Special Issue "Remote Sensing Applications in Monitoring of Protected Areas"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 December 2019).

Special Issue Editors

Dr. Yeqiao Wang
E-Mail Website
Guest Editor
Department of Natural Resources Science, University of Rhode Island, 1 Greenhouse Road, Kingston, RI 02881, USA
Tel. +1 401-874-4345
Interests: Terrestrial remote sensing, protected areas; coastal environments; wetlands; inventory and monitoring of ecological conditions; land use and land cover change; biodiversity conservation; mountainous regions; decision support
Special Issues and Collections in MDPI journals
Dr. Zhong Lu
E-Mail Website
Guest Editor
Huffington Department of Earth Sciences, Southern Methodist University, Dallas, TX 75275-0395, USA
Tel. 214-768-0101
Interests: SAR; InSAR; time-series InSAR; geophysical modeling; volcanoes; landslides; geohazards
Special Issues and Collections in MDPI journals
Dr. Yongwei Sheng
E-Mail Website
Guest Editor
Department of Geography, University of California, Los Angeles (UCLA),PO Box 951524, 1255 Bunche Hall, Los Angeles, CA 90095, USA
Tel. (310) 825-1415; Fax: (310) 206-5976
Interests: hydrology; lake dynamics; water resources; vegetation monitoring; glacier changes; remote sensing; geographic information systems (GIS); Tibetan Plateau; Arctic; Central Asia
Special Issues and Collections in MDPI journals
Dr. Yuyu Zhou
E-Mail Website
Guest Editor
Department of Geological and Atmospheric Sciences, Iowa State University, Ames, IA 50011-1051, USA
Interests: ecosystem and human system dynamics; phenology indicators urban heat island; climate change; urbanization
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing has been successfully applied in the inventory and monitoring of protected areas around the world. Protected areas include national parks, national forests, all level of natural preserves and designated areas for the conservation of biological diversity and cultural and natural significance. Protected areas also include frontier and wilderness areas that are among the treasures of the natural world. Some protected areas are the only places that contain undisturbed landscapes and ecosystems on the planet. Along coasts and across oceans, protected areas include national seashores and marine protected areas that encompass intertidal or subtidal terrain, together with their overlying water and associated flora, fauna, historical and cultural features. With intensified impacts of climate and environmental change, protected areas are becoming more important in terms of serving as indicators of ecosystem status and function. A study reported recently that “Earth’s remaining wilderness areas are becoming increasingly important buffers against changing conditions in the Anthropocene. Yet they aren’t an explicit target in international policy frameworks (Watson et al., Nature, Vol. 563, 2018).” Remote sensing plays an irreplaceable role on the frontline of these challenges.

With the rapid development of remote sensing science and technologies, this Special Issue aims to publish original manuscripts on the latest innovative research and advancement in the remote sensing of protected areas. Comprehensive reviews of this research field are also welcome. Potential topics include, but are not limited to:

  • State-of-the-art remote sensing technologies to capture the dynamics of ecosystem variations.
  • Evaluations of newly available sensors, data, and new development of integrated approaches.
  • Methods for processing advanced remote sensing and time series data.
  • Integration of multisource and open source data, such as from in situ measurements, UAV observations, Google Earth Engine, habitat assessments, social economic development, policy factors, and citizen science in inventory and monitoring practices.
  • Applications of remote sensing in topics, such as biospheric, atmospheric, hydrospheric and societal dimensions of protected lands, habitat mapping and biodiversity conservation, impacts of climate change, detection of effects from extreme natural and anthoropogenic disturbances, as well as uncertainty, mitigation, resilience and sustainability of protected areas under changing environments.

Contributors are required to check the website below and follow the specific instructions for authors. https://www.mdpi.com/journal/remotesensing/instructions

Dr. Yeqiao Wang
Dr. Zhong Lu
Dr. Yongwei Sheng
Dr. Yuyu Zhou
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 papers will be 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 2000 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

  • Protected areas
  • New sensor applications
  • Dynamic simulation modeling
  • Climate change
  • Ecosystem functions and services
  • Biodiversity conservation
  • Resiliance and sustainability
  • Natural hazards and human disturbances
  • Socio-economic indicators
  • Governance, management and decision support

Published Papers (8 papers)

