itsdm: Isolation forest-based presence-only species distribution modelling and explanation in r

Multiple statistical algorithms have been used for species distribution modelling (SDM). Due to shortcomings in species occurrence datasets, presence-only methods (such as MaxEnt) have become increasingly widely used. However, sampling bias remains a challenging issue, particularly for density-based approaches. The Isolation Forest (iForest) algorithm is a presence-only method less sensitive to sampling patterns and over-fitting because it fits the model by describing the unsuitable instead of suitable conditions. Here, we present the itsdm package for species distribution modelling with iForest, which provides a workflow wrapper for the algorithms in iForest family and convenient tools for model diagnostic and post-modelling analysis. itsdm allows users to fit and evaluate an iForest SDM using presence-only occurrence data. It also helps the users to understand relationships between species and the living environment using Shapley values, a suggested technique in explainable artificial intelligence (xAI). Additionally, itsdm can make spatial response maps that indicate how species respond to environmental variables across space and detect areas potentially affected by a changing environment. We demonstrated the usage of the itsdm package and compared iForest with other mainstream SDMs using virtual species. The results enlightened that iForest is an advantageous presence-only SDM when the actual distribution range is unclear. © 2023 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society

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

Is closing the agricultural yield gap a “risky” endeavor?

CONTEXT: Sub-Saharan Africa (SSA) has the climatic and biophysical potential to grow the crops it needs to meet rapidly growing food demand; however, agricultural productivity remains low. While potential maize yields in Zambia are 9 t per hectare (t/ha), the average farmer produces only 1–2. OBJECTIVE: We evaluate the contribution of responses to weather risk to that gap by decomposing the yield gap in maize in Zambia. While we know that improved seed and fertilizer can expand yield and profit, they may also increase the variance of yield under different weather outcomes, reducing their adoption. METHODS: We use a novel approach combining crop modeling and statistical analysis of survey data to obtain the yield gap components in Zambia driven by input cost and input risk. We use a crop model to simulate district-level marginal effects of fertilizer and seed maturity choice on the mean and variance of expected yield and profit under all-weather outcomes for each district for the past 30 years. We compare input levels that maximize expected yield to those that maximize expected profit and maximize the expected mean-variance trade-off assuming risk-aversion. To determine how much farmers' input choices are made to reduce risk, we then quantify differences in the expected riskiness of inputs by district. RESULTS AND CONCLUSIONS: We find approximately one-quarter of the yield gap can be explained by risk-reducing behavior, albeit with a substantial geographic variation. Given this finding, under present conditions, we expect that the average maximum yield that farmers can obtain without increasing risk is 6.75 t/ha compared to a potential profit-maximizing level of 8.84 t/ha. SIGNIFICANCE: The risk-related yield gap is only expected to increase with weather extremes driven by climate change. Promoting “one-size-fits all” solutions to closing the yield gap could underestimate the effect of risk mitigation on agricultural production while increasing farmers.CONTEXTO: El África subsahariana (ASS) tiene el potencial climático y biofísico para aumentar los cultivos que necesita para satisfacer la creciente demanda de alimentos; sin embargo, la productividad agrícola sigue siendo baja. Si bien los rendimientos potenciales del maíz en Zambia son de 9 t por hectárea (t/ha), el agricultor promedio produce sólo 1-2. OBJETIVO: Evaluamos la contribución de las respuestas al riesgo climático a esa brecha descomponiendo la brecha de rendimiento del maíz en Zambia. Si bien sabemos que las semillas y los fertilizantes mejorados pueden aumentar el rendimiento y las ganancias, también pueden aumentar la variación del rendimiento en diferentes condiciones climáticas, lo que reduce su adopción. MÉTODO: Utilizamos un enfoque novedoso que combina modelos de cultivos y análisis estadístico de datos de encuestas para obtener los componentes de la brecha de rendimiento en Zambia impulsados por el costo y el riesgo de los insumos. Utilizamos un modelo de cultivo para simular los efectos marginales a nivel de distrito de la elección de la madurez de las semillas y los fertilizantes sobre la media y la varianza del rendimiento y la ganancia esperados bajo resultados en cualquier condición climática para cada distrito durante los últimos 30 años. Comparamos los niveles de insumos que maximizan el rendimiento esperado con aquellos que maximizan el beneficio esperado y maximizan la compensación esperada entre media y varianza suponiendo aversión al riesgo. Para determinar en qué medida los agricultores eligen insumos para reducir el riesgo, luego cuantificamos las diferencias en el riesgo esperado de los insumos por distrito. RESULTADOS Y CONCLUSIONES: Encontramos que aproximadamente una cuarta parte de la brecha de rendimiento puede explicarse por un comportamiento de reducción de riesgos, aunque con una variación geográfica sustancial. Dado este hallazgo, en las condiciones actuales, esperamos que el rendimiento máximo promedio que los agricultores pueden obtener sin aumentar el riesgo sea de 6,75 t/ha en comparación con un nivel potencial de maximización de ganancias de 8,84 t/ha. SIGNIFICADO: Sólo se espera que la brecha de rendimiento relacionada con el riesgo aumente con los extremos climáticos impulsados por el cambio climático. Promover soluciones únicas para cerrar la brecha de rendimiento podría subestimar el efecto de la mitigación de riesgos en la producción agrícola y al mismo tiempo aumentar los agricultores.Centro de Investigación en Economía y ProspectivaFil: Gatti, Nicolás. Instituto Nacional de Tecnología Agropecuaria (INTA). Centro de Investigación en Economía y Prospectiva (CIEP); ArgentinaFil: Gatti, Nicolás. Universidad del Centro de Estudios Macroeconómicos de Argentina (UCEMA); ArgentinaFil: Cecil, Michael. Clark University. Department of Geography; Estados UnidosFil: Baylis, Kathy. University of California Santa Barbara. Department of Geography; Estados UnidosFil: Estes, Lyndon. Clark University. Department of Geography; Estados UnidosFil: Blekking, Jordan. Indiana University. Bloomington Department of Geography; Estados UnidosFil: Heckelei, Thomas. Universitaet Bonn. Institute for Food and Resource Economics; AlemaniaFil: Vergopolan, Noemi. Princeton University. Atmospheric and Oceanic Sciences Program; Estados UnidosFi: Evans, Tom. University of Arizona. School of Geography, Development & Environment; Estados Unido

