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Applying Earth Surface Monitoring to Investigate Climate and Land Change Interactions

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (30 April 2020) | Viewed by 45940

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Special Issue Editors

Mr. Christopher Soulard

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Guest Editor

US Geological Survey, Western Geographic Science Center 345 Middlefield Road, Menlo Park, CA 94025, USA
Interests: land use; land cover; remote sensing; image classification; change detection; cloud computing

Dr. Miguel Villarreal

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Guest Editor

US Geological Survey, Western Geographic Science Center 345 Middlefield Road, Menlo Park, CA 94025, USA
Interests: drylands; landscape ecology; geospatial analysis; remote sensing; change detection

Special Issue Information

Dear Colleagues,

Monitoring change across the Earth’s surface has evolved considerably over the past five decades since civilian Earth Observation (EO) satellites were first launched into orbit. Continual technological advances and access to free satellite imagery have increased our ability to map and monitor both abrupt and subtle land surface changes over large collections of images. Extracting information on the surface changes with high spatial and temporal resolution is now being applied at national and global scales like never before. In many cases, large-scale efforts are aided by cloud-based EO processing that circumvents storage and processing constraints and new methodological approaches (including machine learning) that produce higher confidence land change estimates.

The promise of higher-quality mapping provides innumerable benefits to applications reliant on such data, from analyses of driving forces to empirically-driven projections of land change. Increasingly, land change monitoring efforts are being coupled with climate records in an effort to better understand how climate changes and land changes interact over short and long time frames. Investigating interactions between land change and climate is critical for reconstructing past landscapes under different climate conditions and projecting future land changes under different climate scenarios.

In this Special Issue, we welcome contributions that further advance EOS land change monitoring but have a greater interest in contributions that investigate cause–effect interactions between land change (detected by EOS) and climate. We request submissions on the following topics:

New machine/deep learning algorithms for multi-temporal EOS analysis;
Monthly-to-annual scale monitoring using cloud computing;
Innovative applications in land change topics, including drought monitoring, vegetation phenology, post-fire vegetation recovery, etc.;
Improvements in detecting and analyzing subtle changes using EOS;
Disentangling the role of climate on land change in complex systems;
Forcings and feedbacks between climate and land change over space and time;
Novel trend analyses across dense time series of climate and land cover change information;
Surface change hindcasting or forecasting informed by established climate-land change relationships.

Mr. Christopher Soulard
Dr. Miguel Villarreal
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

landscape change detection
time series
climate sensitivity
climate projections
scenarios
machine learning
cloud computing

Published Papers (11 papers)

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Research

30 pages, 26945 KiB

Open AccessArticle

Investigating the Effects of Land Use and Land Cover on the Relationship between Moisture and Reflectance Using Landsat Time Series

by Heather J. Tollerud, Jesslyn F. Brown and Thomas R. Loveland

Remote Sens. 2020, 12(12), 1919; https://doi.org/10.3390/rs12121919 - 13 Jun 2020

Cited by 13 | Viewed by 3709

Abstract

To better understand the Earth system, it is important to investigate the interactions between precipitation, land use/land cover (LULC), and the land surface, especially vegetation. An improved understanding of these land-atmosphere interactions can aid understanding of the climate system and modeling of time series satellite data. Here, we investigate the effect of precipitation and LULC on the reflectance of the land surface in the northern U.S. Great Plains. We utilize time series satellite data from the 45 year Landsat archive. The length of the Landsat record allows for analysis of multiple periods of drought and wet conditions (reflecting climate, as well as weather), such that the precipitation-reflectance relationship can be investigated robustly for every individual pixel in the study area. The high spatial resolution of Landsat (30 m) allows for investigation of spatial patterns in weather (i.e., precipitation extremes) interactions with land surface reflectance at the scale of individual fields. Weather history is represented by a drought index that describes effective moisture availability, the Standardized Precipitation and Evaporation Index (SPEI). We find that effective moisture has a robust and consistent effect on reflectance over many types of land cover, with ∼90% of all pixels having significantly (

p < 0.01

) higher visible reflectance during dry periods than during wet, occurring in nearly all regional, temporal, and LULC categories investigated. In grassland, the relationship is especially strong; there is an average reflectance increase of more than a third between very wet and very dry conditions (red band), and ∼99% of pixels have a significant relationship. In cropland, the effective moisture-reflectance relationship is more variable, suggesting that management decisions are an important factor in cropland-reflectance relationships. Full article

