A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery

Semantic segmentation (classification) of Earth Observation imagery is a crucial task in remote sensing. This paper presents a comprehensive review of technical factors to consider when designing neural networks for this purpose. The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and transformer models, discussing prominent design patterns for these ANN families and their implications for semantic segmentation. Common pre-processing techniques for ensuring optimal data preparation are also covered. These include methods for image normalization and chipping, as well as strategies for addressing data imbalance in training samples, and techniques for overcoming limited data, including augmentation techniques, transfer learning, and domain adaptation. By encompassing both the technical aspects of neural network design and the data-related considerations, this review provides researchers and practitioners with a comprehensive and up-to-date understanding of the factors involved in designing effective neural networks for semantic segmentation of Earth Observation imagery.Comment: 145 pages with 32 figure

Improved Fine-Scale Tropical Forest Cover Mapping for Southeast Asia Using Planet-NICFI and Sentinel-1 Imagery

The accuracy of existing forest cover products typically suffers from “rounding” errors arising from classifications that estimate the fractional cover of forest in each pixel, which often exclude the presence of large, isolated trees and small or narrow forest clearings, and is primarily attributable to the moderate resolution of the imagery used to make maps. However, the degree to which such high-resolution imagery can mitigate this problem, and thereby improve large-area forest cover maps, is largely unexplored. Here, we developed an approach to map tropical forest cover at a fine scale using Planet and Sentinel-1 synthetic aperture radar (SAR) imagery in the Google Earth Engine platform and used it to map all of Southeastern Asia’s forest cover. The machine learning approach, based on the Random Forests models and trained and validated using a total of 37,345 labels collected from Planet imagery across the entire region, had an accuracy of 0.937 and an F1 score of 0.942, while a version based only on Planet imagery had an accuracy of 0.908 and F1 of 0.923. We compared the accuracy of our resulting maps with 5 existing forest cover products derived from medium-resolution optical-only or combined optical-SAR approaches at 3,000 randomly selected locations. We found that our approach overall achieved higher accuracy and helped minimize the rounding errors commonly found along small or narrow forest clearings and deforestation frontiers where isolated trees are common. However, the forest area estimates varied depending on topographic location and showed smaller differences in highlands (areas \u3e300 m above sea level) but obvious differences in complex lowland landscapes. Overall, the proposed method shows promise for monitoring forest changes, particularly those caused by deforestation frontiers. Our study also represents one of the most extensive applications of Planet imagery to date, resulting in an open, high-resolution map of forest cover for the entire Southeastern Asia region. © 2023 Feng Yang et al

Climate change: Helping nature survive the human response

Climate change poses profound, direct, and well-documented threats to biodiversity. A significant fraction of Earth\u27s species is at risk of extinction due to changing precipitation and temperature regimes, rising and acidifying oceans, and other factors. There is also growing awareness of the diversity and magnitude of responses, both proactive and reactive, that people will undertake as lives and livelihoods are affected by climate change. Yet to date few studies have examined the relationship between these two powerful forces. The natural systems upon which people depend, already under direct assault from climate change, are further threatened by how we respond to climate change. Human history and recent studies suggest that our actions to cope with climate change (adaptation) or lessen its rate and magnitude (mitigation) could have impacts that match-and even exceed-the direct effects of climate change on ecosystems. If we are to successfully conserve biodiversity and maintain ecosystem services in a warming world, considerable effort is needed to predict and reduce the indirect risks created by climate change. ©2010 Wiley Periodicals, Inc.

Cognitive Biases about Climate Variability in Smallholder Farming Systems in Zambia

Given the varying manifestations of climate change over time and the influence of climate perceptions on adaptation, it is important to understand whether farmer perceptions match patterns of environmental change from observational data. We use a combination of social and environmental data to understand farmer perceptions related to rainy season onset. Household surveys were conducted with 1171 farmers across Zambia at the end of the 2015/16 growing season eliciting their perceptions of historic changes in rainy season onset and their heuristics about when rain onset occurs. We compare farmers' perceptions with satellite-gauge-derived rainfall data from the Climate Hazards Group Infrared Precipitation with Station dataset and hyper-resolution soil moisture estimates from the HydroBlocks land surface model. We find evidence of a cognitive bias, where farmers perceive the rains to be arriving later, although the physical data do not wholly support this. We also find that farmers' heuristics about rainy season onset influence maize planting dates, a key determinant of maize yield and food security in sub-Saharan Africa. Our findings suggest that policy makers should focus more on current climate variability than future climate change.National Science Foundation [SES-1360463, BCS-1115009, BCS-1026776]6 month embargo; published online: 29 March 2019This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Climate change must not blow conservation off course

The authors comment on the national strategy, National Fish, Wildlife and Plants Climate Adaptation Strategy released by the U.S. for conservation planning for climatic changes. They state that configuration of the protection policies around global warming could be harmful and might be of limited use. They mention considering climate changes for protection of biodiversity

