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Advancements in Remote Sensing of Land Surface Change

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 42679

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Special Issue Editors

Dr. George Xian

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Guest Editor

USGS Center for Earth Resources Observation and Science, Sioux Falls, SD 57198, USA
Interests: remote sensing; land change; urban; urban heat island
Special Issues, Collections and Topics in MDPI journals

Dr. Robert Kennedy

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Guest Editor

Department of Forest Science, Oregon State University, Corvallis, OR 97331-5503, USA
Interests: geospatial analysis; remote sensing; modeling; landscape ecology; disturbance dynamics; computational methods

Dr. Yuyu Zhou

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Guest Editor

Department of Geological & Atmospheric Sciences, Iowa State University, 3019 Agronomy Hall, Ames, IA 50011, USA
Interests: urban remote sensing; urbanization; urban heat island; building energy use; vegetation phenology
Special Issues, Collections and Topics in MDPI journals

Dr. Seth Munson

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Guest Editor

US Geological Survey, Southwest Biological Science Center, Flagstaff, AZ 86001, USA
Interests: plant ecology; ecosystem ecology; landscape ecology; restoration ecology

Special Issue Information

Dear Colleagues,

Remote sensing information has been used in a wide range of earth science research and applications, such as over land, water, ecosystems, and geology. Remote sensing-derived land change information has been applied to quantify and model physical properties of the Earth’s surface, as well as to monitor land cover and use changes. Satellite sensors, such as the Landsat series of satellites (1980s to the present), ASTER and MODIS sensors on Terra (1999 to the present), MODIS on Aqua (2002 to the present), and VIIRS (2012 to the present), have routinely provided remotely sensed imagery of Earth’s surface condition, allowing for change assessment. With recent advances in remote sensing technologies, multiple remotely sensed data products are readily available to the scientific community with the potential to advance our scientific understanding of various dynamic processes associated with the terrestrial ecosystem.

This Special Issue invites manuscripts that focus on advancements in methodologies relating to and new knowledge gained by using remote sensing datasets to characterize land surface changes across large geographical areas and assess how ecosystem processes respond to land use and climate change. Topics on overcoming the challenges of using these data and advancements in understanding dynamic land processes, including the types, trends, magnitudes, causes, and consequences of land surface change and ecosystem responses, will also be considered.

Dr. George Xian
Dr. Robert Kennedy
Dr. Yuyu Zhou
Dr. Seth Munson
Guest Editors

Manuscript Submission Information

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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.

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Keywords

land surface
change
remote sensing
monitoring
ecosystem
climate

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Published Papers (11 papers)

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Research

21 pages, 8542 KiB

Open AccessArticle

Spatial-Temporal Distribution of the Freeze–Thaw Cycle of the Largest Lake (Qinghai Lake) in China Based on Machine Learning and MODIS from 2000 to 2020

by Weixiao Han, Chunlin Huang, Juan Gu, Jinliang Hou and Ying Zhang

Remote Sens. 2021, 13(9), 1695; https://doi.org/10.3390/rs13091695 - 27 Apr 2021

Cited by 8 | Viewed by 2530

Abstract

The lake ice phenology variations are vital for the land–surface–water cycle. Qinghai Lake is experiencing amplified warming under climate change. Based on the MODIS imagery, the spatio-temporal dynamics of the ice phenology of Qinghai Lake were analyzed using machine learning during the 2000/2001 to 2019/2020 ice season, and cloud gap-filling procedures were applied to reconstruct the result. The results showed that the overall accuracy of the water–ice classification by random forest and cloud gap-filling procedures was 98.36% and 92.56%, respectively. The annual spatial distribution of the freeze-up and break-up dates ranged primarily from DOY 330 to 397 and from DOY 70 to 116. Meanwhile, the decrease rates of freeze-up duration (DFU), full ice cover duration (DFI), and ice cover duration (DI) were 0.37, 0.34, and 0.13 days/yr., respectively, and the duration was shortened by 7.4, 6.8, and 2.6 days over the past 20 years. The increased rate of break-up duration (DBU) was 0.58 days/yr. and the duration was lengthened by 11.6 days. Furthermore, the increase in temperature resulted in an increase in precipitation after two years; the increase in precipitation resulted in the increase in DBU and decrease in DFU in corresponding years, and decreased DI and DFI after one year. Full article