(This article belongs to the Special Issue Applying Earth Surface Monitoring to Investigate Climate and Land Change Interactions)

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32 pages, 3858 KiB

Open AccessArticle

A Cloud-Based Evaluation of the National Land Cover Database to Support New Mexico’s Food–Energy–Water Systems

by Melakeneh G. Gedefaw, Hatim M.E. Geli, Kamini Yadav, Ashraf J. Zaied, Yelena Finegold and Kenneth G. Boykin

Remote Sens. 2020, 12(11), 1830; https://doi.org/10.3390/rs12111830 - 5 Jun 2020

Cited by 6 | Viewed by 3325

Abstract

Accurate estimation of land use/land cover (LULC) areas is critical, especially over the semi-arid environments of the southwestern United States where water shortage and loss of rangelands and croplands are affecting the food production systems. This study was conducted within the context of providing an improved understanding of New Mexico’s (NM’s) Food–Energy–Water Systems (FEWS) at the county level. The main goal of this analysis was to evaluate the most important LULC classes for NM’s FEWS by implementing standardized protocols of accuracy assessment and providing bias-corrected area estimates of these classes. The LULC data used in the study was based on National Land Cover Database (NLCD) legacy maps of 1992, 2001, 2006, 2011, and 2016. The analysis was conducted using the cloud-based geospatial processing and modeling tools available from System for Earth Observation Data Access, Processing, and Analysis for Land Monitoring (SEPAL) of the Food and Agricultural Organization. Accuracy assessment, uncertainty analysis, and bias-adjusted area estimates were evaluated by collecting a total of 11,428 reference samples using the Open Foris Collect Earth tool that provided access to high spatial and temporal resolution images available in Google Earth. The reference samples were allocated using a stratified random sampling approach. The results showed an overall accuracy that ranged from 71%–100% in all six study counties. The user’s and producer’s accuracy of most LULC classes were about or above 80%. The obtained bias-adjusted area estimates were higher than those based on pixel counting. The bias-adjusted area estimates simultaneously showed decreasing and increasing trends in grassland and shrubland, respectively in four counties that include Curry, Roosevelt, Lea, and Eddy during the 1992–2016 period. Doña Ana county experienced increasing and decreasing trends in grassland and shrubland areas, respectively. San Juan county experienced decreasing trends in both grassland and shrubland areas. Cultivated cropland areas showed decreasing trends in three counties in southeast NM that rely on groundwater resources including Curry, Roosevelt, and Lea. Similarly, cultivated cropland areas showed increasing trends in the other three counties that rely on surface water or conjunctive use of surface and groundwater resources including San Juan, Doña Ana, and Eddy. The use of SEPAL allowed for efficient assessment and production of more accurate bias-adjusted area estimates compared to using pixel counting. Providing such information can help in understanding the behavior of NM’s food production systems including rangelands and croplands, better monitoring and characterizing NM’s FEWS, and evaluating their behavior under changing environmental and climatic conditions. More effort is needed to evaluate the ability of the NLCD data and other similar products to provide more accurate LULC area estimates at local scales. Full article

(This article belongs to the Special Issue Applying Earth Surface Monitoring to Investigate Climate and Land Change Interactions)

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19 pages, 6126 KiB

Open AccessArticle

Elevation and Climate Effects on Vegetation Greenness in an Arid Mountain-Basin System of Central Asia

by Xiaoli Tai, Howard E. Epstein and Bo Li

Remote Sens. 2020, 12(10), 1665; https://doi.org/10.3390/rs12101665 - 22 May 2020