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Research

Open AccessArticle
Mapping Spatial Variations of Structure and Function Parameters for Forest Condition Assessment of the Changbai Mountain National Nature Reserve
Remote Sens. 2019, 11(24), 3004; https://doi.org/10.3390/rs11243004 - 13 Dec 2019
Abstract
Forest condition is the baseline information for ecological evaluation and management. The National Forest Inventory of China contains structural parameters, such as canopy closure, stand density and forest age, and functional parameters, such as stand volume and soil fertility. Conventionally forest conditions are [...] Read more.
Forest condition is the baseline information for ecological evaluation and management. The National Forest Inventory of China contains structural parameters, such as canopy closure, stand density and forest age, and functional parameters, such as stand volume and soil fertility. Conventionally forest conditions are assessed through parameters collected from field observations, which could be costly and spatially limited. It is crucial to develop modeling approaches in mapping forest assessment parameters from satellite remote sensing. This study mapped structure and function parameters for forest condition assessment in the Changbai Mountain National Nature Reserve (CMNNR). The mapping algorithms, including statistical regression, random forests, and random forest kriging, were employed with predictors from Advanced Land Observing Satellite (ALOS)-2, Sentinel-1, Sentinel-2 satellite sensors, digital surface model of ALOS, and 1803 field sampled forest plots. Combined predicted parameters and weights from principal component analysis, forest conditions were assessed. The models explained spatial dynamics and characteristics of forest parameters based on an independent validation with all r values above 0.75. The root mean square error (RMSE) values of canopy closure, stand density, stand volume, forest age and soil fertility were 4.6%, 33.8%, 29.4%, 20.5%, and 14.3%, respectively. The mean assessment score suggested that forest conditions in the CMNNR are mainly resulted from spatial variations of function parameters such as stand volume and soil fertility. This study provides a methodology on forest condition assessment at regional scales, as well as the up-to-date information for the forest ecosystem in the CMNNR. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Monitoring of Protected Areas)
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Open AccessArticle
Assessment of Night-Time Lighting for Global Terrestrial Protected and Wilderness Areas
Remote Sens. 2019, 11(22), 2699; https://doi.org/10.3390/rs11222699 - 18 Nov 2019
Abstract
Protected areas (PAs) play an important role in biodiversity conservation and ecosystem integrity. However, human development has threatened and affected the function and effectiveness of PAs. The Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) night-time stable light (NTL) data have proven to be [...] Read more.
Protected areas (PAs) play an important role in biodiversity conservation and ecosystem integrity. However, human development has threatened and affected the function and effectiveness of PAs. The Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) night-time stable light (NTL) data have proven to be an effective indicator of the intensity and change of human-induced urban development over a long time span and at a larger spatial scale. We used the NTL data from 1992 to 2013 to characterize the human-induced urban development and studied the spatial and temporal variation of the NTL of global terrestrial PAs. We selected seven types of PAs defined by the International Union for Conversation of Nature (IUCN), including strict nature reserve (Ia), wilderness area (Ib), national park (II), natural monument or feature (III), habitat/species management area (IV), protected landscape/seascape (V), and protected area with sustainable use of natural resources (VI). We evaluated the NTL digital number (DN) in PAs and their surrounding buffer zones, i.e., 0–1 km, 1–5 km, 5–10 km, 10–25 km, 25–50 km, and 50–100 km. The results revealed the level, growth rate, trend, and distribution pattern of NTL in PAs. Within PAs, areas of types V and Ib had the highest and lowest NTL levels, respectively. In the surrounding 1–100 km buffer zones, type V PAs also had the highest NTL level, but type VI PAs had the lowest NTL level. The NTL level in the areas surrounding PAs was higher than that within PAs. Types Ia and III PAs showed the highest and lowest NTL growth rate from 1992 to 2013, respectively, both inside and outside of PAs. The NTL distributions surrounding the Ib and VI PAs were different from other types. The areas close to Ib and VI boundaries, i.e., in the 0–25 km buffer zones, showed lower NTL levels, for which the highest NTL level was observed within the 25–100 km buffer zone. However, other types of PAs showed the opposite NTL patterns. The NTL level was lower in the distant buffer zones, and the lowest night light was within the 1–25 km buffer zones. Globally, 6.9% of PAs are being affected by NTL. Conditions of wilderness areas, e.g., high latitude regions, Tibetan Plateau, Amazon, and Caribbean, are the least affected by NTL. The PAs in Europe, Asia, and North America are more affected by NTL than South America, Africa, and Oceania. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Monitoring of Protected Areas)