(This article belongs to the Special Issue Advancements in Remote Sensing of Land Surface Change)

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14 pages, 5770 KiB

Open AccessArticle

Climate and Socioeconomic Factors Drive Irrigated Agriculture Dynamics in the Lower Colorado River Basin

by Cynthia L. Norton, Matthew P. Dannenberg, Dong Yan, Cynthia S. A. Wallace, Jesus R. Rodriguez, Seth M. Munson, Willem J. D. van Leeuwen and William K. Smith

Remote Sens. 2021, 13(9), 1659; https://doi.org/10.3390/rs13091659 - 24 Apr 2021

Cited by 7 | Viewed by 3899

Abstract

The Colorado River Basin (CRB) includes seven states and provides municipal and industrial water to millions of people across all major southwestern cities both inside and outside the basin. Agriculture is the largest part of the CRB economy and crop production depends on irrigation, which accounts for about 74% of the total water demand cross the region. A better understanding of irrigation water demands is critically needed as temperatures continue to rise and drought intensifies, potentially leading to water shortages across the region. Yet, past research on irrigation dynamics has generally utilized relatively low spatiotemporal resolution datasets and has often overlooked the relationship between climate and management decisions such as land fallowing, i.e., the practice of leaving cultivated land idle for a growing season. Here, we produced annual estimates of fallow and active cropland extent at high spatial resolution (30 m) from 2001 to 2017 by applying the fallow-land algorithm based on neighborhood and temporal anomalies (FANTA). We specifically focused on diverse CRB agricultural regions: the lower Colorado River planning (LCRP) area and the Pinal and Phoenix active management areas (PPAMA). Utilizing ground observations collected in 2014 and 2017, we found an overall classification accuracy of 88.9% and 87.2% for LCRP and PPAMA, respectively. We then quantified how factors such as climate, district water rights, and market value influenced: (1) annual fallow and active cropland extent and (2) annual cropland productivity, approximated by integrated growing season NDVI (iNDVI). We found that for the LCRP, a region of winter cropping and senior (i.e., preferential) water rights, active cropland productivity was positively correlated with cool-season average vapor pressure deficit (R = 0.72; p < 0.01). By contrast, for the PPAMA, a region of summer cropping and junior water rights, annual fallow and active cropland extent was positively correlated with cool-season aridity (precipitation/potential evapotranspiration) (R = 0.46; p < 0.05), and active cropland productivity was positively correlated with warm-season aridity (precipitation/potential evapotranspiration) (R = 0.42; p < 0.01). We also found that PPAMA cropland productivity was more sensitive to aridity when crop prices were low, potentially due to the influence of market value on management decisions. Our analysis highlights how biophysical (e.g., temperature and precipitation) and socioeconomic (e.g., water rights and crop market value) factors interact to explain seasonal patterns of cropland extent, water use and productivity. These findings indicate that increasing aridity across the region may result in reduced cropland productivity and increased land fallowing for some regions, particularly those with junior water rights. Full article

(This article belongs to the Special Issue Advancements in Remote Sensing of Land Surface Change)

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18 pages, 3033 KiB

Open AccessArticle

Quantitative Recognition and Characteristic Analysis of Production-Living-Ecological Space Evolution for Five Resource-Based Cities: Zululand, Xuzhou, Lota, Surf Coast and Ruhr