Cited by 16 | Viewed by 3434

Abstract

Mountain-basin systems (MBS) in Central Asia are unique and complex ecosystems, wherein their elevation gradients lead to high spatial heterogeneity in vegetation and its response to climate change. Exploring elevation-dependent vegetation greenness variation and the effects of climate factors on vegetation has important theoretical and practical significance for regulating the ecological processes of this system. Based on the MODIS NDVI (remotely sensed normalized difference vegetation index), and observed precipitation and temperature data sets, we analyzed vegetation greenness and climate patterns and dynamics with respect to elevation (300–3600 m) in a typical MBS, in Altay Prefecture, China, during 2000–2017. Results showed that vegetation exhibited a greening (NDVI) trend for the whole region, as well as the mountain, oasis and desert zones, but only the desert zone reached significant level. Vegetation in all elevation bins showed greening, with significant trends at 400–700 m and 2600–3500 m. In summer, lower elevation bins (below 1500 m) had a nonsignificant wetting and warming trend and higher elevation bins had a nonsignificant drying and warming trend. Temperature trend increased with increasing elevation, indicating that warming was stronger at higher elevations. In addition, precipitation had a significantly positive coefficient and temperature a nonsignificant coefficient with NDVI at both regional scale and subregional scale. Our analysis suggests that the regional average could mask or obscure the relationship between climate and vegetation at elevational scale. Vegetation greenness had a positive response to precipitation change in all elevation bins, and had a negative response to temperature change at lower elevations (below 2600 m), and a positive response to temperature change at higher elevations. We observed that vegetation greenness was more sensitive to precipitation than to temperature at lower elevations (below 2700 m), and was more sensitive to temperature at higher elevations. Full article

(This article belongs to the Special Issue Applying Earth Surface Monitoring to Investigate Climate and Land Change Interactions)

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29 pages, 7849 KiB

Open AccessArticle

Isolating Anthropogenic Wetland Loss by Concurrently Tracking Inundation and Land Cover Disturbance across the Mid-Atlantic Region, U.S.

by Melanie K. Vanderhoof, Jay Christensen, Yen-Ju G. Beal, Ben DeVries, Megan W. Lang, Nora Hwang, Christine Mazzarella and John W. Jones

Remote Sens. 2020, 12(9), 1464; https://doi.org/10.3390/rs12091464 - 5 May 2020

Cited by 13 | Viewed by 3562

Abstract

Global trends in wetland degradation and loss have created an urgency to monitor wetland extent, as well as track the distribution and causes of wetland loss. Satellite imagery can be used to monitor wetlands over time, but few efforts have attempted to distinguish anthropogenic wetland loss from climate-driven variability in wetland extent. We present an approach to concurrently track land cover disturbance and inundation extent across the Mid-Atlantic region, United States, using the Landsat archive in Google Earth Engine. Disturbance was identified as a change in greenness, using a harmonic linear regression approach, or as a change in growing season brightness. Inundation extent was mapped using a modified version of the U.S. Geological Survey’s Dynamic Surface Water Extent (DSWE) algorithm. Annual (2015–2018) disturbance averaged 0.32% (1095 km² year^-1) of the study area per year and was most common in forested areas. While inundation extent showed substantial interannual variability, the co-occurrence of disturbance and declines in inundation extent represented a minority of both change types, totaling 109 km² over the four-year period, and 186 km², using the National Wetland Inventory dataset in place of the Landsat-derived inundation extent. When the annual products were evaluated with permitted wetland and stream fill points, 95% of the fill points were detected, with most found by the disturbance product (89%) and fewer found by the inundation decline product (25%). The results suggest that mapping inundation alone is unlikely to be adequate to find and track anthropogenic wetland loss. Alternatively, remotely tracking both disturbance and inundation can potentially focus efforts to protect, manage, and restore wetlands. Full article

(This article belongs to the Special Issue Applying Earth Surface Monitoring to Investigate Climate and Land Change Interactions)

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26 pages, 6603 KiB

Open AccessArticle

Drought Sensitivity and Trends of Riparian Vegetation Vigor in Nevada, USA (1985–2018)

by Christine M. Albano, Kenneth C. McGwire, Mark B. Hausner, Daniel J. McEvoy, Charles G. Morton and Justin L. Huntington

