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Monitoring Agricultural Land-Use Change and Land-Use Intensity Ⅱ

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (28 March 2023) | Viewed by 23805

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Special Issue Editors

Dr. Luo Liu

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Guest Editor

College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Interests: crop system; crop mapping; deep learning
Special Issues, Collections and Topics in MDPI journals

Dr. Yuanwei Qin

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Guest Editor

Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK 73019, USA
Interests: forest resources and ecosystem; forest carbon; agriculture; environmental remote sensing
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Dr. Bingwen Qiu

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Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350108, China
Interests: crop mapping; time series images; sentinel

Dr. Qiangyi Yu

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Guest Editor

Institute of Agriculture Resource and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Interests: agricultural land use; land use intensity; land use and land cover chang
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Dr. Zhi Qiao

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Guest Editor

School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China
Interests: urban climate; remote sensing; land use; cover change
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Special Issue Information

Dear Colleagues,

Agricultural development is related to food security and the supply of fiber, fuel and raw materials for human life. According to the analysis of FAO, “The world’s population is expected to grow to almost 10 billion by 2050, boosting agricultural demand by some 50 percent compared to 2013 in a scenario of modest economic growth”. Combined with the influences of global climate change, human activities and urbanization, this is bound to lead to huge changes in land use and cropping systems, potentially imposing huge challenges to the sustainable development of agriculture. Remote sensing has the capacity to monitor changes and impacts to agriculture and assist in the adaptive evolution of agricultural practices in order to face this major challenge.

This Special Issue aims to compile the most recent research on mapping and monitoring land use and crop systems including crop intensity, crop types, crop growth and crop yield in different locations and scales, using the existing spatial, spectral and temporal resolution provided by satellite, airborne and UAV image data, which is committed to providing reliable data support and theoretical guidance for the sustainable management of agricultural development.

We would like to invite you to submit articles about the main topics below (but articles need not be limited to this list):

Monitoring agriculture land use/cover;
Mapping crop intensity at large scale;
Phenological/Time-series based crop mapping;
Monitoring crop growth;
Monitoring crop yield.

Dr. Luo Liu
Dr. Yuanwei Qin
Dr. Bingwen Qiu
Dr. Qiangyi Yu
Dr. Zhi Qiao
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

farmland
crop intensity
crop type
crop phenology
land abandonment
deep learning
large scale mapping
crop yield monitoring

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Published Papers (9 papers)

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23 pages, 3884 KiB

Open AccessArticle

Cropland Extraction in Southern China from Very High-Resolution Images Based on Deep Learning

by Dehua Xie, Han Xu, Xiliu Xiong, Min Liu, Haoran Hu, Mengsen Xiong and Luo Liu

Remote Sens. 2023, 15(9), 2231; https://doi.org/10.3390/rs15092231 - 23 Apr 2023

Cited by 5 | Viewed by 2611

Abstract

Accurate cropland information is crucial for the assessment of food security and the formulation of effective agricultural policies. Extracting cropland from remote sensing imagery is challenging due to spectral diversity and mixed pixels. Recent advances in remote sensing technology have facilitated the availability of very high-resolution (VHR) remote sensing images that provide detailed ground information. However, VHR cropland extraction in southern China is difficult because of the high heterogeneity and fragmentation of cropland and the insufficient observations of VHR sensors. To address these challenges, we proposed a deep learning-based method for automated high-resolution cropland extraction. The method used an improved HRRS-U-Net model to accurately identify the extent of cropland and explicitly locate field boundaries. The HRRS-U-Net maintained high-resolution details throughout the network to generate precise cropland boundaries. Additionally, the residual learning (RL) and the channel attention mechanism (CAM) were introduced to extract deeper discriminative representations. The proposed method was evaluated over four city-wide study areas (Qingyuan, Yangjiang, Guangzhou, and Shantou) with a diverse range of agricultural systems, using GaoFen-2 (GF-2) images. The cropland extraction results for the study areas had an overall accuracy (OA) ranging from 97.00% to 98.33%, with F1 scores (F1) of 0.830–0.940 and Kappa coefficients (Kappa) of 0.814–0.929. The OA was 97.85%, F1 was 0.915, and Kappa was 0.901 over all study areas. Moreover, our proposed method demonstrated advantages compared to machine learning methods (e.g., RF) and previous semantic segmentation models, such as U-Net, U-Net++, U-Net3+, and MPSPNet. The results demonstrated the generalization ability and reliability of the proposed method for cropland extraction in southern China using VHR remote images. Full article