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Open AccessArticle
A Multi-Temporal Object-Based Image Analysis to Detect Long-Lived Shrub Cover Changes in Drylands
Remote Sens. 2019, 11(22), 2649; https://doi.org/10.3390/rs11222649 - 13 Nov 2019
Abstract
Climate change and human actions condition the spatial distribution and structure of vegetation, especially in drylands. In this context, object-based image analysis (OBIA) has been used to monitor changes in vegetation, but only a few studies have related them to anthropic pressure. In [...] Read more.
Climate change and human actions condition the spatial distribution and structure of vegetation, especially in drylands. In this context, object-based image analysis (OBIA) has been used to monitor changes in vegetation, but only a few studies have related them to anthropic pressure. In this study, we assessed changes in cover, number, and shape of Ziziphus lotus shrub individuals in a coastal groundwater-dependent ecosystem in SE Spain over a period of 60 years and related them to human actions in the area. In particular, we evaluated how sand mining, groundwater extraction, and the protection of the area affect shrubs. To do this, we developed an object-based methodology that allowed us to create accurate maps (overall accuracy up to 98%) of the vegetation patches and compare the cover changes in the individuals identified in them. These changes in shrub size and shape were related to soil loss, seawater intrusion, and legal protection of the area measured by average minimum distance (AMD) and average random distance (ARD) analysis. It was found that both sand mining and seawater intrusion had a negative effect on individuals; on the contrary, the protection of the area had a positive effect on the size of the individuals’ coverage. Our findings support the use of OBIA as a successful methodology for monitoring scattered vegetation patches in drylands, key to any monitoring program aimed at vegetation preservation. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Monitoring of Protected Areas)
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Open AccessArticle
Monitoring and Assessment of Wetland Loss and Fragmentation in the Cross-Boundary Protected Area: A Case Study of Wusuli River Basin
Remote Sens. 2019, 11(21), 2581; https://doi.org/10.3390/rs11212581 - 03 Nov 2019
Abstract
Comparative evaluation of cross-boundary wetland protected areas is essential to underpin knowledge-based bilateral conservation policies and funding decisions by governments and managers. In this paper, wetland change monitoring for the Wusuli River Basin in the cross-boundary zone of China and Russia from 1990 [...] Read more.
Comparative evaluation of cross-boundary wetland protected areas is essential to underpin knowledge-based bilateral conservation policies and funding decisions by governments and managers. In this paper, wetland change monitoring for the Wusuli River Basin in the cross-boundary zone of China and Russia from 1990 to 2015 was quantitatively analyzed using Landsat images. The spatial-temporal distribution of wetlands was identified using a rule-based object-oriented classification method. Wetland dynamics were determined by combining annual land change area (ALCA), annual land change rate (ALCR), landscape metrics and spatial analysis in a geographic information system (GIS). A Mann–Kendall test was used to evaluate changing climate trends. Results showed that natural wetlands in the Wusuli River Basin have declined by 5625.76 km2 in the past 25 years, especially swamp/marsh, which decreased by 26.88%. Specifically, natural wetlands declined by 49.93% in the Chinese section but increased with an ALCA of 16.62 km2/y in the Russian section during 1990–2015. Agricultural encroachment was the most important reason for the loss and degradation of natural wetlands in the Wusuli River Basin, especially in China. Different population change trends and conservation policies in China and Russia affected natural wetland dynamics. The research offers an efficient and effective method to evaluate cross-boundary wetland change. This study provides important scientific information necessary for developing future ecological conservation and management of cross-boundary wetlands. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Monitoring of Protected Areas)
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Open AccessArticle
Spatio-Temporal Variations of Carbon Use Efficiency in Natural Terrestrial Ecosystems and the Relationship with Climatic Factors in the Songnen Plain, China
Remote Sens. 2019, 11(21), 2513; https://doi.org/10.3390/rs11212513 - 27 Oct 2019
Abstract
The Songnen Plain (SNP) is an important grain production base, and is designated as an ecological red-line as a protected area in China. Natural ecosystems such as the ecological protection barrier play an important role in maintaining the productivity and sustainability of farmland. [...] Read more.