Remote Sens. 2020, 12(9), 1362; https://doi.org/10.3390/rs12091362 - 25 Apr 2020

Cited by 25 | Viewed by 5220

Abstract

Dryland riparian areas are under increasing stress due to expanding human water demands and a warming climate. Quantifying responses of dryland riparian vegetation to these pressures is complicated by high climatic variability, which can create strong, transient changes in vegetation vigor that could mask other disturbance events. In this study, we utilize a 34-year archive of Landsat satellite data to (1) quantify the strength and timescales of vegetation responses to interannual variability in drought status and (2) isolate and remove this influence to assess resultant trends in vegetation vigor for riparian areas across the state of Nevada, the driest state in the USA. Correlations between annual late-summer Normalized Difference Vegetation Index (NDVI) and the Standardized Precipitation–Evapotranspiration Index (SPEI) were calculated across a range of time periods (varying timing and durations) for all riparian pixels within each of the 45 ecoregions, and the variability of these values across the study area is shown. We then applied a novel drought adjustment method that used the strongest SPEI–NDVI timescale relationships for each ecoregion to remove the influence of interannual drought status. Our key result is a 30 m resolution map of drought-adjusted riparian NDVI trends (1985–2018). We highlight and describe locations where impacts of invasive species biocontrol, mine water management, agriculture, changing water levels, and fire are readily visualized with our results. We found more negatively trending riparian areas in association with wide valley bottoms, low-intensity agricultural land uses, and private land ownerships and more positive trends in association with narrow drainages, public lands, and surrounding perennial water bodies (an indication of declining water levels allowing increased vegetative cover). The drought-adjusted NDVI improved the statistical significance of trend estimates, thereby improving the ability to detect such changes. Results from this study provide insight into the strength and timescales of riparian vegetation responses to drought and can provide important information for managing riparian areas within the study area. The novel approach to drought adjustment is readily transferrable to other regions. Full article

(This article belongs to the Special Issue Applying Earth Surface Monitoring to Investigate Climate and Land Change Interactions)

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19 pages, 3967 KiB

Open AccessArticle

Implementation of a Surface Water Extent Model in Cambodia using Cloud-Based Remote Sensing

by Christopher E. Soulard, Jessica J. Walker and Roy E. Petrakis

Remote Sens. 2020, 12(6), 984; https://doi.org/10.3390/rs12060984 - 19 Mar 2020

Cited by 15 | Viewed by 4759

Abstract

Mapping surface water over time provides the spatially explicit information essential for hydroclimatic research focused on droughts and flooding. Hazard risk assessments and water management planning also rely on accurate, long-term measurements describing hydrologic fluctuations. Stream gages are a common measurement tool used to better understand flow and inundation dynamics, but gage networks are incomplete or non-existent in many parts of the world. In such instances, satellite imagery may provide the only data available to monitor surface water changes over time. Here, we describe an effort to extend the applicability of the USGS Dynamic Surface Water Extent (DSWE) model to non-US regions. We leverage the multi-decadal archive of the Landsat satellite in the Google Earth Engine (GEE) cloud-based computing platform to produce and analyze 372 monthly composite maps and 31 annual maps (January 1988–December 2018) in Cambodia, a flood-prone country in Southeast Asia that lacks a comprehensive stream gage network. DSWE relies on a series of spectral water indices and elevation data to classify water into four categories of water inundation. We compared model outputs to existing surface water maps and independently assessed DSWE accuracy at discrete dates across the time series. Despite considerable cloud obstruction and missing imagery across the monthly time series, the overall accuracy exceeded 85% for all annual tests. The DSWE model consistently mapped open water with high accuracy, and areas classified as “high confidence” water correlate well to other available maps at the country scale. Results in Cambodia suggest that extending DSWE globally using a cloud computing framework may benefit scientists, managers, and planners in a wide array of applications across the globe. Full article

(This article belongs to the Special Issue Applying Earth Surface Monitoring to Investigate Climate and Land Change Interactions)

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20 pages, 6617 KiB

Open AccessArticle

Mapping Evapotranspiration, Vegetation and Precipitation Trends in the Catchment of the Shrinking Lake Poopó

