Forests in Southeast Asia are experiencing some of the highest rates of deforestation and degradation in the world, with natural forest species being replaced by cropland and plantation monoculture. In this work, we have developed an innovative method to accurately map rubber and palm oil plantations using fusion of Landsat-8, Sentinel 1 and 2. We applied cloud and shadow masking, bidirectional reflectance distribution function (BRDF), atmospheric and topographic corrections to the optical imagery and a speckle filter and harmonics for Synthetic Aperture Radar (SAR) data. In this workflow, we created yearly composites for all sensors and combined the data into a single composite. A series of covariates were calculated from optical bands and sampled using reference data of the land cover classes including surface water, forest, urban and built-up, cropland, rubber, palm oil and mangrove. This training dataset was used to create biophysical probability layers (primitives) for each class. These primitives were then used to create land cover and probability maps in a decision tree logic and Monte-Carlo simulations. Validation showed good overall accuracy (84%) for the years 2017 and 2018. Filtering for validation points with high error estimates improved the accuracy up to 91%. We demonstrated and concluded that error quantification is an essential step in land cover classification and land cover change detection. Our overall analysis supports and presents a path for improving present assessments for sustainable supply chain analyses and associated recommendations.

Investors’ sophistication on climate risk is increasing and as part of this they require high-quality and comprehensive Scope 3 emissions data. Accordingly, we investigate Scope 3 emissions data divergence (across different providers), composition (which Scope 3 categories are reported) and whether machine-learning models can be used to predict Scope 3 emissions for non-reporting firms. We find considerable divergence in theaggregatedScope 3 emissions values from three of the largest data providers (Bloomberg, Refinitiv Eikon, and ISS). The divergence is largest for ISS, as it replaces reported Scope 3 emissions with estimates from its economic input-output and life cycle assessment modelling. With respect to the composition of Scope 3 emissions, firms generally report incomplete composition, yet they are reporting more categories over time. There is a persistent contrast between relevance and completeness in the composition of Scope 3 emissions across sectors, with low materiality categories such as travel emissions being reported more frequently than typically high materiality ones, such as the use of products and processing of sold products. Finally, machine learning algorithms can improve the prediction accuracy of the aggregated Scope 3 emissions by up to 6% and up to 25% when each category is estimated individually and aggregated into total Scope 3 emissions. However, absolute prediction performance is low even with the best models, with the accuracy of estimates primarily limited by low observations in specific Scope 3 categories. We conclude that investors should be cognizant of Scope 3 emissions data divergence, incomplete reporting of Scope 3 categories, and that predictions for non-reporting firms have high absolute errors even when using machine learning models. For both reported and estimated data, caveat emptor applies.

In this study, we develop a vegetation monitoring framework which is applicable at a planetary scale, and is based on the BACI (Before-After, Control-Impact) design. This approach utilizes Google Earth Engine, a state-of-the-art cloud computing platform. A web-based application for users named EcoDash was developed. EcoDash maps vegetation using Enhanced Vegetation Index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) products (the MOD13A1 and MYD13A1 collections) from both Terra and Aqua sensors from the years 2000 and 2002, respectively. to detect change in vegetation, we define an EVI baseline period, and then draw results at a planetary scale using the web-based application by measuring improvement or degradation in vegetation based on the user-defined baseline periods. We also used EcoDash to measure the impact of deforestation and mitigation efforts by the Vietnam Forests and Deltas (VFD) program for the Nghe An and Thanh Hoa provinces in Vietnam. Using the period before 2012 as a baseline, we found that as of March 2017, 86% of the geographical area within the VFD program shows improvement, compared to only a 24% improvement in forest cover for all of Vietnam. Overall, we show how using satellite imagery for monitoring vegetation in a cloud-computing environment could be a cost-effective and useful tool for land managers and other practitioners

Understanding land cover change dynamics and potential pathways of change is of critical importance for sustainable resource management, to promote food security and resilience on a range of spatial scales. Data scarcity is a key concern, however, with the availability of free Earth Observation (EO) data, such challenges can be suitably addressed. In this research we have developed a robust machine learning (random forest) approach utilizing EO and Geographic Information System (GIS) data, which enables an innovative means for our simulations to be driven only by historical drivers of change and hotspot prediction based on probability to change. We used the Mekong region as a case study to generate a training and validation sample from historical land cover patterns of change and used this information to train a random forest machine learning model. Data samples were created from the SERVIR-Mekong land cover data series. Data sets were created for 2 categories both containing 8 classes. The 2 categories included—any generic class to change into a specific one and vice versa. Classes included the following: Aquaculture; Barren; Cropland; Flooded Forest; Mangroves; Forest; Plantations; Wetlands; and Urban. The training points were used to sample a series of satellite-derived surface reflectance products and other data layers such as information on slope, distance to road and census data, which represent the drivers of change. The classifier was trained in binary mode and showed a clear separation between change and no change. An independent validation dataset of historical change pixels show that all median change probabilities are greater than 80% and all lower quantiles, except one, are greater than 70%. The 2018 probability change maps show high probabilities for the Plantations and Forest classes in the ‘Generic to Specific’ and ’Specific to generic’ category, respectively. A time-series analysis of change probability shows that forests have become more likely to convert into other classes during the last two decades, across all countries. We successfully demonstrated that historical change patters combined with big data and machine learning technologies are powerful tools for predictive change analytics on a planetary scale.

We take advantage of a combination of a severe weather event from 3 to 4 June 2015 and a local policy, to investigate the housing market response to climate change‐related flooding hazard. The study focuses on a residential area in a low‐lying coastal suburb of Dunedin, New Zealand, where the groundwater level is shallow and close to sea level. An unusually heavy rain event in June 2015 resulted in flooding of a significant portion of land in especially low‐lying areas. The city council responded by reviewing processes for storm‐water management and by imposing minimum‐floor‐level [MFL] requirements on new construction in the low‐lying areas previously identified as at risk of flooding. Applying a ‘diff‐in‐diff‐in‐diff’ strategy in hedonic regression analyses, we find that houses in the MFL zone sell for a discount of about 5 per cent prior to the flood. This discount briefly tripled in the area that flooded, but disappeared within 15 months, indicating either very short memory among homebuyers or no long‐run change in perception of hazard.

Satellite-based forest alert systems are an important tool for ecosystem monitoring, planning conservation, and increasing public awareness of forest cover change. Continuous monitoring in tropical regions, such as those experiencing pronounced monsoon seasons, can be complicated by spatially extensive and persistent cloud cover. One solution is to use Synthetic Aperture Radar (SAR) imagery acquired by the European Space Agency’s Sentinel-1A and B satellites. The Sentinel 1A and B satellites acquire C-band radar data that penetrates cloud cover and can be acquired during the day or night. One challenge associated with operational use of radar imagery is that the speckle associated with the backscatter values can complicate traditional pixel-based analysis approaches. A potential solution is to use deep learning semantic segmentation models that can capture predictive features that are more robust to pixel-level noise. In this analysis, we present a prototype SAR-based forest alert system that utilizes deep learning classifiers, deployed using the Google Earth Engine cloud computing platform, to identify forest cover change with near real-time classification over two Cambodian wildlife sanctuaries. By leveraging a pre-existing forest cover change dataset derived from multispectral Landsat imagery, we present a method for efficiently developing a SAR-based semantic segmentation dataset. In practice, the proposed framework achieved good performance comparable to an existing forest alert system while offering more flexibility and ease of development from an operational standpoint.