by Yuanyuan Tao and Qianxin Wang

Remote Sens. 2021, 13(8), 1563; https://doi.org/10.3390/rs13081563 - 17 Apr 2021

Cited by 38 | Viewed by 3076

Abstract

The accurate identification of PLES changes and the discovery of their evolution characteristics is a key issue to improve the ability of the sustainable development for resource-based urban areas. However, the current methods are unsuitable for the long-term and large-scale PLES investigation. In this study, a modified method of PLES recognition is proposed based on the remote sensing image classification and land function evaluation technology. A multi-dimensional index system is constructed, which can provide a comprehensive evaluation for PLES evolution characteristics. For validation of the proposed methods, the remote sensing image, geographic information, and socio-economic data of five resource-based urbans (Zululand in South Africa, Xuzhou in China, Lota in Chile, Surf Coast in Australia, and Ruhr in Germany) from 1975 to 2020 are collected and tested. The results show that the data availability and calculation efficiency are significantly improved by the proposed method, and the recognition precision is better than 87% (Kappa coefficient). Furthermore, the PLES evolution characteristics show obvious differences at the different urban development stages. The expansions of production, living, and ecological space are fastest at the mining, the initial, and the middle ecological restoration stages, respectively. However, the expansion of living space is always increasing at any stage, and the disorder expansion of living space has led to the decrease of integration of production and ecological spaces. Therefore, the active polices should be formulated to guide the transformation of the living space expansion from jumping-type and spreading-type to filling-type, and the renovation of abandoned industrial and mining lands should be encouraged. Full article

(This article belongs to the Special Issue Advancements in Remote Sensing of Land Surface Change)

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30 pages, 10162 KiB

Open AccessArticle

Historical Changes in Land Use and Suitability for Future Agriculture Expansion in Western Bahia, Brazil

by Fernando Martins Pimenta, Allan Turini Speroto, Marcos Heil Costa and Emily Ane Dionizio

Remote Sens. 2021, 13(6), 1088; https://doi.org/10.3390/rs13061088 - 12 Mar 2021

Cited by 22 | Viewed by 5210

Abstract

Western Bahia is a critical region in Brazil’s recent expansion of agricultural output. Its outstanding increase in production is associated with strong growth in cropland area and irrigation. Here we present analyses of Western Bahian historical changes in land use, including irrigated area, and suitability for future agricultural expansion that respects permanent protection areas and the limits established by the Brazilian Forest Code in the Cerrado biome. For this purpose, we developed a land use and land cover classification database using a random forest classifier and Landsat images. A spatial multicriteria decision analysis to evaluate land suitability was performed by combining this database with precipitation and slope data. We demonstrate that between 1990 and 2020, the region’s total agricultural area increased by 3.17 Mha and the irrigated area increased by 193,480 ha. Throughout the region, the transition between the different classes of land use and land cover followed different pathways and was strongly influenced by land suitability and also appears to be influenced by Brazil’s new Forest Code of 2012. We conclude that even if conservation restrictions are considered, agricultural area could nearly double in the region, with expansion possible mostly in areas we classify as moderately suitable for agriculture, which are subject to climate hazards when used for rainfed crops but are otherwise fine for pastures and irrigated croplands. Full article

(This article belongs to the Special Issue Advancements in Remote Sensing of Land Surface Change)

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17 pages, 9294 KiB

Open AccessArticle

A Weighted-Time-Lag Method to Detect Lag Vegetation Response to Climate Variation: A Case Study in Loess Plateau, China, 1982–2013

by Qianqian Sun, Chao Liu, Tianyang Chen and Anbing Zhang

Remote Sens. 2021, 13(5), 923; https://doi.org/10.3390/rs13050923 - 2 Mar 2021

Cited by 18 | Viewed by 2950

Abstract

Vegetation fluctuation is sensitive to climate change, and this response exhibits a time lag. Traditionally, scholars estimated this lag effect by considering the immediate prior lag (e.g., where vegetation in the current month is impacted by the climate in a certain prior month) or the lag accumulation (e.g., where vegetation in the current month is impacted by the last several months). The essence of these two methods is that vegetation growth is impacted by climate conditions in the prior period or several consecutive previous periods, which fails to consider the different impacts coming from each of those prior periods. Therefore, this study proposed a new approach, the weighted time-lag method, in detecting the lag effect of climate conditions coming from different prior periods. Essentially, the new method is a generalized extension of the lag-accumulation method. However, the new method detects how many prior periods need to be considered and, most importantly, the differentiated climate impact on vegetation growth in each of the determined prior periods. We tested the performance of the new method in the Loess Plateau by comparing various lag detection methods by using the linear model between the climate factors and the normalized difference vegetation index (NDVI). The case study confirmed four main findings: (1) the response of vegetation growth exhibits time lag to both precipitation and temperature; (2) there are apparent differences in the time lag effect detected by various methods, but the weighted time-lag method produced the highest determination coefficient (R²) in the linear model and provided the most specific lag pattern over the determined prior periods; (3) the vegetation growth is most sensitive to climate factors in the current month and the last month in the Loess Plateau but reflects a varied of responses to other prior months; and (4) the impact of temperature on vegetation growth is higher than that of precipitation. The new method provides a much more precise detection of the lag effect of climate change on vegetation growth and makes a smart decision about soil conservation and ecological restoration after severe climate events, such as long-lasting drought or flooding. Full article