Probabilistic global maps of crop-specific areas from 1961 to 2014

Agriculture has substantial socioeconomic and environmental impacts that vary between crops. However, information on how the spatial distribution of specific crops has changed over time across the globe is relatively sparse. We introduce the Probabilistic Cropland Allocation Model (PCAM), a novel algorithm to estimate where specific crops have likely been grown over time. Specifically, PCAM downscales annual and national-scale data on the crop-specific area harvested of 17 major crops to a global 0.5-degree grid from 1961 to 2014. To do this, pixels are assigned into probability clusters based upon crop-specific pixel suitability (based on mean climate and soil characteristics) and gridded historical agricultural areas. PCAM maps compare relatively well with an existing gridded dataset of crop-specific areas circa 2000 (simple matching coefficient value \u3e0.8 for all crops). PCAM estimates compare less well with time series county-level agricultural census data for the United States. Importantly, deviations between census data and PCAM benchmark estimates (driven by soil and climate suitability) can be used to infer the importance of other factors of agricultural production (e.g. labor, agricultural policy, extreme climate) in future work. Our results provide new insights into the likely changes in the spatial distribution of major crops over the past half-century

Consequences of underexplored variation in biodiversity indices used for land-use prioritization

For biodiversity protection to play a persuasive role in land-use planning, conservationists must be able to offer objective systems for ranking which natural areas to protect or convert. Representing biodiversity in spatially explicit indices is challenging because it entails numerous judgments regarding what variables to measure, how to measure them, and how to combine them. Surprisingly few studies have explored this variation. Here, we explore how this variation affects which areas are selected for agricultural conversion by a land-use prioritization model designed to reduce the biodiversity losses associated with agricultural expansion in Zambia. We first explore the similarity between model recommendations generated by three recently published composite indices and a commonly used rarity-weighted species richness metric. We then explore four underlying sources of ecological and methodological variation within these and other approaches, including different terrestrial vertebrate taxonomic groups, different species-richness metrics, different mathematical methods for combining layers, and different spatial resolutions of inputs. The results generated using different biodiversity approaches show very low spatial agreement regarding which areas to convert to agriculture. There is little overlap in areas identified for conversion using previously published indices (mean Jaccard similarity, Jw, between 0.3 and 3.7%), different taxonomic groups (5.0% \u3c mean Jw \u3c 13.5%), or different measures of species richness (15.6% \u3c mean Jw \u3c 33.7%). Even with shared conservation goals, different methods for combining layers and different input spatial resolutions still produce meaningful, though smaller, differences among areas selected for conversion (40.9% \u3c mean Jw \u3c 67.5%). The choice of taxonomic group had the largest effect on conservation priorities, followed by the choice of species richness metric, the choice of combination method, and finally the choice of spatial resolution. These disagreements highlight the challenge of objectively representing biodiversity in land-use planning tools, and present a credibility challenge for conservation scientists seeking to inform policy making. Our results suggest an urgent need for a more consistent and transparent framework for designing the biodiversity indices used in land-use planning, which we propose here

The influence of climate variability on internal migration flows in South Africa

This work investigates the impact of climate variability on internal migration flows in post-apartheid South Africa. We combine information from South African censuses and climatic data to build a panel database covering the waves 1997-2001 and 2007-2011. The database enables the examination of the effect of spatiotemporal variability in temperature and precipitation on inter-district migration flows defined by five-year intervals. We employ a gravity approach where bilateral migration flows are explained by climate variability at the origin, along with a number of geographic, socio-economic and demographic factors traditionally identified as potential drivers of migration. Overall, we find that an increase in positive temperature extremes as well as positive and negative excess rainfall at the origin act as a push effect and enhance out-migration. However, the significance of the effect of climate on migration greatly varies by migrant characteristics. Particularly, flows of black and low-income South African migrants are strongly influenced by climatic variables whereas those of white and high-income migrants exhibit a weak impact. We also argue that agriculture may function as a transmission channel through which adverse climatic conditions affect migration

A generalized computer vision approach to mapping crop fields in heterogeneous agricultural landscapes

Smallholder farms dominate in many parts of the world, particularly Sub-Saharan Africa. These systems are characterized by small, heterogeneous, and often indistinct field patterns, requiring a specialized methodology to map agricultural land cover. Using a variety of sites in South Africa, we present a new approach to mapping agricultural fields, based on efficient extraction of a vast set of simple, highly correlated, and interdependent features, followed by a random forest classifier. We achieved similar high performance across agricultural types, including the spectrally indistinct smallholder fields as well as the more easily distinguishable commercial fields, and demonstrated the ability to generalize performance across large geographic areas. In sensitivity analyses, we determined multi-temporal information provided greater gains in performance than the addition of multi-spectral bands available in DigitalGlobe Worldview-2 imagery