The Songnen Plain (SNP) is an important grain production base, and is designated as an ecological red-line as a protected area in China. Natural ecosystems such as the ecological protection barrier play an important role in maintaining the productivity and sustainability of farmland. Carbon use efficiency (CUE), defined as the ratio of net primary productivity (NPP) to gross primary productivity (GPP), represents the ecosystem capacity of transferring carbon from the atmosphere to terrestrial biomass. The understanding of the CUE of natural ecosystems in protected farmland areas is vital to predicting the impact of global change and human disturbances on carbon budgets and evaluating ecosystem functions. To date, the changes in CUE at different time scales and their relationships with climatic factors have yet to be fully understood. CUE and the response to land surface phenology are also deserving attention. In this study, variations in ecosystem CUE in the SNP during 2001–2015 were investigated using Moderate-Resolution Imaging Spectroradiometer (MODIS) GPP and NPP data products estimated using the Carnegie-Ames-Stanford approach (CASA) model. The relationships between CUE and phenological and climate factors were explored. The results showed that ecosystem CUE fluctuated over time in the SNP. The lowest and highest CUE values mainly occurred in May and October, respectively. At seasonal scale, average CUE followed a descending order of Autumn > Summer > Spring. The CUE of mixed forest was greater than that of other ecosystems at both monthly and seasonal scales. Land surface phenology plays an important role in the regulation of CUE. The earlier start (SOS), the later end (EOS) and longer length (LOS) of the growing season would contribute increasing of CUE. Precipitation and temperature affected CUE positively in most areas of the SNP. These findings help explain the CUE of natural ecosystems in the protected farmland areas and improve our understanding of ecosystem carbon allocation dynamics in temperate semi-humid to semi-arid transitional region under climate and phenological fluctuations. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Monitoring of Protected Areas)
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Open AccessArticle
Long-Term Spatiotemporal Dynamics of Terrestrial Biophysical Variables in the Three-River Headwaters Region of China from Satellite and Meteorological Datasets
Remote Sens. 2019, 11(14), 1633; https://doi.org/10.3390/rs11141633 - 10 Jul 2019
Abstract
Terrestrial biophysical variables play an essential role in quantifying the amount of energy budget, water cycle, and carbon sink over the Three-River Headwaters Region of China (TRHR). However, direct field observations are missing in this region, and few studies have focused on the [...] Read more.
Terrestrial biophysical variables play an essential role in quantifying the amount of energy budget, water cycle, and carbon sink over the Three-River Headwaters Region of China (TRHR). However, direct field observations are missing in this region, and few studies have focused on the long-term spatiotemporal variations of terrestrial biophysical variables. In this study, we evaluated the spatiotemporal dynamics of biophysical variables including meteorological variables, vegetation, and evapotranspiration (ET) over the TRHR, and analyzed the response of vegetation and ET to climate change in the period from 1982 to 2015. The main input gridded datasets included meteorological reanalysis data, a satellite-based vegetation index dataset, and the ET product developed by a process-based Priestley–Taylor algorithm. Our results illustrate that: (1) The air temperature and precipitation over the TRHR increased by 0.597 °C and 41.1 mm per decade, respectively, while the relative humidity and surface downward shortwave radiation declined at a rate of 0.9% and 1.8 W/m2 per decade during the period 1982–2015, respectively. We also found that a ‘dryer warming’ tendency and a ‘wetter warming’ tendency existed in different areas of the TRHR. (2) Due to the predominant ‘wetter warming’ tendency characterized by the increasing temperature and precipitation, more than 56.8% of areas in the TRHR presented a significant increment in vegetation (0.0051/decade, p < 0.05), particularly in the northern and western meadow areas. When energy was the limiting factor for vegetation growth, temperature was a considerably more important driving factor than precipitation. (3) The annual ET of the TRHR increased by 3.34 mm/decade (p < 0.05) with an annual mean of 230.23 mm/year. More importantly, our analysis noted that ET was governed by terrestrial water supply, e.g., soil moisture and precipitation in the arid region of the western TRHR. By contrast, atmospheric evaporative demand derived by temperature and relative humidity was the primary controlling factor over the humid region of the southeastern TRHR. It was noted that land management activities, e.g., irrigation, also had a nonnegligible impact on the temporal and spatial variation of ET. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Monitoring of Protected Areas)
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Open AccessArticle
High Spatial Resolution Remote Sensing for Salt Marsh Mapping and Change Analysis at Fire Island National Seashore
Remote Sens. 2019, 11(9), 1107; https://doi.org/10.3390/rs11091107 - 09 May 2019
Cited by 4
Abstract