by Juan Torres-Batlló, Belén Martí-Cardona and Ramiro Pillco-Zolá

Remote Sens. 2020, 12(1), 73; https://doi.org/10.3390/rs12010073 - 24 Dec 2019

Cited by 14 | Viewed by 5801

Abstract

Lake Poopó is located in the Andean Mountain Range Plateau or Altiplano. A general decline in the lake water level has been observed in the last two decades, coinciding roughly with an intensification of agriculture exploitation, such as quinoa crops. Several factors have been linked with the shrinkage of the lake, including climate change, increased irrigation, mining extraction and population growth. Being an endorheic catchment, evapotranspiration (ET) losses are expected to be the main water output mechanism and previous studies demonstrated ET increases using Earth observation (EO) data. In this study, we seek to build upon these earlier findings by analyzing an ET time series dataset of higher spatial and temporal resolution, in conjunction with land cover and precipitation data. More specifically, we performed a spatio-temporal analysis, focusing on wet and dry periods, that showed that ET changes occur primarily in the wet period, while the dry period is approximately stationary. An analysis of vegetation trends performed using 500 MODIS vegetation index products (NDVI) also showed an overall increasing trend during the wet period. Analysis of NDVI and ET across land cover types showed that only croplands had experienced an increase in NDVI and ET losses, while natural covers showed either constant or decreasing NDVI trends together with increases in ET. The larger increase in vegetation and ET losses over agricultural regions, strongly suggests that cropping practices exacerbated water losses in these areas. This quantification provides essential information for the sustainable planning of water resources and land uses in the catchment. Finally, we examined the spatio-temporal trends of the precipitation using the newly available Climate Hazards Group Infrared Precipitation with Stations (CHIRPS-v2) product, which we validated with onsite rainfall measurements. When integrated over the entire catchment, precipitation and ET showed an average increasing trend of 5.2 mm yr⁻¹ and 4.3 mm yr⁻¹, respectively. This result suggests that, despite the increased ET losses, the catchment-wide water storage should have been offset by the higher precipitation. However, this result is only applicable to the catchment-wide water balance, and the location of water may have been altered (e.g., by river abstractions or by the creation of impoundments) to the detriment of the Lake Poopó downstream. Full article

(This article belongs to the Special Issue Applying Earth Surface Monitoring to Investigate Climate and Land Change Interactions)

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18 pages, 2336 KiB

Open AccessFeature PaperArticle

Phenology Patterns Indicate Recovery Trajectories of Ponderosa Pine Forests After High-Severity Fires

by Jessica J. Walker and Christopher E. Soulard

Remote Sens. 2019, 11(23), 2782; https://doi.org/10.3390/rs11232782 - 26 Nov 2019

Cited by 11 | Viewed by 3453

Abstract

Post-fire recovery trajectories in ponderosa pine (Pinus ponderosa Laws.) forests of the southwestern United States are increasingly shifting away from pre-burn vegetation communities. This study investigated whether phenological metrics derived from a multi-decade remotely sensed imagery time-series could differentiate among grass, evergreen shrub, deciduous, or conifer-dominated replacement pathways. We focused on 10 fires that burned ponderosa pine forests in Arizona and New Mexico, USA before the year 2000. A total of 29 sites with discernable post-fire recovery signals were selected within high-severity burn areas. At each site, we used Google Earth Engine to derive time-series of normalized difference vegetation index (NDVI) signals from Landsat Thematic Mapper, Enhanced Thematic Mapper Plus, and Operational Land Imager data from 1984 to 2017. We aggregated values to 8- and 16-day intervals, fit Savitzky–Golay filters to each sequence, and extracted annual phenology metrics of amplitude, base value, peak value, and timing of peak value in the TIMESAT analysis package. Results showed that relative to post-fire conditions, pre-burn ponderosa pine forests exhibit significantly lower mean NDVI amplitude (0.14 vs. 0.21), higher mean base NDVI (0.47 vs. 0.22), higher mean peak NDVI (0.60 vs. 0.43), and later mean peak NDVI (day of year 277 vs. 237). Vegetation succession pathways exhibit distinct phenometric characteristics as early as year 5 (amplitude) and as late as year 20 (timing of peak NDVI). This study confirms the feasibility of leveraging phenology metrics derived from long-term imagery time-series to identify and monitor ecological outcomes. This information may be of benefit to land resource managers who seek indicators of future landscape compositions to inform management strategies. Full article

(This article belongs to the Special Issue Applying Earth Surface Monitoring to Investigate Climate and Land Change Interactions)

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18 pages, 4538 KiB

Open AccessFeature PaperArticle

Landsat Time Series Assessment of Invasive Annual Grasses Following Energy Development

by Miguel L. Villarreal, Christopher E. Soulard and Eric K. Waller

Remote Sens. 2019, 11(21), 2553; https://doi.org/10.3390/rs11212553 - 30 Oct 2019

