Satellite-based tracking of agricultural adaptation progress

Lack of systematic tools and approaches for measuring climate change adaptation limits the measurement of progress toward the adaptation goals of the Paris Agreement. To this end, we piloted a new approach, the Biomass Climate Adaptation Index (Biomass CAI), for measuring agricultural adaptation progress in Ethiopia across multiple scales using satellite remote sensing data. The Biomass CAI can monitor agri-biomass productivity associated with adaptation interventions remotely and facilitate more tailored precision adaptation. The Biomass CAI focuses on decision-support for end-users to ensure that the most effective climate change adaptation investments and interventions can be made in agricultural and food systems

Earth observation, open data and machine learning for near real time threat monitoring of vulnerable plant species

The IUCN data base lists several plant species whose existence is currently threatened by human activities and climatic extremes. Here we report a methodology that monitors threat status of these species in near real time, by deriving data from multiple open data sources, by linking them via a machine learning analytical framework, with interpretations facilitated by a web based geospatial visualization framework

Biological control of an agricultural pest protects tropical forests

Though often perceived as an environmentally-risky practice, biological control of invasive species can restore crop yields, ease land pressure and thus contribute to forest conservation. Here, we show how biological control against the mealybug Phenacoccus manihoti (Hemiptera) slows deforestation across Southeast Asia. In Thailand, this newly-arrived mealybug caused an 18% decline in cassava yields over 2009–2010 and an escalation in prices of cassava products. This spurred an expansion of cassava cropping in neighboring countries from 713,000 ha in 2009 to > 1 million ha by 2011: satellite imagery reveals 388%, 330%, 185% and 608% increases in peak deforestation rates in Cambodia, Lao PDR, Myanmar and Vietnam focused in cassava crop expansion areas. Following release of the host-specific parasitoid Anagyrus lopezi (Hymenoptera) in 2010, mealybug outbreaks were reduced, cropping area contracted and deforestation slowed by 31–95% in individual countries. Hence, when judiciously implemented, insect biological control can deliver substantial environmental benefits

Sequential Recurrent Encoders for Land Cover Mapping in the Brazilian Amazon using MODIS Imagery and Auxiliary Datasets

To test an existing sequential recurrent encoders model based on convolutional variants of RNNs for the task of LUC classification across the Brazilian Amazon and to compare different arrangements of input features and their impact on the classifier performanc

Indirect Impact Assessment of the road segment San Juan Nepomuceno-Ruta 6 in Paraguay

This study analyzes the past, current and potential future deforestation resulting from the improvement of the road segment between San Juan Nepomuceno and the 6th route (PR-L1080) in Southeastern Paraguay. For the purpose of the analysis, the study used satellite images to detect deviations from the usual pattern of vegetation and thus identify anthropogenic change. The deforestation baseline in the study area was defined using Landsat imagery from 1987 to 2014. Using this baseline and additional datasets such as distance to roads, distance to rivers, ecosystems and elevation, a map of deforestation risk was developed at national scale and then applied in the area of influence of the studied road. For the period 2000-2014, Paraguay recorded a very high deforestation rate of 0.77% per year, while the average deforestation rate in South America as a whole is around 0.41% per year. The main causes of deforestation in Paraguay are cattle ranching, agriculture activities and infrastructure development. Currently, most of the deforestation occurs in the Dry Chaco region of Paraguay located in the north of the country. A map of potential deforestation for the year 2023 was created based on the current rates of deforestation detected using Landsat imagery and the different levels of deforestation risk in a given area. Finally, potential future deforestation rates were calculated for the studied area. The results show that the implementation of this infrastructure project will potentially increase deforestation by 1.41% in the study area, especially, if appropriate measures for the management of natural resources are not undertaken. As a path of dirt road already exists and given the geographical conditions within the studied area, the risk of deforestation is currently very high even if the road is not paved yet. Therefore, the project of paving the current dirt road has a relatively low impact on the deforestation risk in the study area and the deforestation rates are predicted to be high with or without the road project implementation. Forests in this area are therefore already under a significant amount of pressure. Indeed, if the road is built the model predicts an estimated forest loss of 15,603 hectares during the next 10 years in the direct area of influence of the road, a substantial loss in the context of deforestation restrictions in the Paraguayan regulatory framework. These findings clearly indicate the indirect impacts that road infrastructure projects (improvement, pavement and construction) could have on land use change (via habitat loss and increased greenhouse gas (GHG) emissions). They also reconfirm the importance of not only ex-ante and detailed environmental impact assessments that should accompany any infrastructure project, but also of national and local policies aimed at discouraging deforestation and promoting compensation and habitat protection schemes, especially in areas known as important carbon sinks and essential for biodiversity conservation

Near real-time monitoring of cassava cultivation area

Remote sensing technologies and deep learning/machine learning approaches play valuable roles in crop inventory, yield estimation, cultivated area estimation, and crop status monitoring. Satellite-based remote sensing has led to increased spatial and temporal resolution, leading to a better quality of land-cover mapping (greater precision, and detail in the number of land cover classes). In this work, we propose to use a long short-term memory neural network (LSTM), an advanced technical model adapted from artificial neural networks (ANN) to estimate cassava cultivation area in southern Laos. LSTM is a modified version of a Recurrent Neural Network (RNN) that uses internal memory to store the information received prior to a given time. This property of LSTMs makes them advantageous for time series regression. We employ Landsat-7/8 and Sentinel-2 time-series datasets and crop phenology information to identify and classify cassava fields using multi-sources remote sensing time-series in a highly fragmented landscape. The results indicate an overall accuracy of > 89% for cassava and > 84% for all-class (barren, bush/grassland, cassava, coffee, forest, seasonal, and water) validating the feasibility of the proposed method. This study demonstrates the potential of LSTM approaches for crop classification using multi-temporal, multi-sources remote sensing time series

Terra-i+ webtool: Simplifying agroforestry sustainability monitoring

In an ever-evolving landscape of regulations and commitments to net-zero emission commodity chains, Terra-i+ offers a satellite-based solution for agroforestry supply chain sustainability management. At its core, Terra-i+ functions as an integrated platform to access critical information about the sustainability status of coffee supply chains. With Terra-i+, stakeholders gain access to essential metrics and insights, empowering them to make informed decisions that drive adoption of sustainable practices