(This article belongs to the Special Issue Monitoring Agricultural Land-Use Change and Land-Use Intensity Ⅱ)

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23 pages, 7196 KiB

Open AccessArticle

Monitoring Cropland Abandonment in Southern China from 1992 to 2020 Based on the Combination of Phenological and Time-Series Algorithm Using Landsat Imagery and Google Earth Engine

by Yingyue Su, Shikun Wu, Shanggui Kang, Han Xu, Guangsheng Liu, Zhi Qiao and Luo Liu

Remote Sens. 2023, 15(3), 669; https://doi.org/10.3390/rs15030669 - 23 Jan 2023

Cited by 8 | Viewed by 2905

Abstract

Cropland abandonment is one of the most widespread types of land-use change in Southern China. Quickly and accurately monitoring spatial-temporal patterns of cropland abandonment is crucial for food security and a good ecological balance. There are still enormous challenges in the long-term monitoring of abandoned cropland in cloud and rain-prone and cropland-fragmented regions. In this study, we developed an approach to automatically obtain Landsat imagery for two key phenological periods, rather than as a time series, and mapped annual land cover from 1989 to 2021 based on the random forest classifier. We also proposed an algorithm for pixel-based, long-term annual land cover correction based on prior knowledge and natural laws, and generated cropland abandonment maps for Guangdong Province over the past 30 years. This work was implemented in Google Earth Engine. Accuracy assessment of the annual cropland abandonment maps for every five years during study period revealed an overall accuracy of 92–95%, producer (user) accuracy of 90–96% (73–87%), and Kappa coefficients of 0.81–0.88. In recent decades, the cropland abandonment area was relatively stable, at around 50 × 10⁴ ha, while the abandonment rate gradually increased with a decrease in the cultivated area after 2000. The Landsat-based cropland abandonment monitoring method can be implemented in regions such as southern China, and will support food security and strategies for maintaining ecological balance. Full article

(This article belongs to the Special Issue Monitoring Agricultural Land-Use Change and Land-Use Intensity Ⅱ)

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22 pages, 15097 KiB

Open AccessArticle

National Scale Maize Yield Estimation by Integrating Multiple Spectral Indexes and Temporal Aggregation

by Yuhua He, Bingwen Qiu, Feifei Cheng, Chongcheng Chen, Yu Sun, Dongshui Zhang, Li Lin and Aizhen Xu

Remote Sens. 2023, 15(2), 414; https://doi.org/10.3390/rs15020414 - 10 Jan 2023

Cited by 4 | Viewed by 2101

Abstract

Maize yield in China accounts for more than one-fourth of the global maize yield, but it is challenged by frequent extreme weather and increasing food demand. Accurate and timely estimation of maize yield is of great significance to crop management and food security. Commonly applied vegetation indexes (VIs) are mainly used in crop yield estimation as they can reflect the greenness of vegetation. However, the environmental pressures of crop growth and development are difficult to monitor and evaluate. Indexes for water content, pigment content, nutrient elements and biomass have been developed to indirectly explain the influencing factors of yield, with extant studies mainly assessing VIs, climate and water content factors. Only a few studies have attempted to systematically evaluate the sensitivity of these indexes. The sensitivity of the spectral indexes, combined indexes and climate factors and the effect of temporal aggregation data need to be evaluated. Thus, this study proposes a novel yield evaluation method for integrating multiple spectral indexes and temporal aggregation data. In particular, spectral indexes were calculated by integrating publicly available data (remote sensing images and climate data) from the Google Earth Engine platform, and county-level maize yields in China from 2015 to 2019 were estimated using a random forest model. Results showed that the normalized moisture difference index (NMDI) is the index most sensitive to yield estimation. Furthermore, the potential of adopting the combined indexes, especially NMDI_NDNI, was verified. Compared with the whole-growth period data and the eight-day time series, the vegetative growth period and the reproductive growth period data were more sensitive to yield estimation. The maize yield in China can be estimated by integrating multiple spectral indexes into the indexes for the vegetative and reproductive growth periods. The obtained R² of maize yield estimation reached 0.8. This study can provide feature knowledge and references for index assessments for yield estimation research. Full article

