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Advanced Phenology, and Land Cover and Land Use Change Studies

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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 (31 March 2022) | Viewed by 17861

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Dr. Shin Nagai

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Research Institute for Global Change, Japan Agency for Marine-Earth Science Technology, 3173-25 Showa-machi, Kanazawa-ku, Yokohama 236-0001, Japan
Interests: remote sensing; ecology; ground-truthing; phenological eyes network; climate change

Dr. Tomoaki Miura

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Guest Editor

Department of Natural Resources and Environmental Management, University of Hawai‘i at Mānoa, Honolulu, HI 96822, USA
Interests: environment; spatial analysis; climate change; remote sensing; satellite image analysis; vegetation; landscape ecology; time series; vegetation mapping
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Dr. Narumasa Tsutsumida

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Graduate School of Global Environmental Studies, Kyoto University, Sakyo Ward, Kyoto 606-8501, Japan
Interests: geographical information (GIS & remote sensing); land use/cover monitoring; spatial accuracy assessment; spatial modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Rigorous monitoring of and accurate information on phenology, and land cover and land use changes (LCLUC) are required to evaluate the spatio-temporal variability of ecosystem functions and services, and biodiversity under climate change and anthropogenic activities. The most widely and frequently used data in previous studies have been in-situ observations such as visual inspections and near-surface remote sensing which are limited in quantity and coarse spatial resolution satellite data which are limited in quality. Now, there are innovative “social sensing” (e.g., twitter, instagram, google trends, face book, mapillary) data and new fine-spatial or -temporal resolution satellite data (e.g., SENTINEL-2A/2B, Himawari-8) available. We believe these new-generation datasets can further our understanding of the interactions among phenology, LCLUC, climate change, and anthropogenic activities. This special issue, “Advanced phenology, and land cover and land use change studies,” calls for studies that present innovative and/or experimental ideas, and investigation results that integrate “social sensing” and “remote sensing” data for advancing phenology and LCLUC studies.

Dr. Shin Nagai
Prof. Dr. Tomoaki Miura
Dr. Narumasa Tsutsumida
Guest Editors

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Keywords

Big data
Biodiversity
Ecosystem service
Land cover and land use change
Remote sensing
Phenology
Social sensing

Published Papers (7 papers)

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Research

16 pages, 3262 KiB

Open AccessArticle

Evaluation of Land Surface Phenology for Autumn Leaf Color Change Based on Citizen Reports across Japan

by Narumasa Tsutsumida, Nagai Shin and Tomoaki Miura

Remote Sens. 2022, 14(9), 2017; https://doi.org/10.3390/rs14092017 - 22 Apr 2022

Cited by 5 | Viewed by 1822

Abstract

Autumn foliage color is an important phenological characteristic associated with climate and appeals to populations as a cultural ecosystem service (CES). Land surface phenology (LSP) analyzed using time-series remotely sensed imagery can facilitate the monitoring of autumn leaf color change (ALCC); however, the monitoring of autumn foliage by LSP approaches is still challenging because of complex spatio-temporal ALCC patterns and observational uncertainty associated with remote sensing sensors. Here, we evaluated the performance of several LSP analysis approaches in estimation of LSP-based ALCCs against the ground-level autumn foliage information obtained from 758 sightseeing (high CES) sites across Japan. The ground information uniquely collected by citizens represented ALCC stages of greening, early, peak, late, and defoliation collected on a daily basis. The ALCC was estimated using a second derivative approach, in which normalized difference vegetation index (NDVI), kernel normalized difference vegetation index (kNDVI), enhanced vegetation index (EVI), two-band enhanced vegetation index (EVI2), and green red vegetation index (GRVI) were applied based on MODerate resolution Imaging Spectroradiometer (MODIS) MOD09A1 with four (Beck, Elmore, Gu, and Zhang) double logistic smoothing methods in 2020. The results revealed inconsistency in the estimates obtained using different analytical methods; those obtained using EVI with the Beck model estimated the peak stage of the ALCC relatively well, while the estimates obtained using other indices and models had high discrepancies along with uncertainty. Our study provided insights on how the LSP approach can be improved toward mapping the CESs offered by autumn foliage. Full article

