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Remote Sensing Satellites Data Analysis for Land Use / Land Cover (LULC) and Vegetation Monitoring

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (25 July 2023) | Viewed by 26799

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Prof. Dr. Martin Kappas

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Division of Cartography, GIS and Remote Sensing, Faculty of Geoscience and Geography, Georg-August University Goettingen, 37077 Goettingen, Germany
Interests: land use/cover change; integrated watershed analysis; desertification in drylands; multi-sensor remote sensing; monitoring concepts; land surface and vegetation dynamics
Special Issues, Collections and Topics in MDPI journals

Dr. Birgitta Putzenlechner

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Division of Cartography, GIS and Remote Sensing, Faculty of Geoscience and Geography, Georg-August University Goettingen, 37077 Goettingen, Germany
Interests: remote sensing; FAPAR; FCOVER; forests; environmental monitoring; UAV; geography; land use and land cover; Sentinel-2; essential climate variables

Dr. Daniel Wyss

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Guest Editor

Division of Cartography, GIS and Remote Sensing, Faculty of Geoscience and Geography, Georg-August University Goettingen, 37077 Goettingen, Germany
Interests: GIS project management; environmental analysis, monitoring, modeling und management; GIS capacity development

Special Issue Information

Dear Colleagues,

Human-induced land use and land cover (LULC) changes have significantly reshaped Earth's terrestrial surface; such alterations comprise physical and biological entities including vegetative cover, water bodies, bare lands and artificial structures as a result of urbanization. Alternatively, land use refers to an intricate combination of socio-economic factors, management principles and economic purposes. We often designate land use and land cover together, but there is a distinct difference between the two.

The role of remote sensing in land use and land cover modeling has gained momentum over the last 50 years (Landsat passed its 50-year anniversary ). The important task for researchers now in the LULC domain is to continue identifying high-impact LCLUC "hotspot" areas around the globe where human-induced LCLUC is occurring on various scalesand to undertake research on land-use adaptation to climate change, integrating the socio-economic component. New sensors, such as those on the Sentinel or the newly launched Landsat 9, and multi-sensor approaches provide insight into changes in land use and land cover. Very high resolution (VHR) data are becoming more easily available at no cost. The commercial data currently distributed by NASA are available under different scientific use licenses and various access portals (e.g. The Commercial Smallsat Data Acquisition (CSDA) program). Currently, data acquired by Planet, Maxar (formerly DigitalGlobe, Inc.) and Spire Global are available. Data from Teledyne Brown Engineering, Inc.’s DLR Earth Sensing Imaging Spectrometer (DESIS) are also accesible. Global data for tropical regions were made freely available by the Norwegian government (e.g. https://www.planet.com/nicfi/). The future hyperspectral EnMAP sensor will also enable new achievements in the exploration of landscapes and the quality of land cover. LCLUC studies focusing on the synergy of various kinds of satellite observations, together with novel methods such as “big data” and “machine learning”, as well as advanced methods to incorporate socio-economic data (e.g. role of remote sensing for land use and land cover change modeling, e.g. Dyna-CLUE), are welcome for this special issue.

Prof. Dr. Martin Kappas
Dr. Birgitta Putzenlechner
Dr. Daniel Wyss

Guest Editors

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Published Papers (7 papers)

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Research

19 pages, 5614 KiB

Open AccessArticle

Identification of Soil Types and Salinity Using MODIS Terra Data and Machine Learning Techniques in Multiple Regions of Pakistan

by Yasin Ul Haq, Muhammad Shahbaz, Shahzad Asif, Khmaies Ouahada and Habib Hamam

Sensors 2023, 23(19), 8121; https://doi.org/10.3390/s23198121 - 27 Sep 2023

Cited by 8 | Viewed by 2925

Abstract

Soil, a significant natural resource, plays a crucial role in supporting various ecosystems and serves as the foundation of Pakistan’s economy due to its primary use in agriculture. Hence, timely monitoring of soil type and salinity is essential. However, traditional methods for identifying soil types and detecting salinity are time-consuming, requiring expert intervention and extensive laboratory experiments. The objective of this study is to propose a model that leverages MODIS Terra data to identify soil types and detect soil salinity. To achieve this, 195 soil samples were collected from Lahore, Kot Addu, and Kohat, dating from October 2022 to November 2022. Simultaneously, spectral data of the same regions were obtained to spatially map soil types and salinity of bare land. The spectral reflectance of band values, salinity indices, and vegetation indices were utilized to classify the soil types and predict soil salinity. To perform the classification and regression tasks, the study employed three popular techniques in the research community: Random Forest (RF), Ada Boost (AB), and Gradient Boosting (GB), along with Decision Tree (DT), K-Nearest Neighbor (KNN), and Extra Tree (ET). A 70–30 test train validation split was used for the implementation of these techniques. The efficacy of the multi-class classification models for soil types was evaluated using accuracy, precision, recall, and f1-score. On the other hand, the regression models’ performances were evaluated and compared using R-squared (R