Is closing the agricultural yield gap a risky endeavor?

CONTEXT: Sub-Saharan Africa (SSA) has the climatic and biophysical potential to grow the crops it needs to meet rapidly growing food demand; however, agricultural productivity remains low. While potential maize yields in Zambia are 9 t per hectare (t/ha), the average farmer produces only 1–2. OBJECTIVE: We evaluate the contribution of responses to weather risk to that gap by decomposing the yield gap in maize in Zambia. While we know that improved seed and fertilizer can expand yield and profit, they may also increase the variance of yield under different weather outcomes, reducing their adoption. METHODS: We use a novel approach combining crop modeling and statistical analysis of survey data to obtain the yield gap components in Zambia driven by input cost and input risk. We use a crop model to simulate district-level marginal effects of fertilizer and seed maturity choice on the mean and variance of expected yield and profit under all-weather outcomes for each district for the past 30 years. We compare input levels that maximize expected yield to those that maximize expected profit and maximize the expected mean-variance trade-off assuming risk-aversion. To determine how much farmers\u27 input choices are made to reduce risk, we then quantify differences in the expected riskiness of inputs by district. RESULTS AND CONCLUSIONS: We find approximately one-quarter of the yield gap can be explained by risk-reducing behavior, albeit with a substantial geographic variation. Given this finding, under present conditions, we expect that the average maximum yield that farmers can obtain without increasing risk is 6.75 t/ha compared to a potential profit-maximizing level of 8.84 t/ha. SIGNIFICANCE: The risk-related yield gap is only expected to increase with weather extremes driven by climate change. Promoting “one-size-fits all” solutions to closing the yield gap could underestimate the effect of risk mitigation on agricultural production while increasing farmers\u27 risk exposure. © 2023 The Author

Climate Change: Helping Nature Survive the Human Response

Climate change poses profound, direct, and well-documented threats to biodiversity. A significant fraction of Earth’s 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

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]

On the Use of Unmanned Aerial Systems for Environmental Monitoring

Environmental monitoring plays a central role in diagnosing climate and management impacts on natural and agricultural systems; enhancing the understanding of hydrological processes; optimizing the allocation and distribution of water resources; and assessing, forecasting, and even preventing natural disasters. Nowadays, most monitoring and data collection systems are based upon a combination of ground-based measurements, manned airborne sensors, and satellite observations. These data are utilized in describing both small- and large-scale processes, but have spatiotemporal constraints inherent to each respective collection system. Bridging the unique spatial and temporal divides that limit current monitoring platforms is key to improving our understanding of environmental systems. In this context, Unmanned Aerial Systems (UAS) have considerable potential to radically improve environmental monitoring. UAS-mounted sensors offer an extraordinary opportunity to bridge the existing gap between field observations and traditional air- and space-borne remote sensing, by providing high spatial detail over relatively large areas in a cost-effective way and an entirely new capacity for enhanced temporal retrieval. As well as showcasing recent advances in the field, there is also a need to identify and understand the potential limitations of UAS technology. For these platforms to reach their monitoring potential, a wide spectrum of unresolved issues and application-specific challenges require focused community attention. Indeed, to leverage the full potential of UAS-based approaches, sensing technologies, measurement protocols, postprocessing techniques, retrieval algorithms, and evaluation techniques need to be harmonized. The aim of this paper is to provide an overview of the existing research and applications of UAS in natural and agricultural ecosystem monitoring in order to identify future directions, applications, developments, and challengespublishersversionPeer reviewe