(This article belongs to the Special Issue Advanced Phenology, and Land Cover and Land Use Change Studies)

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27 pages, 8122 KiB

Open AccessArticle

Simulating Future LUCC by Coupling Climate Change and Human Effects Based on Multi-Phase Remote Sensing Data

by Zihao Huang, Xuejian Li, Huaqiang Du, Fangjie Mao, Ning Han, Weiliang Fan, Yanxin Xu and Xin Luo

Remote Sens. 2022, 14(7), 1698; https://doi.org/10.3390/rs14071698 - 31 Mar 2022

Cited by 15 | Viewed by 2219

Abstract

Future land use and cover change (LUCC) simulations play an important role in providing fundamental data to reveal the carbon cycle response of forest ecosystems to LUCC. Subtropical forests have great potential for carbon sequestration, yet their future dynamics under natural and human influences are unclear. Zhejiang Province in China is an important distribution area for subtropical forests. For forest management, it is of great significance to explore the future dynamic changes of subtropical forests in Zhejiang. As a popular LUCC spatial simulation model, the cellular automata (CA) model coupled with machine learning and LUCC quantitative demand models such as system dynamics (SD) can achieve effective LUCC simulation. Therefore, we first integrated a back propagation neural network (BPNN), a CA, and a SD model as a BPNN_CA_SD (BCS) coupled model for future LUCC simulation and then designed a slow development scenario (SD_Scenario), a harmonious development scenario (HD_Scenario), a baseline development scenario (BD_Scenario), and a fast development scenario (FD_Scenario), combining climate change and human disturbance. Thirdly, we obtained future land-use patterns in Zhejiang Province from 2014 to 2084 under multiple scenarios, and finally, we analyzed the temporal and spatial changes of land use and discussed the subtropical forest dynamics of the future. The results showed the following: (1) The overall accuracy was approximately 0.8, the kappa coefficient was 0.75, and the figure of merit (FOM) value was over 28% when using the BCS model to predict LUCC, indicating that the model could predict the consistent change of LUCC accurately. (2) The future evolution of the LUCC under different scenarios varied, with the growth of bamboo forests and the decline of coniferous forests in the FD_Scenario being prominent among the forest dynamics changes. Compared with 2014, the bamboo forest in 2084 will increase by 37%, while the coniferous forest will decrease by 25%. (3) Comparing the area and spatial change of the subtropical forests, the SD_Scenario was found to be beneficial for the forest ecology. These results can provide an important decision-making reference for land-use planning and sustainable forest development in Zhejiang Province. Full article

(This article belongs to the Special Issue Advanced Phenology, and Land Cover and Land Use Change Studies)

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37 pages, 12902 KiB

Open AccessArticle

Using Eco-Geographical Zoning Data and Crowdsourcing to Improve the Detection of Spurious Land Cover Changes

by Ling Zhu, Dejun Gao, Tao Jia and Jingyi Zhang

Remote Sens. 2021, 13(16), 3244; https://doi.org/10.3390/rs13163244 - 16 Aug 2021

Cited by 2 | Viewed by 2154

Abstract

To address problems in remote sensing image change detection, this study proposes a method for identifying spurious changes based on an eco-geographical zoning knowledge base and crowdsourced data mining. After preliminary change detection using the super pixel cosegmentation method, eco-geographical zoning is introduced, and the rules of spurious change are collected based on the knowledge of expert interpreters, and from statistics on existing land cover products according to each eco-geographical zone. Uncertain changed patches with a high possibility of spurious change according to the eco-geographical zoning rule were published in the form of a map service on an online platform, and then crowd tagging information on spurious changed patches was collected. The Hyperlink-Induced Topic Search (HITS) algorithm was used to calculate the spurious change degree of changed patches. We selected the northern part of Laos as the experimental area and the Chinese GF-1 Wide Field View (WFV) images for change detection to verify the effectiveness of the method. The results show that the accuracy of change detection improves by 23% after removing the spurious changes. Spurious changes caused by clouds, river water turbidity, spectral differences in cultivated land before and after harvest, and changes in shrubs, grassland, and forest density, can be removed using an eco-geographical zoning knowledge base and crowdsourced data mining methods. Full article