(This article belongs to the Special Issue Monitoring Agricultural Land-Use Change and Land-Use Intensity Ⅱ)

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20 pages, 3377 KiB

Open AccessArticle

An In-Depth Assessment of the Drivers Changing China’s Crop Production Using an LMDI Decomposition Approach

by Yuqiao Long, Wenbin Wu, Joost Wellens, Gilles Colinet and Jeroen Meersmans

Remote Sens. 2022, 14(24), 6399; https://doi.org/10.3390/rs14246399 - 19 Dec 2022

Cited by 3 | Viewed by 2233

Abstract

Over the last decades, growing crop production across China has had far-reaching consequences for both the environment and human welfare. One of the emerging questions is “how to meet the growing food demand in China?” In essence, the consensus is that the best way forward would be to increase crop yield rather than further extend the current cropland area. However, assessing progress in crop production is challenging as it is driven by multiple factors. To date, there are no studies to determine how multiple factors affect the crop production increase, considering both intensive farming (using yield and multiple cropping index) and large-scale farming (using mean parcel size and number of parcels). Using the Logarithmic-Mean-Divisia-Index (LMDI) decomposition method combined with statistical data and land cover data (GlobeLand30), we assess the contribution of intensive farming and large-scale farming changes to crop production dynamics at the national and county scale. Despite a negative contribution from MPS (mean parcel size, ), national crop production increased due to positive contributions from yield (), MCI (multiple cropping index, ), as well as NP (number of parcels, ). This allowed China to meet the growing national crop demand. We further find that large differences across regions persist over time. For most counties, the increase in crop production is a consequence of improved yields. However, in the North China Plain, NP is another important factor leading to crop production improvement. On the other hand, regions witnessing a decrease in crop production (e.g., the southeast coastal area of China) were characterized by a remarkable decrease in yield and MCI. Our detailed analyses of crop production provide accurate estimates and therefore can guide policymakers in addressing food security issues. Specifically, besides stabilizing yield and maintaining the total NP, it would be advantageous for crop production to increase the mean parcel size and MCI through land consolidation and financial assistance for land transfer and advanced agricultural infrastructure. Full article

(This article belongs to the Special Issue Monitoring Agricultural Land-Use Change and Land-Use Intensity Ⅱ)

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16 pages, 3467 KiB

Open AccessArticle

The Quantitative Impact of the Arable Land Protection Policy on the Landscape of Farmland Abandonment in Guangdong Province

by Le Li, Siyan Zheng, Kefei Zhao, Kejian Shen, Xiaolu Yan and Yaolong Zhao

Remote Sens. 2022, 14(19), 4991; https://doi.org/10.3390/rs14194991 - 7 Oct 2022

Cited by 2 | Viewed by 3118

Abstract

In the past two decades, the Ministry of Agriculture and Rural Affairs of China (MARA) has issued a series of strict cultivated land protection policies to prevent the spread of farmland abandonment and maintain a dynamic balance between the quantity and quality of arable land. However, high-speed economic development, strict arable land protection policies, and ecological security and sustainable development strategies interacting with human activities have brought challenges to quantifying the effectiveness of arable land protection policies. In this study, we proposed a method to quantify the impacts of the arable land protection policies and evaluate the quantitative impacts on farmland abandonment in Guangdong Province after 2014 from the perspective of landscape ecology. The results illustrated that the landscape fragmentation of farmland abandonment in Guangdong Province decreased after the new arable land policies were issued. More annual farmland abandonment (AFA) shifted to seasonal farmland abandonment (SFA), revealing the considerable pronounced effects of farmland abandonment management. The new policies effectively restrained the area increase for AFA in the regions with lower rural population (RPOP) and lower gross domestic product (GDP), and reduced the fragmentation of AFA in the regions with the highest RPOP and lower GDP. Additionally, the new policies effectively restrained the fragmentation increase for SFA in the regions with lower RPOP and lower GDP, and reduced the area increase for SFA in the regions with the highest RPOP and lower GDP. The management effect was not that significant in the regions with higher RPOP and higher GDP. These findings will provide important data references for arable land decision making in southern China. Full article

