-validated root-mean-square error (398 kg·ha −1 ) values, although it was not the model with the highest number of calibration units. The proposed approach can be applied for the timely production of maps of estimated biomass at the end of the growing season before field measurements are made available. These maps can be used for the improved management of rangeland resources, for decisions on fire prevention and aid allocation, and for the planning of more in-depth field missions.

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Section: Linear Regression Model and Spatial Aggregation Levelsmentioning

confidence: 99%

Phenology-Based Biomass Estimation to Support Rangeland Management in Semi-Arid Environments

et al. 2017

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“…The map has many other application possibilities. It can be used, for example when planning suitable target areas for agricultural and forestry schemes, which will have a major impact on the success of the region's food security and sustainable forest management (Mücher et al, 2016;Milder et al, 2013;Nijbroek & Andelman 2015;Williams et al, 2008). The biophysical map is a major improvement, considering that at present, the incentive schemes rely on limited knowledge, like rainfall (PFP, 2016) or general environmental classifications (De Pauw, 1984).…”

Section: Discussionmentioning

confidence: 99%

“…During the past decade, open-access geospatial datasets of high spatial accuracy and with global coverage have become widely available, allowing mapping and modeling of environmental factors at rather detailed scales in most parts of the world (Metzger et al, 2013b;Mücher et al, 2016;Sayre et al, 2014). Furthermore, environmental satellite image repositories with abundant and up-to-date image data and access to increased computing power have created new opportunities for the creation of biophysical mapping based on quantitative and repeatable methods (Coops et al, 2009;Song et al, 2017;Xiong et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Biophysical regions of the Southern Highlands, Tanzania: regionalization in a data scarce environment with open geospatial data and statistical methods

2020

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Spatially explicit, evidence-based and regionally contextualized data on biophysical landscape characteristics is an essential basis for regionally sustainable landscape management schemes. In many regions of the Global South, the availability of such information is poor, especially at the subnational level, and the spatial management is often based on generic and outdated information, leading to severe threats for land sustainability. We have developed a biophysical regionalization of the Southern Highlands area of Tanzania. The map is based on open-source global datasets depicting climate, soil, topography and vegetation. Through replicable statistical and geospatial analyses, we have identified 7 regions and 18 subsections with biophysically similar and spatially distinctive environmental conditions. The regions provide spatially contextualized support for understanding and managing the landscapes of the Southern Highlands. The applications for such data sets are numerous, from screening suitability areas for e.g. afforestation schemes to evaluating the distinctiveness and vulnerability of landscapes to degradation.

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“…In this study, the Global Agro-Environmental Stratification (GAES) dataset was used as units of assessing the accuracy of individual cropland layers for each agro-environmental zone (AEZ). The dataset provides four levels of agro-environmental stratification based on the region's climatic regimes, soil, terrain, elevation conditions, water availability, and land cover proprieties (Mücher et al, 2016). Level 3 was appropriate with the distribution of reference samples set built in the former section.…”

Section: Agro-environmental Stratificationmentioning

confidence: 99%

Constructing a 30m African Cropland Layer for 2016 by Integrating Multiple Remote sensing, crowdsourced, and Auxiliary Datasets

Nabil

Zhang

et al. 2021

Big Earth Data

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Despite its essential importance to various spatial agriculture and environmental applications, the information on actual cropland area and its geographical distribution remain highly uncertain over Africa among remote-sensing products. Each of the African regions has its unique physical and environmental limiting factors to accurate cropland mapping, which leads to high spatial discrepancies among remote sensing cropland products. Since no dataset could cope with all limitations, multiple datasets initially derived from various remote sensing sensors and classification techniques must be integrated into a more accurate cropland product than individual layers. Here, in the current study, four cropland products, produced initially from multiple sensors (e.g. Landsat-8 OLI, Sentinel-2 MSI, and PROBA-V) to cover the period (2015-2017), were integrated based on their cropland mapping accuracy to build a more accurate cropland layer. The four cropland layers' accuracy was assessed at Agro-ecological zones units via an intensive reference dataset (17,592 samples). The most accurate cropland layer was then identified for each zone to construct the final cropland mask at 30 m resolution for the nominal year of 2016 over Africa. As a result, the new layer was produced in higher cropland mapping accuracy (overall accuracy = 91.64% and cropland's F-score = 0.75). The layer mapped the African cropland area as 282 Mha (9.38% of the Continent area). Compared to earlier cropland synergy layers, the constructed cropland mask showed a considerable improvement in its spatial resolution (30 m instead of 250 m), mapping quality, and closeness to official statistics (R 2 = 0.853 and RMSE = 2.85 Mha). The final layer can be downloaded as described under the "Data Availability Statement" section.

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A new Global Agro-Environmental Stratification (GAES)

Cited by 7 publications

References 28 publications

Phenology-Based Biomass Estimation to Support Rangeland Management in Semi-Arid Environments

Phenology-Based Biomass Estimation to Support Rangeland Management in Semi-Arid Environments

Biophysical regions of the Southern Highlands, Tanzania: regionalization in a data scarce environment with open geospatial data and statistical methods

Constructing a 30m African Cropland Layer for 2016 by Integrating Multiple Remote sensing, crowdsourced, and Auxiliary Datasets

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