(This article belongs to the Special Issue Advanced Phenology, and Land Cover and Land Use Change Studies)

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21 pages, 10846 KiB

Open AccessArticle

Monitoring the Responses of Deciduous Forest Phenology to 2000–2018 Climatic Anomalies in the Northern Hemisphere

by Kevin Bórnez, Aleixandre Verger, Adrià Descals and Josep Peñuelas

Remote Sens. 2021, 13(14), 2806; https://doi.org/10.3390/rs13142806 - 16 Jul 2021

Cited by 5 | Viewed by 2356

Abstract

Monitoring the phenological responses of deciduous forests to climate is important, due to the increasing frequency and intensity of extreme climatic events associated with climate change and global warming, which will in turn affect vegetation seasonality. We investigated the spatiotemporal patterns of the response of deciduous forests to climatic anomalies in the Northern Hemisphere, using satellite-derived phenological metrics from the Copernicus Global Land Service Leaf Area Index, and multisource climatic datasets for 2000–2018 at resolutions of 0.1°. Thereafter, we assessed the impact of extreme heatwaves and droughts on this deciduous forest phenology. We assumed that changes in the deciduous forest phenology in the Northern Hemisphere for the period 2000–2018 were monotonic, and that temperature and precipitation were the main climatic drivers. Analyses of partial correlations of phenological metrics with the timing of the start of the season (SoS), end of the season (EoS), and climatic variables indicated that changes in preseason temperature played a stronger role than precipitation in affecting the interannual variability of SoS anomalies: the higher the temperature, the earlier the SoS in most deciduous forests in the Northern Hemisphere (mean correlation coefficient of −0.31). Correlations between the SoS and temperature were significantly negative in 57% of the forests, and significantly positive in 15% of the forests (p < 0.05). Both temperature and precipitation contributed to the advance and delay of the EoS. A later EoS was significantly correlated with a positive Standardized Precipitation Evapotranspiration Index (SPEI) at the regional scale (~30% of deciduous forests). The timings of the EoS and SoS shifted by >20 d in response to heatwaves throughout most of Europe in 2003, and in the United States of America in 2012. This study contributes to improve our understanding of the phenological responses of deciduous forests in the Northern Hemisphere to climate change and extreme climate events. Full article

(This article belongs to the Special Issue Advanced Phenology, and Land Cover and Land Use Change Studies)

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22 pages, 5138 KiB

Open AccessArticle

An Automatic Method to Detect Lake Ice Phenology Using MODIS Daily Temperature Imagery

by Xin Zhang, Kaicun Wang and Georgiy Kirillin

Remote Sens. 2021, 13(14), 2711; https://doi.org/10.3390/rs13142711 - 09 Jul 2021

Cited by 6 | Viewed by 2430

Abstract

Lake ice phenology is a climate-sensitive indicator. However, ground-based monitoring suffers from the limitations of human vision and the difficulty of its implementation in harsh environments. Remote sensing provides great potential to detect lake ice phenology. In this study, a new automated method was developed to extract lake ice phenology parameters by capturing the temporal pattern of the transitional water/ice phase using a parameterized time function. The method is based on Moderate-Resolution Imaging Spectroradiometer (MODIS) daily temperature products, which have unique potential for monitoring lake ice cover as a result of providing four observations per day at 1 km spatial resolution from 2002 to 2016. Three seasonally ice-covered lakes with different characteristics in different climate regions were selected to test the method during the period of 2002–2016. The temporal pattern of water/ice transition phase was determined on the basis of unfrozen water cover fraction extracted from the MODIS daily temperature data, and was compared with the MODIS snow and reflectance products and Landsat images. A good agreement with an R² of above 0.8 was found when compared with the MODIS snow product. The annual variation of extracted ice phenology dates showed good consistency with the MODIS reflectance and AMSR-E/2 products. The approach was then applied to nine seasonally ice-covered lakes in northern China from 2002 to 2016. The strongest tendency towards a later freeze-up start date was revealed in Lake Qinghai (6.31 days/10 yr) among the lakes in Tibetan plateau, and the break-up start and end dates rapidly shifted towards earlier dates in Lake Hulun (−3.73 days/10 yr; −5.02 days/10 yr). The method is suitable for estimating and monitoring ice phenology on different types of lakes over large scales and has a strong potential to provide valuable information on the responses of ice processes to climate change. Full article