(This article belongs to the Special Issue Advancements in Remote Sensing of Land Surface Change)

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16 pages, 2989 KiB

Open AccessArticle

Mitigation of Arctic Tundra Surface Warming by Plant Evapotranspiration: Complete Energy Balance Component Estimation Using LANDSAT Satellite Data

by Václav Nedbal, Kamil Láska and Jakub Brom

Remote Sens. 2020, 12(20), 3395; https://doi.org/10.3390/rs12203395 - 16 Oct 2020

Cited by 8 | Viewed by 2763

Abstract

Global climate change is expected to cause a strong temperature increase in the polar regions, accompanied by a reduction in snow cover. Due to a lower albedo, bare ground absorbs more solar energy and its temperature can increase more. Here, we show that vegetation growth in such bare ground areas can efficiently mitigate surface warming in the Arctic, thanks to plant evapotranspiration. In order to establish a comprehensive energy balance for the Arctic land surface, we used an ensemble of methods of ground-based measurements and multispectral satellite image analysis. Our estimate is that the low vegetation of polar tundra transforms 26% more solar energy into evapotranspiration than bare ground in clear sky weather. Due to its isolation properties, vegetation further reduces ground heat flux under the surface by ~4%, compared to bare areas, thus lowering the increase in subsurface temperature. As a result, ~22% less solar energy can be transformed into sensible heat flux at vegetated surfaces as opposed to bare ground, bringing about a decrease in surface temperature of ~7.8 °C. Full article

(This article belongs to the Special Issue Advancements in Remote Sensing of Land Surface Change)

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18 pages, 11803 KiB

Open AccessArticle

Methods of Rapid Quality Assessment for National-Scale Land Surface Change Monitoring

by Qiang Zhou, Christopher Barber and George Xian

Remote Sens. 2020, 12(16), 2524; https://doi.org/10.3390/rs12162524 - 6 Aug 2020

Cited by 3 | Viewed by 3229

Abstract

Providing rapid access to land surface change data and information is a goal of the U.S. Geological Survey. Through the Land Change Monitoring, Assessment, and Projection (LCMAP) initiative, we have initiated a monitoring capability that involves generating a suite of 10 annual land cover and land surface change datasets across the United States at a 30-m spatial resolution. During the LCMAP automated production, on a tile-by-tile basis, erroneous data can occasionally be generated due to hardware or software failure. While crucial to assure the quality of the data, rapid evaluation of results at the pixel level during production is a substantial challenge because of the massive data volumes. Traditionally, product quality relies on the validation after production, which is inefficient to reproduce the whole product when an error occurs. This paper presents a method for automatically evaluating LCMAP results during the production phase based on 14 indices to quickly find and flag erroneous tiles in the LCMAP products. The methods involved two types of comparisons: comparing LCMAP values across the temporal record to measure internal consistency and calculating the agreement with multiple intervals of the National Land Cover Database (NLCD) data to measure the consistency with existing products. We developed indices on a tile-by-tile basis in order to quickly find and flag potential erroneous tiles by comparing with surrounding tiles using local outlier factor analysis. The analysis integrates all indices into a local outlier score (LOS) to detect erroneous tiles that are distinct from neighboring tiles. Our analysis showed that the methods were sensitive to partially erroneous tiles in the simulated data with a LOS higher than 2. The rapid quality assessment methods also successfully identified erroneous tiles during the LCMAP production, in which land surface change results were not properly saved to the products. The LOS map and indices for rapid quality assessment also point to directions for further investigations. A map of all LOS values by tile for the published LCMAP shows all LOS values are below 2. We also investigated tiles with high LOS to ensure the distinction with neighboring tiles was reasonable. An index in this study shows the overall agreement between LCMAP and NLCD on a tile basis is above 71.5% and has an average at 89.1% across the 422 tiles in the conterminous United States. The workflow is suitable for other studies with a large volume of image products. Full article

