Since the first documented soil survey in Tanzania by Milne (J Ecol 35:192-265, 1936), a number of other soil inventory exercises at different scales have been made. The main challenge has been the fragmented nature of the often outdated detailed soil maps and small-scale less-informative country-wide soil maps. Recent advances in information and computational technology have created vast potential to collect, map, harness, communicate and update soil information. These advances present favorable conditions to support the already popular shift from qualitative (conventional) to quantitative (digital) soil mapping (DSM). In this study, two decision tree machine learning algorithms, J48 and Random Forest (RF), were applied to digitally predict k-means numerically classified soil clusters to update a soil map produced in 1959. Predictors were derived from 1 arc SRTM digital elevation data and a 5 m RapidEye satellite image. J48 and RF predicted the soil units of the legacy maps with greater detail. However, RF showed superiority for predicting clusters J48 could not predict and for showing higher pixel contiguity. No significant difference (P = 0.05) was observed between the soil properties of the predicted soil clusters and the actual field validation points. Young soils (Entisols and Inceptisols) were found to occupy about 56 % of the study site’s 30,000 ha followed by Alfisols, Mollisols and Vertisols at 31, 9 and 4 %, respectively. This study demonstrates the usefulness of DSM techniques to update conventionally prepared legacy maps to offer soil information at improved detail to agricultural land use planners and decision makers of Tanzania to make evidence-based decisions for climate-resilient agriculture and other land uses. © Springer International Publishing AG 2016.
Tag: machine learning
Optimising carbon fixation through agroforestry: Estimation of aboveground biomass using multi-sensor data synergy and machine learning
As agricultural land expansion is the primary driver of deforestation, agroforestry could be an optimal land use strategy for climate change mitigation and reducing pressure on forests. Agroforestry is a promising method for carbon sequestration. With recent advancements in geospatial and data science technology, the ability to predict aboveground biomass (AGB) and assess ecosystem services in agroforestry is rapidly expanding. This study was conducted in the Belpada Block of Balangir, Odisha, a forest-dominated region of eastern India. We recorded species occurrence and measured plant parameters, including Circumference at Breast Height (CBH), height, and geolocation, in 196 plots (0.09 ha) in agroforestry intervention sites and noted the tree species. This study used Sentinel-1 and Sentinel-2 multi sensor data to achieve data synergy in AGB estimation. Three machine learning models were used: Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN). The RF model exhibited the highest level of prediction accuracy (R2 = 0.69 and RMSE = 17.07 Mg/ha), followed by the ANN model (R2 = 0.63 and RMSE = 19.35 Mg/ha), SVM model (R2 = 0.54, RMSE = 21.97 Mg/ha. The spectral vegetation indices that are (Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), Modified Simple Ratio (MSR), Modified Soil-Adjusted Vegetation Index (MSAVI), Difference Vegetation Index (DVI), and SAR backscatter values, were found important variables for AGB prediction. The findings revealed that agroforestry interventions and plantations resulted in an average carbon stock increase of 15 Mg/ha over five years in the study area. The Plant Value Index (PVI), which indicates the importance of species in the local economy and biomass carbon storage, showed that Tectona grandis was the dominant species with the highest PVI value (88.35), followed by Eucalyptus globulus (56.87), Mangifera indica (53.75), and Azadirachta indica (15.45). This approach enables the expansion of monitoring efforts to assess carbon stock in agroforestry systems, thereby promoting effective management strategies.
Categorizing the songbird market through big data and machine learning in the context of Indonesia’s online market
The songbird trade has been identified as a major threat to wild populations, and the bird market has now expanded to online platforms. The study explored the use of machine learning models as a monitoring framework; developed models for taxa identification; applied the best model to understand the current market situation (taxa composition, asking price, and location); and conducted a survey to understand the profile of sellers. The authors found that the machine learning models produced a high level of accuracy in distinguishing relevant ads and identified the songbirds’ taxa. The Support Vector Machine (SVM) was selected as the best model and was used to predict the ad population. The model identified 284,118 songbirds from 247 taxa that were listed online from April 2020 to September 2021. The authors also found that 6.2% of ads listed threatened taxa based on the IUCN Red List. The survey results suggested that songbird sellers are mostly hobbyists or breeders looking for extra income from selling birds. As current studies of the songbird market are mostly conducted offline in the bird markets, transactions by non-bird traders or among hobbyists in the online market are remain underreported. Therefore, monitoring needs to be extended to the online market and to our knowledge, currently there is no applied system or platform is identified for monitoring online songbird market. The result from this study can help fill this gap. Information from the monitoring of the songbird online market in this study may assist stakeholders in formulating corrective action based on the current market situation.
Big data analysis to understanding online songbird trade in Indonesia: What are the most traded species?
Under the GCRF Trade, Development and the Environment Hub Project (TRADE Hub), the CIFOR research team is studying the wildlife trade in Indonesia with a focus on songbirds. The study is exploring the use of big data analysis and machine learning to monitor songbird trade in the online marketplace.