(This article belongs to the Special Issue Advanced Phenology, and Land Cover and Land Use Change Studies)

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13 pages, 4164 KiB

Open AccessArticle

Satellite-Based Estimation of the Influence of Land Use and Cover Change on the Surface Shortwave Radiation Budget in a Humid Basin

by Shuchao Ye, Huihui Feng, Bin Zou, Ying Ding, Sijia Zhu, Feng Li and Guotao Dong

Remote Sens. 2021, 13(8), 1447; https://doi.org/10.3390/rs13081447 - 08 Apr 2021

Cited by 5 | Viewed by 1929

Abstract

The surface shortwave radiation budget (Rsn) is one of the main drivers of Earth’s ecosystems and varies with atmospheric and surface conditions. Land use and cover change (LUCC) alters radiation through biogeophysical effects. However, due to the complex interactions between atmospheric and surface factors, it is very challenging to quantify the sole impacts of LUCC. Based on satellite data from the Global Land Surface Satellite (GLASS) Product and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, this study introduces an observation-based approach for detecting LUCC influences on the Rsn by examining a humid basin over the Dongting Lake Basin, China from 2001 to 2015. Our results showed that the Rsn of the study area presented a decreasing trend due to the combined effects of LUCC and climate change. Generally, LUCC contributed −0.45 W/m² to Rsn at the basin scale, which accounted for 2.53% of the total Rsn change. Furthermore, the LUCC contributions reached −0.69 W/m², 0.21 W/m², and −0.41 W/m² in regions with land transitions of forest→grass, grass→forest, and grass→farmland, which accounted for 5.38%, −4.68%, and 2.40% of the total Rsn change, respectively. Physically, LUCC affected surface radiation by altering the surface properties. Specifically, LUCC induced albedo changes of +0.0039 at the basin scale and +0.0061, −0.0020, and +0.0036 in regions with land transitions of forest→grass, grass→forest, and grass→farmland, respectively. Our findings revealed the impact and process of LUCC on the surface radiation budget, which could support the understanding of the physical mechanisms of LUCC’s impact on ecosystems. Full article

(This article belongs to the Special Issue Advanced Phenology, and Land Cover and Land Use Change Studies)

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22 pages, 11585 KiB

Open AccessArticle

Spatio-Temporal Evolution, Future Trend and Phenology Regularity of Net Primary Productivity of Forests in Northeast China

by Chunli Wang, Qun’ou Jiang, Xiangzheng Deng, Kexin Lv and Zhonghui Zhang

Remote Sens. 2020, 12(21), 3670; https://doi.org/10.3390/rs12213670 - 09 Nov 2020

Cited by 15 | Viewed by 3167

Abstract

Net Primary Productivity (NPP) is one of the significant indicators to measure environmental changes; thus, the relevant study of NPP in Northeast China, Asia, is essential to climate changes and ecological sustainable development. Based on the Global Production Efficiency (GLO-PEM) model, this study firstly estimated the NPP in Northeast China, from 2001 to 2019, and then analyzed its spatio-temporal evolution, future changing trend and phenology regularity. Over the years, the NPP of different forests type in Northeast China showed a gradual increasing trend. Compared with other different time stages, the high-value NPP (700–1300 gC·m⁻²·a⁻¹) in Changbai Mountain, from 2017 to 2019, is more widely distributed. For instance, the NPP has an increasing rate of 6.92% compared to the stage of 2011–2015. Additionally, there was a significant advance at the start of the vegetation growth season (SOS), and a lag at the end of the vegetation growth season (EOS), from 2001 to 2019. Thus, the whole growth period of forests in Northeast China became prolonged with the change of phenology. Moreover, analysis on the sustainability of NPP in the future indicates that the reverse direction feature of NPP change will be slightly stronger than the co-directional feature, meaning that about 30.68% of the study area will switch from improvement to degradation. To conclude, these above studies could provide an important reference for the sustainable development of forests in Northeast China. Full article

(This article belongs to the Special Issue Advanced Phenology, and Land Cover and Land Use Change Studies)

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