(This article belongs to the Special Issue Advancements in Remote Sensing of Land Surface Change)

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22 pages, 2853 KiB

Open AccessArticle

Application of Empirical Land-Cover Changes to Construct Climate Change Scenarios in Federally Managed Lands

by Christopher E. Soulard and Matthew Rigge

Remote Sens. 2020, 12(15), 2360; https://doi.org/10.3390/rs12152360 - 23 Jul 2020

Cited by 3 | Viewed by 3012

Abstract

Sagebrush-dominant ecosystems in the western United States are highly vulnerable to climatic variability. To understand how these ecosystems will respond under potential future conditions, we correlated changes in National Land Cover Dataset “Back-in-Time” fractional cover maps from 1985-2018 with Daymet climate data in three federally managed preserves in the sagebrush steppe ecosystem: Beaty Butte Herd Management Area, Hart Mountain National Antelope Refuge, and Sheldon National Wildlife Refuge. Future (2018 to 2050) abundance and distribution of vegetation cover were modeled at a 300-m resolution under a business-as-usual climate (BAU) scenario and a Representative Concentration Pathway (RCP) 8.5 climate change scenario. Spatially explicit map projections suggest that climate influences may make the landscape more homogeneous in the near future. Specifically, projections indicate that pixels with high bare ground cover become less bare ground dominant, pixels with moderate herbaceous cover contain less herbaceous cover, and pixels with low shrub cover contain more shrub cover. General vegetation patterns and composition do not differ dramatically between scenarios despite RCP 8.5 projections of +1.2 °C mean annual minimum temperatures and +7.6 mm total annual precipitation. Hart Mountain National Antelope Refuge is forecast to undergo the most change, with both models projecting larger declines in bare ground and larger increases in average herbaceous and shrub cover compared to Beaty Butte Herd Management Area and Sheldon National Wildlife Refuge. These scenarios present plausible future outcomes intended to guide federal land managers to identify vegetation cover changes that may affect habitat condition and availability for species of interest. Full article

(This article belongs to the Special Issue Advancements in Remote Sensing of Land Surface Change)

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31 pages, 14150 KiB

Open AccessEditor’s ChoiceArticle

Land-Cover Changes to Surface-Water Buffers in the Midwestern USA: 25 Years of Landsat Data Analyses (1993–2017)

by Tedros M. Berhane, Charles R. Lane, Samson G. Mengistu, Jay Christensen, Heather E. Golden, Shi Qiu, Zhe Zhu and Qiusheng Wu

Remote Sens. 2020, 12(5), 754; https://doi.org/10.3390/rs12050754 - 25 Feb 2020

Cited by 16 | Viewed by 5704

Abstract

To understand the timing, extent, and magnitude of land use/land cover (LULC) change in buffer areas surrounding Midwestern US waters, we analyzed the full imagery archive (1982–2017) of three Landsat footprints covering ~100,000 km². The study area included urbanizing Chicago, Illinois and St. Louis, Missouri regions and agriculturally dominated landscapes (i.e., Peoria, Illinois). The Continuous Change Detection and Classification algorithm identified 1993–2017 LULC change across three Landsat footprints and in 90 m buffers for ~110,000 surface waters; waters were also size-binned into five groups for buffer LULC change analyses. Importantly, buffer-area LULC change magnitude was frequently much greater than footprint-level change. Surface-water extent in buffers increased by 14–35x the footprint rate and forest decreased by 2–9x. Development in buffering areas increased by 2–4x the footprint-rate in Chicago and Peoria area footprints but was similar to the change rate in the St. Louis area footprint. The LULC buffer-area change varied in waterbody size, with the greatest change typically occurring in the smallest waters (e.g., <0.1 ha). These novel analyses suggest that surface-water buffer LULC change is occurring more rapidly than footprint-level change, likely modifying the hydrology, water quality, and biotic integrity of existing water resources, as well as potentially affecting down-gradient, watershed-scale storages and flows of water, solutes, and particulate matter. Full article