Salt marshes are changing due to natural and anthropogenic stressors such as sea level rise, nutrient enrichment, herbivory, storm surge, and coastal development. This study analyzes salt marsh change at Fire Island National Seashore (FIIS), a nationally protected area, using object-based image analysis [...] Read more.
Salt marshes are changing due to natural and anthropogenic stressors such as sea level rise, nutrient enrichment, herbivory, storm surge, and coastal development. This study analyzes salt marsh change at Fire Island National Seashore (FIIS), a nationally protected area, using object-based image analysis (OBIA) to classify a combination of data from Worldview-2 and Worldview-3 satellites, topobathymetric Light Detection and Ranging (LiDAR), and National Agricultural Imagery Program (NAIP) aerial imageries acquired from 1994 to 2017. The salt marsh classification was trained and tested with vegetation plot data. In October 2012, Hurricane Sandy caused extensive overwash and breached a section of the island. This study quantified the continuing effects of the breach on the surrounding salt marsh. The tidal inundation at the time of image acquisition was analyzed using a topobathymetric LiDAR-derived Digital Elevation Model (DEM) to create a bathtub model at the target tidal stage. The study revealed geospatial distribution and rates of change within the salt marsh interior and the salt marsh edge. The Worldview-2/Worldview-3 imagery classification was able to classify the salt marsh environments accurately and achieved an overall accuracy of 92.75%. Following the breach caused by Hurricane Sandy, bayside salt marsh edge was found to be eroding more rapidly (F1, 1597 = 206.06, p < 0.001). However, the interior panne/pool expansion rates were not affected by the breach. The salt marsh pannes and pools were more likely to revegetate if they had a hydrological connection to a mosquito ditch (χ2 = 28.049, p < 0.001). The study confirmed that the NAIP data were adequate for determining rates of salt marsh change with high accuracy. The cost and revisit time of NAIP imagery creates an ideal open data source for high spatial resolution monitoring and change analysis of salt marsh environments. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Monitoring of Protected Areas)
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Open AccessArticle
Competition and Burn Severity Determine Post-Fire Sapling Recovery in a Nationally Protected Boreal Forest of China: An Analysis from Very High-Resolution Satellite Imagery
Remote Sens. 2019, 11(6), 603; https://doi.org/10.3390/rs11060603 - 13 Mar 2019
Abstract
Anticipating how boreal forest landscapes will change in response to changing fire regime requires disentangling the effects of various spatial controls on the recovery process of tree saplings. Spatially explicit monitoring of post-fire vegetation recovery through moderate resolution Landsat imagery is a popular [...] Read more.
Anticipating how boreal forest landscapes will change in response to changing fire regime requires disentangling the effects of various spatial controls on the recovery process of tree saplings. Spatially explicit monitoring of post-fire vegetation recovery through moderate resolution Landsat imagery is a popular technique but is filled with ambiguous information due to mixed pixel effects. On the other hand, very-high resolution (VHR) satellite imagery accurately measures crown size of tree saplings but has gained little attention and its utility for estimating leaf area index (LAI, m2/m2) and tree sapling abundance (TSA, seedlings/ha) in post-fire landscape remains untested. We compared the explanatory power of 30 m Landsat satellite imagery with 0.5-m WorldView-2 VHR imagery for LAI and TSA based on field sampling data, and subsequently mapped the distribution of LAI and TSA based on the most predictive relationships. A random forest (RF) model was applied to assess the relative importance and causal mechanisms of spatial controls on tree sapling recovery. The results showed that pixel percentage of canopy trees (PPCT) derived from VHR imagery outperform all Landsat-derived spectral indices for explaining variance of LAI (R2VHR = 0.676 vs. R2Landsat = 0.427) and TSA (R2VHR = 0.508 vs. R2Landsat = 0.499). The RF model explained an average of 55.5% (SD = 3.0%, MSE = 0.382, N = 50) of the variation of estimated LAI. Understory vegetation coverage (competition) and post-fire surviving mature trees (seed sources) were the most important spatial controls for LAI recovery, followed by burn severity (legacy effect), topographic factors (environmental filter) and nearest distance to unburned area (edge effect). These analyses allow us to conclude that in our study area, mitigating wildfire severity and size may increase forest resilience to wildfire damage. Given the easily-damaged seed banks and relatively short seed dispersal distance of coniferous trees, reasonable human help to natural recovery of coniferous forests is necessary for severe burns with a large patch size, particularly in certain areas. Our research shows the VHR WorldView-2 imagery better resolves key characteristics of forest landscapes like LAI and TSA than Landsat imagery, providing a valuable tool for land managers and researchers alike. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Monitoring of Protected Areas)
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