(This article belongs to the Special Issue Monitoring Agricultural Land-Use Change and Land-Use Intensity Ⅱ)

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13 pages, 5954 KiB

Open AccessArticle

Asian Rice Calendar Dynamics Detected by Remote Sensing and Their Climate Drivers

by Jing Zhang, Huaqing Wu, Zhao Zhang, Liangliang Zhang, Yuchuan Luo, Jichong Han and Fulu Tao

Remote Sens. 2022, 14(17), 4189; https://doi.org/10.3390/rs14174189 - 25 Aug 2022

Cited by 8 | Viewed by 2631

Abstract

Detecting crop calendar changes is critically important for crop monitoring and management, but the lack of annual, Asia-wide, and long-term rice calendar datasets limits our understanding of rice phenological changes and their climate drivers. In this study, we retrieved key rice phenological dates from the GLASS AVHRR LAI through combining threshold-based and inflection-based detection methods, analyzed the changes during the period 1995–2015, and identified the key climate drivers of the main rice seasons in Asia. The retrieved phenological dates had a high level of agreement with the referenced observations. All R² were greater than 0.80. The length of the vegetation growing period (VGP) was mostly shortened (by an average of −4 days per decade), while the length of the reproductive growing period was mostly prolonged (by an average of 2 days per decade). Moreover, solar radiation had the most significant impact on the rice calendar changes, followed by the maximum and minimum temperatures. The VGP in tropical areas is the most sensitive to climate change. Our study extends the annual rice phenology dynamics to a higher spatial–temporal resolution and provides new insights into rice calendar changes and their climate drivers, which will assist governments and researchers regarding food security and agricultural sustainability. Full article

(This article belongs to the Special Issue Monitoring Agricultural Land-Use Change and Land-Use Intensity Ⅱ)

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17 pages, 5747 KiB

Open AccessArticle

Monitoring Cropland Abandonment in Hilly Areas with Sentinel-1 and Sentinel-2 Timeseries

by Shan He, Huaiyong Shao, Wei Xian, Ziqiang Yin, Meng You, Jialong Zhong and Jiaguo Qi

Remote Sens. 2022, 14(15), 3806; https://doi.org/10.3390/rs14153806 - 7 Aug 2022

Cited by 12 | Viewed by 2829

Abstract

Abandoned cropland may lead to a series of issues regarding the environment, ecology, and food security. In hilly areas, cropland is prone to be abandoned due to scattered planting, relatively fewer sunlight hours, and a lower agricultural input–output ratio. Furthermore, the impact of abandoned rainfed cropland differs from abandoned irrigated cropland; thus, the corresponding land strategies vary accordingly. Unfortunately, monitoring abandoned cropland is still an enormous challenge in hilly areas. In this study, a new approach was proposed by (1) improving the availability of Sentinel-1 and Sentinel-2 images by a series of processes, (2) obtaining training samples from multisource data overlay analysis and timeseries viewer tool, (3) mapping annual land cover from all available Sentinel-1 and Sentinel-2 images, training samples, and the random forest classifier, and (4) mapping the spatiotemporal distribution of abandoned rainfed cropland and irrigated cropland in hilly areas by assessing land-cover trajectories along with time. The result showed that rainfed cropland had lower F1 scores (0.759 to 0.8) compared to that irrigated cropland (0.836 to 0.879). High overall accuracies of around 0.90 were achieved, with the kappa values ranging from 0.851 to 0.862, which outperformed the existing products in accuracy and spatial detail. Our study provides a reference for extracting the spatiotemporal distribution of abandoned rainfed cropland and irrigated cropland in hilly areas. Full article

(This article belongs to the Special Issue Monitoring Agricultural Land-Use Change and Land-Use Intensity Ⅱ)