Cited by 9 | Viewed by 3793

Abstract

Invasive annual grasses are of concern in much of the western United States because they tolerate resource variability and have high reproductive capacity, with propagules that are readily dispersed in disturbed areas like those created and maintained for energy development. Early season invasive grasses “green up” earlier than most native plants, producing a distinct pulse of greenness in the early spring that can be exploited to identify their location using multi-date imagery. To determine if invasive annual grasses increased around energy developments after the construction phase, we calculated an invasives index using Landsat TM and ETM+ imagery for a 34-year time period (1985–2018) and assessed trends for 1755 wind turbines installed between 1988 and 2013 in the southern California desert. The index uses the maximum Normalized Difference Vegetation Index (NDVI) for early season greenness (January-June), and mean NDVI (July–October) for the later dry season. We estimated the relative cover of invasive annuals each year at turbine locations and control sites and tested for changes before and after each turbine was installed. The time series was also mapped across the region and temporal trends were assessed relative to seasonal precipitation. The results showed an increase in early season invasives at turbine sites after installation, but also an increase in many of the surrounding control areas. Maps of the invasive index show a region-wide increase starting around 1998, and much of the increase occurred in areas surrounding wind development sites. These results suggest that invasions around the energy developments occurred within the context of a larger regional invasion, and while the development did not necessarily initiate the invasion, annual grasses were more prevalent around the developments. Full article

(This article belongs to the Special Issue Applying Earth Surface Monitoring to Investigate Climate and Land Change Interactions)

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20 pages, 40798 KiB

Open AccessArticle

Where’s the Rock: Using Convolutional Neural Networks to Improve Land Cover Classification

by Helen Petliak, Corina Cerovski-Darriau, Vadim Zaliva and Jonathan Stock

Remote Sens. 2019, 11(19), 2211; https://doi.org/10.3390/rs11192211 - 21 Sep 2019

Cited by 7 | Viewed by 5343

Abstract

While machine learning techniques have been increasingly applied to land cover classification problems, these techniques have not focused on separating exposed bare rock from soil covered areas. Therefore, we built a convolutional neural network (CNN) to differentiate exposed bare rock (rock) from soil cover (other). We made a training dataset by mapping exposed rock at eight test sites across the Sierra Nevada Mountains (California, USA) using USDA’s 0.6 m National Aerial Inventory Program (NAIP) orthoimagery. These areas were then used to train and test the CNN. The resulting machine learning approach classifies bare rock in NAIP orthoimagery with a 0.95

F_{1}

score. Comparatively, the classical OBIA approach gives only a 0.84

F_{1}

score. This is an improvement over existing land cover maps, which underestimate rock by almost 90%. The resulting CNN approach is likely scalable but dependent on high-quality imagery and high-performance algorithms using representative training sets informed by expert mapping. As image quality and quantity continue to increase globally, machine learning models that incorporate high-quality training data informed by geologic, topographic, or other topical maps may be applied to more effectively identify exposed rock in large image collections. Full article

(This article belongs to the Special Issue Applying Earth Surface Monitoring to Investigate Climate and Land Change Interactions)

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15 pages, 3692 KiB

Open AccessArticle

Coupled Biospheric Synchrony of the Coastal Temperate Ecosystem in Northern Patagonia: A Remote Sensing Analysis

by Carlos Lara, Bernard Cazelles, Gonzalo S. Saldías, Raúl P. Flores, Álvaro L. Paredes and Bernardo R. Broitman

Remote Sens. 2019, 11(18), 2092; https://doi.org/10.3390/rs11182092 - 7 Sep 2019

Cited by 8 | Viewed by 2678

Abstract

Over the last century, climate change has impacted the physiology, distribution, and phenology of marine and terrestrial primary producers worldwide. The study of these fluctuations has been hindered due to the complex response of plants to environmental forcing over large spatial and temporal scales. To bridge this gap, we investigated the synchrony in seasonal phenological activity between marine and terrestrial primary producers to environmental and climatic variability across northern Patagonia. We disentangled the effects on the biological activity of local processes using advanced time-frequency analysis and partial wavelet coherence on 15 years (2003–2017) of data from MODIS (Moderate Resolution Imaging Spectroradiometer) onboard the Terra and Aqua satellites and global climatic variability using large-scale climate indices. Our results show that periodic variations in both coastal ocean and land productivity are associated with sea surface temperature forcing over seasonal scales and with climatic forcing over multi-annual (2–4 years) modes. These complex relationships indicate that large-scale climatic processes primarily modulate the synchronous phenological seasonal activity across northern Patagonia, which makes these unique ecosystems highly exposed to future climatic change. Full article

(This article belongs to the Special Issue Applying Earth Surface Monitoring to Investigate Climate and Land Change Interactions)

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