^{2}

), Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The results demonstrated that Random Forest outperformed other methods for both predicting soil types (accuracy = 65.38, precision = 0.60, recall = 0.57, and f1-score = 0.57) and predicting salinity (R

^{2}

= 0.90, MAE = 0.56, MSE = 0.98, RMSE = 0.97). Finally, the study designed a web portal to enable real-time prediction of soil types and salinity using these models. This web portal can be utilized by farmers and decision-makers to make informed decisions regarding soil, crop cultivation, and agricultural planning. Full article

(This article belongs to the Special Issue Remote Sensing Satellites Data Analysis for Land Use / Land Cover (LULC) and Vegetation Monitoring)

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20 pages, 45778 KiB

Open AccessArticle

Unveiling the Dynamics of Thermal Characteristics Related to LULC Changes via ANN

by Yasir Hassan Khachoo, Matteo Cutugno, Umberto Robustelli and Giovanni Pugliano

Sensors 2023, 23(15), 7013; https://doi.org/10.3390/s23157013 - 7 Aug 2023

Cited by 6 | Viewed by 2051

Abstract

Continuous and unplanned urbanization, combined with negative alterations in land use land cover (LULC), leads to a deterioration of the urban thermal environment and results in various adverse ecological effects. The changes in LULC and thermal characteristics have significant implications for the economy, climate patterns, and environmental sustainability. This study focuses on the Province of Naples in Italy, examining LULC changes and the Urban Thermal Field Variance Index (UTFVI) from 1990 to 2022, predicting their distributions for 2030. The main objectives of this research are the investigation of the future seasonal thermal characteristics of the study area by characterizing land surface temperature (LST) through the UTFVI and analyzing LULC dynamics along with their correlation. To achieve this, Landsat 4-5 Thematic Mapper (TM) and Landsat 9 Operational Land Imager (OLI) imagery were utilized. LULC classification was performed using a supervised satellite image classification system, and the predictions were carried out using the cellular automata-artificial neural network (CA-ANN) algorithm. LST was calculated using the radiative transfer equation (RTE), and the same CA-ANN algorithm was employed to predict UTFVI for 2030. To investigate the multi-temporal correlation between LULC and UTFVI, a cross-tabulation technique was employed. The study’s findings indicate that between 2022 and 2030, there will be a 9.4% increase in built-up and bare-land areas at the expense of the vegetation class. The strongest UTFVI zone during summer is predicted to remain stable from 2022 to 2030, while winter UTFVI shows substantial fluctuations with a 4.62% decrease in the none UTFVI zone and a corresponding increase in the strongest UTFVI zone for the same period. The results of this study reveal a concerning trend of outward expansion in the built-up area of the Province of Naples, with central northern regions experiencing the highest growth rate, predominantly at the expense of vegetation cover. These predictions emphasize the urgent need for proactive measures to preserve and protect the diminishing vegetation cover, maintaining ecological balance, combating the urban heat island effect, and safeguarding biodiversity in the province. Full article

(This article belongs to the Special Issue Remote Sensing Satellites Data Analysis for Land Use / Land Cover (LULC) and Vegetation Monitoring)

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16 pages, 6665 KiB

Open AccessArticle

Rapid Identification of Main Vegetation Types in the Lingkong Mountain Nature Reserve Based on Multi-Temporal Modified Vegetation Indices