(This article belongs to the Special Issue Advancements in Remote Sensing of Land Surface Change)

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21 pages, 5834 KiB

Open AccessArticle

Remotely Sensed Mid-Channel Bar Dynamics in Downstream of the Three Gorges Dam, China

by Zhaofei Wen, Hong Yang, Ce Zhang, Guofan Shao and Shengjun Wu

Remote Sens. 2020, 12(3), 409; https://doi.org/10.3390/rs12030409 - 28 Jan 2020

Cited by 26 | Viewed by 4849

Abstract

The downstream reach of the Three Gorges Dam (TGD) along the Yangtze River (1560 km) hosts numerous mid-channel bars (MCBs). MCBs dynamics are crucial to the river’s hydrological processes and local ecological function. However, a systematic understanding of such dynamics and their linkage to TGD remains largely unknown. Using Landsat-image-extracted MCBs and several spatial-temporal analysis methods, this study presents a comprehensive understanding of MCB dynamics in terms of number, area, and shape, over downstream of TGD during the period 1985–2018. On average, a total of 140 MCBs were detected and grouped into four types representing small (<2 km²), middle (2 km² – 7 km²), large (7 km² – 33 km²) and extra-large size (>33 km²) MCBs, respectively. MCBs number decreased after TGD closure but most of these happened in the lower reach. The area of total MCBs experienced an increasing trend (2.77 km²/yr, p-value < 0.01) over the last three decades. The extra-large MCBs gained the largest area increasing rate than the other sizes of MCBs. Small MCBs tended to become relatively round, whereas the others became elongate in shape after TGD operation. Impacts of TGD operation generally diminished in the longitudinal direction from TGD to Hankou and from TGD to Jiujiang for shape and area dynamics, respectively. The quantified longitudinal and temporal dynamics of MCBs across the entire Yangtze River downstream of TGD provides a crucial monitoring basis for continuous investigation of the changing mechanisms affecting the morphology of the Yangtze River system. Full article

(This article belongs to the Special Issue Advancements in Remote Sensing of Land Surface Change)

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23 pages, 7625 KiB

Open AccessArticle

Assessment of the Impacts of Image Signal-to-Noise Ratios in Impervious Surface Mapping

by George Xian, Hua Shi, Cody Anderson and Zhuoting Wu

Remote Sens. 2019, 11(22), 2603; https://doi.org/10.3390/rs11222603 - 6 Nov 2019

Cited by 3 | Viewed by 3784

Abstract

Medium spatial resolution satellite images are frequently used to characterize thematic land cover and a continuous field at both regional and global scales. However, high spatial resolution remote sensing data can provide details in landscape structures, especially in the urban environment. With upgrades to spatial resolution and spectral coverage for many satellite sensors, the impact of the signal-to-noise ratio (SNR) in characterizing a landscape with highly heterogeneous features at the sub-pixel level is still uncertain. This study used WorldView-3 (WV3) images as a basis to evaluate the impacts of SNR on mapping a fractional developed impervious surface area (ISA). The point spread function (PSF) from the Landsat 8 Operational Land Imager (OLI) was used to resample the WV3 images to three different resolutions: 10 m, 20 m, and 30 m. Noise was then added to the resampled WV3 images to simulate different fractional levels of OLI SNRs. Furthermore, regression tree algorithms were incorporated into these images to estimate the ISA at different spatial scales. The study results showed that the total areal estimate could be improved by about 1% and 0.4% at 10-m spatial resolutions in our two study areas when the SNR changes from half to twice that of the Landsat OLI SNR level. Such improvement is more obvious in the high imperviousness ranges. The root-mean-square-error of ISA estimates using images that have twice and two-thirds the SNRs of OLI varied consistently from high to low when spatial resolutions changed from 10 m to 20 m. The increase of SNR, however, did not improve the overall performance of ISA estimates at 30 m. Full article

(This article belongs to the Special Issue Advancements in Remote Sensing of Land Surface Change)

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