Detecting Drought-Induced Tree Mortality in Sierra Nevada Forests with Time Series of Satellite Data

Seasonal forecasts of crop yield are important components for agricultural policy decisions and farmer planning. A wide range of input data are often needed to forecast crop yield in a region where sophisticated approaches such as machine learning and process-based models are used. This requires considerable effort for data preparation in addition to identifying data sources. Here, we propose a simpler approach called the Analogy Based Crop-yield (ABC) forecast scheme to make timely and accurate prediction of regional crop yield using a minimum set of inputs. In the ABC method, a growing season from a prior long-term period, e.g., 10 years, is first identified as analogous to the current season by the use of a similarity index based on the time series leaf area index (LAI) patterns. Crop yield in the given growing season is then forecasted using the weighted yield average reported in the analogous seasons for the area of interest. The ABC approach was used to predict corn and soybean yields in the Midwestern U.S. at the county level for the period of 2017–2019. The MOD15A2H, which is a satellite data product for LAI, was used to compile inputs. The mean absolute percentage error (MAPE) of crop yield forecasts was <10% for corn and soybean in each growing season when the time series of LAI from the day of year 89 to 209 was used as inputs to the ABC approach. The prediction error for the ABC approach was comparable to results from a deep neural network model that relied on soil and weather data as well as satellite data in a previous study. These results indicate that the ABC approach allowed for crop yield forecast with a lead-time of at least two months before harvest. In particular, the ABC scheme would be useful for regions where crop yield forecasts are limited by availability of reliable environmental data.

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Cross-scale interaction of host tree size and climatic water deficit governs bark beetle-induced tree mortality

Nature Communications ◽

10.1038/s41467-020-20455-y ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Michael J. Koontz ◽

Andrew M. Latimer ◽

Leif A. Mortenson ◽

Christopher J. Fettig ◽

Malcolm P. North

Keyword(s):

Water Deficit ◽

Sierra Nevada ◽

Bark Beetle ◽

Ponderosa Pine ◽

Tree Mortality ◽

Mortality Rates ◽

Dendroctonus Brevicomis ◽

Scale Interactions ◽

Climatic Water Deficit ◽

Host Mortality

AbstractThe recent Californian hot drought (2012–2016) precipitated unprecedented ponderosa pine (Pinus ponderosa) mortality, largely attributable to the western pine beetle (Dendroctonus brevicomis; WPB). Broad-scale climate conditions can directly shape tree mortality patterns, but mortality rates respond non-linearly to climate when local-scale forest characteristics influence the behavior of tree-killing bark beetles (e.g., WPB). To test for these cross-scale interactions, we conduct aerial drone surveys at 32 sites along a gradient of climatic water deficit (CWD) spanning 350 km of latitude and 1000 m of elevation in WPB-impacted Sierra Nevada forests. We map, measure, and classify over 450,000 trees within 9 km2, validating measurements with coincident field plots. We find greater size, proportion, and density of ponderosa pine (the WPB host) increase host mortality rates, as does greater CWD. Critically, we find a CWD/host size interaction such that larger trees amplify host mortality rates in hot/dry sites. Management strategies for climate change adaptation should consider how bark beetle disturbances can depend on cross-scale interactions, which challenge our ability to predict and understand patterns of tree mortality.

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Estimating long-term tree mortality rate time series by combining data from periodic inventories and harvest reports in a Bayesian state-space model

Forest Ecology and Management ◽

10.1016/j.foreco.2012.12.022 ◽

2013 ◽

Vol 292 ◽

pp. 64-74 ◽

Cited By ~ 7

Author(s):

Katalin Csilléry ◽

Maëlle Seignobosc ◽

Valentine Lafond ◽

Georges Kunstler ◽

Benoît Courbaud

Keyword(s):

Time Series ◽

Mortality Rate ◽

State Space ◽

Tree Mortality ◽

State Space Model ◽

Space Model ◽

Combining Data

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The Use of Satellite TIR Time Series for Thermal Anomalies’ Detection on Natural and Urban Areas

Engineering Proceedings ◽

10.3390/engproc2021005005 ◽

2021 ◽

Vol 1 (1) ◽

Author(s):

Malvina Silvestri ◽

Federico Rabuffi ◽

Massimo Musacchio ◽

Sergio Teggi ◽

Maria Fabrizia Buongiorno

Keyword(s):

Time Series ◽

Land Surface Temperature ◽

Satellite Data ◽

Land Surface ◽

Urban Areas ◽

Temperature Time Series ◽

Campi Flegrei ◽

Thermal Anomalies ◽

Pca Method ◽

Temperature Time

In this work, the land surface temperature time series derived using Thermal InfraRed (TIR) satellite data offers the possibility to detect thermal anomalies by using the PCA method. This approach produces very detailed maps of thermal anomalies, both in geothermal areas and in urban areas. Tests were conducted on the following three Italian sites: Solfatara-Campi Flegrei (Naples), Parco delle Biancane (Grosseto) and Modena city.