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22 pages, 6913 KiB

Open AccessArticle

The Spatiotemporal Changes in Ecological–Environmental Quality Caused by Farmland Consolidation Using Google Earth Engine: A Case Study from Liaoning Province in China

by Maoxin Zhang, Tingting He, Cifang Wu and Guangyu Li

Remote Sens. 2022, 14(15), 3646; https://doi.org/10.3390/rs14153646 - 29 Jul 2022

Cited by 8 | Viewed by 1733

Abstract

Farmland consolidation (FC) is among the measures to solve farmland issues, such as farmland fragmentation, and its impact on the ecological environment has always been controversial. In terms of long-term series and large-area analysis, the calculation of a large amount of data makes the analysis of the ecological–environmental quality of farmland consolidation very difficult. To solve this problem, our study applied a remote sensing ecological index model on the Google Earth Engine platform to analyze the changes in the ecological–environmental quality in two prefecture-level cities in Liaoning Province over the past 20 years. In addition, we analyzed the impacts of FC projects on the ecological environment from 2006 to 2018 and compared them to farmland without consolidation. The study results show that FC caused negative impacts on the quality of the ecological environment during the FC period (2006–2018) and that the FC’s positive effects take time to develop. In each FC phase, the results showed that FC exhibited negative effects before 2010 because the proportion of ecological–environmental quality reductions (0–47.67%) was higher than the proportion of increases (9.62–46.15%) in those FC phases. Since 2011, the area experiencing positive ecological–environmental benefits (31.96–72.01%) enabled by FC is higher than the area of negative impact (2.24–18.07%). This seems to be triggered by policy evolution. Based on the trend analysis, the proportion of FC areas with improved ecological–environmental quality grew faster (Gini index decreased 0.09) than that of farmland without consolidation (Gini index decreased 0.05) from before FC to after FC. Moreover, the new FC projects (2011–2018) performed better than the early projects (2006–2010), which may be due to policy evolution and technological advancements. However, the new FC projects (2011–2018) caused a dramatic decrease in ecological–environmental quality in a small number of areas due to the study time constraints. In conclusion, we believe that FC could improve the ecological–environmental quality of farmland, whereas some measures are needed to reduce its temporal negative impact on ecological–environmental quality, which may be caused by human interference. The remote sensing ecological index obtained using the Google Earth Engine platform provided an effective and reliable method for detecting the impacts of FC on the ecological–environmental quality of farmland. This could provide the basis and support for the monitoring of ecological–environmental changes in FC areas at a regional level. Full article

(This article belongs to the Special Issue Monitoring Agricultural Land-Use Change and Land-Use Intensity Ⅱ)

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14 pages, 3479 KiB

Open AccessTechnical Note

Estimate the Earliest Phenophase for Garlic Mapping Using Time Series Landsat 8/9 Images

by Yan Guo, Haoming Xia, Xiaoyang Zhao, Longxin Qiao and Yaochen Qin

Remote Sens. 2022, 14(18), 4476; https://doi.org/10.3390/rs14184476 - 8 Sep 2022

Cited by 10 | Viewed by 2234

Abstract

Garlic is the major economic crop in China. Timely and accurate identification and mapping of garlic are significant for garlic yield prediction and garlic market management. Previous studies on garlic mapping were mainly based on all observations of the entire growing season, so the resulting maps have a hysteresis. Here, we determined the optimal identification strategy and the earliest identifiable phenophase for garlic based on all available Landsat 8/9 time series imagery in Google Earth Engine. Specifically, we evaluated the performance of different vegetation indices for each phenophase to determine the optimal classification metrics for garlic. Secondly, we identified garlic using random forest algorithm and classification metrics of different time series lengths. Finally, we determined the earliest identifiable phenophase of garlic and generated an early-season garlic distribution map. Garlic could be identified as early as March (bud differentiation period) with an F1 of 0.91. Our study demonstrates the differences in the performance of vegetation indices at different phenophases, and these differences provide a new idea for mapping crops. The generated early-season garlic distribution map provides timely data support for various stakeholders. Full article

(This article belongs to the Special Issue Monitoring Agricultural Land-Use Change and Land-Use Intensity Ⅱ)

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