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Response of high-elevation limber pine (Pinus flexilis) to multiyear droughts and 20th-century warming, Sierra Nevada, California, USA

Canadian Journal of Forest Research ◽

10.1139/x07-097 ◽

2007 ◽

Vol 37 (12) ◽

pp. 2508-2520 ◽

Cited By ~ 46

Author(s):

Constance I. Millar ◽

Robert D. Westfall ◽

Diane L. Delany

Keyword(s):

20Th Century ◽

Sierra Nevada ◽

Mountain Pine Beetle ◽

Tree Mortality ◽

High Elevation ◽

Dwarf Mistletoe ◽

Lower Growth ◽

Pinus Flexilis ◽

Limber Pine ◽

Temperature And Precipitation

Limber pine ( Pinus flexilis James) stands along the eastern escarpment of the Sierra Nevada, California, experienced significant mortality from 1985 to 1995 during a period of sustained low precipitation and high temperature. The stands differ from old-growth limber pine forests in being dense, young, more even-aged, and located in warmer, drier microclimates. Tree growth showed high interannual variability. Relative to live trees, dead trees over their lifetimes had higher series sensitivity, grew more variably, and had lower growth. Although droughts recurred during the 20th century, tree mortality occurred only in the late 1980s. Significant correlations and interactions of growth and mortality dates with temperature and precipitation indicate that conditions of warmth plus sustained drought increased the likelihood of mortality in the 1985–1995 interval. This resembles a global-change-type drought, where warming combined with drought was an initial stress, trees were further weakened by dwarf mistletoe ( Arceuthobium cyanocarpum (A. Nels. ex Rydb.) A. Nels.), and proximally killed by mountain pine beetle ( Dendroctonus ponderosae Hopkins). However, the thinning effect of the drought-related mortality appears to have promoted resilience and improved near-term health of these stands, which suffered no additional mortality in the subsequent 1999–2004 drought.

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DEVELOPMENT OF TIME-SERIES HUMAN SETTLEMENT MAPPING SYSTEM USING HISTORICAL LANDSAT ARCHIVE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b8-1385-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 1385-1388

Author(s):

H. Miyazaki ◽

M. Nagai ◽

R. Shibasaki

Keyword(s):

Public Health ◽

Time Series ◽

Risk Management ◽

Satellite Data ◽

Disaster Risk ◽

Disaster Risk Management ◽

Training Data ◽

Series Data ◽

Landsat Data ◽

Human Settlement

Methodology of automated human settlement mapping is highly needed for utilization of historical satellite data archives for urgent issues of urban growth in global scale, such as disaster risk management, public health, food security, and urban management. As development of global data with spatial resolution of 10-100 m was achieved by some initiatives using ASTER, Landsat, and TerraSAR-X, next goal has targeted to development of time-series data which can contribute to studies urban development with background context of socioeconomy, disaster risk management, public health, transport and other development issues. We developed an automated algorithm to detect human settlement by classification of built-up and non-built-up in time-series Landsat images. A machine learning algorithm, Local and Global Consistency (LLGC), was applied with improvements for remote sensing data. The algorithm enables to use MCD12Q1, a MODIS-based global land cover map with 500-m resolution, as training data so that any manual process is not required for preparation of training data. In addition, we designed the method to composite multiple results of LLGC into a single output to reduce uncertainty. The LLGC results has a confidence value ranging 0.0 to 1.0 representing probability of built-up and non-built-up. The median value of the confidence for a certain period around a target time was expected to be a robust output of confidence to identify built-up or non-built-up areas against uncertainties in satellite data quality, such as cloud and haze contamination. Four scenes of Landsat data for each target years, 1990, 2000, 2005, and 2010, were chosen among the Landsat archive data with cloud contamination less than 20%.We developed a system with the algorithms on the Data Integration and Analysis System (DIAS) in the University of Tokyo and processed 5200 scenes of Landsat data for cities with more than one million people worldwide.

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DEVELOPMENT OF TIME-SERIES HUMAN SETTLEMENT MAPPING SYSTEM USING HISTORICAL LANDSAT ARCHIVE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b8-1385-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 1385-1388 ◽

Cited By ~ 1

Author(s):

H. Miyazaki ◽

M. Nagai ◽

R. Shibasaki

Keyword(s):

Public Health ◽

Time Series ◽

Risk Management ◽

Satellite Data ◽

Disaster Risk ◽

Disaster Risk Management ◽

Training Data ◽

Series Data ◽

Landsat Data ◽

Human Settlement

Methodology of automated human settlement mapping is highly needed for utilization of historical satellite data archives for urgent issues of urban growth in global scale, such as disaster risk management, public health, food security, and urban management. As development of global data with spatial resolution of 10-100 m was achieved by some initiatives using ASTER, Landsat, and TerraSAR-X, next goal has targeted to development of time-series data which can contribute to studies urban development with background context of socioeconomy, disaster risk management, public health, transport and other development issues. We developed an automated algorithm to detect human settlement by classification of built-up and non-built-up in time-series Landsat images. A machine learning algorithm, Local and Global Consistency (LLGC), was applied with improvements for remote sensing data. The algorithm enables to use MCD12Q1, a MODIS-based global land cover map with 500-m resolution, as training data so that any manual process is not required for preparation of training data. In addition, we designed the method to composite multiple results of LLGC into a single output to reduce uncertainty. The LLGC results has a confidence value ranging 0.0 to 1.0 representing probability of built-up and non-built-up. The median value of the confidence for a certain period around a target time was expected to be a robust output of confidence to identify built-up or non-built-up areas against uncertainties in satellite data quality, such as cloud and haze contamination. Four scenes of Landsat data for each target years, 1990, 2000, 2005, and 2010, were chosen among the Landsat archive data with cloud contamination less than 20%.We developed a system with the algorithms on the Data Integration and Analysis System (DIAS) in the University of Tokyo and processed 5200 scenes of Landsat data for cities with more than one million people worldwide.

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