by Wenjing Fang, Hongfen Zhu, Shuai Li, Haoxi Ding and Rutian Bi

Sensors 2023, 23(2), 659; https://doi.org/10.3390/s23020659 - 6 Jan 2023

Cited by 6 | Viewed by 2462

Abstract

Nature reserves are among the most bio-diverse regions worldwide, and rapid and accurate identification is a requisite for their management. Based on the multi-temporal Sentinel-2 dataset, this study presents three multi-temporal modified vegetation indices (the multi-temporal modified normalized difference Quercus wutaishanica index (MTM-NDQI), the multi-temporal modified difference scrub grass index (MTM-DSI), and the multi-temporal modified ratio shaw index (MTM-RSI)) to improve the classification accuracy of the remote sensing of vegetation in the Lingkong Mountain Nature Reserve of China (LMNR). These three indices integrate the advantages of both the typical vegetation indices and the multi-temporal remote sensing data. By using the proposed indices with a uni-temporal modified vegetation index (the uni-temporal modified difference pine-oak mixed forest index (UTM-DMI)) and typical vegetation indices (e.g., the ratio vegetation index (RVI), the difference vegetation index (DVI), and the normalized difference vegetation index (NDVI)), an optimal feature set is obtained that includes the NDVI of December, the NDVI of April, and the UTM-DMI, MTM-NDQI, MTM-DSI, and MTM-RSI. The overall accuracy (OA) of the random forest classification (98.41%) and Kappa coefficient of the optimal feature set (0.98) were higher than those of the time series NDVI (OA = 96.03%, Kappa = 0.95), the time series RVI (OA = 95.56%, Kappa = 0.95), and the time series DVI (OA = 91.27%, Kappa = 0.90). The OAs of the rapid classification and the Kappa coefficient of the knowledge decision tree based on the optimal feature set were 95.56% and 0.95, respectively. Meanwhile, only three of the seven vegetation types were omitted or misclassified slightly. Overall, the proposed vegetation indices have advantages in identifying the vegetation types in protected areas. Full article

(This article belongs to the Special Issue Remote Sensing Satellites Data Analysis for Land Use / Land Cover (LULC) and Vegetation Monitoring)

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21 pages, 13812 KiB

Open AccessArticle

Land-Use and Land-Cover Classification in Semi-Arid Areas from Medium-Resolution Remote-Sensing Imagery: A Deep Learning Approach

by Kamran Ali and Brian A. Johnson

Sensors 2022, 22(22), 8750; https://doi.org/10.3390/s22228750 - 12 Nov 2022

Cited by 32 | Viewed by 6906

Abstract

Detailed Land-Use and Land-Cover (LULC) information is of pivotal importance in, e.g., urban/rural planning, disaster management, and climate change adaptation. Recently, Deep Learning (DL) has emerged as a paradigm shift for LULC classification. To date, little research has focused on using DL methods for LULC mapping in semi-arid regions, and none that we are aware of have compared the use of different Sentinel-2 image band combinations for mapping LULC in semi-arid landscapes with deep Convolutional Neural Network (CNN) models. Sentinel-2 multispectral image bands have varying spatial resolutions, and there is often high spectral similarity of different LULC features in semi-arid regions; therefore, selection of suitable Sentinel-2 bands could be an important factor for LULC mapping in these areas. Our study contributes to the remote sensing literature by testing different Sentinel-2 bands, as well as the transferability of well-optimized CNNs, for semi-arid LULC classification in semi-arid regions. We first trained a CNN model in one semi-arid study site (Gujranwala city, Gujranwala Saddar and Wazirabadtownships, Pakistan), and then applied the pre-trained model to map LULC in two additional semi-arid study sites (Lahore and Faisalabad city, Pakistan). Two different composite images were compared: (i) a four-band composite with 10 m spatial resolution image bands (Near-Infrared (NIR), green, blue, and red bands), and (ii) a ten-band composite made by adding two Short Wave Infrared (SWIR) bands and four vegetation red-edge bands to the four-band composite. Experimental results corroborate the validity of the proposed CNN architecture. Notably, the four-band CNN model has shown robustness in semi-arid regions, where spatially and spectrally confusing land-covers are present. Full article

(This article belongs to the Special Issue Remote Sensing Satellites Data Analysis for Land Use / Land Cover (LULC) and Vegetation Monitoring)

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25 pages, 8102 KiB

Open AccessArticle

Irrigated Crop Types Mapping in Tashkent Province of Uzbekistan with Remote Sensing-Based Classification Methods

by Elbek Erdanaev, Martin Kappas and Daniel Wyss

Sensors 2022, 22(15), 5683; https://doi.org/10.3390/s22155683 - 29 Jul 2022

Cited by 14 | Viewed by 3584

Abstract

Appropriate crop type mapping to monitor and control land management is very important in developing countries. It can be very useful where digital cadaster maps are not available or usage of Remote Sensing (RS) data is not utilized in the process of monitoring and inventory. The main goal of the present research is to compare and assess the importance of optical RS data in crop type classification using medium and high spatial resolution RS imagery in 2018. With this goal, Landsat 8 (L8) and Sentinel-2 (S2) data were acquired over the Tashkent Province between the crop growth period of May and October. In addition, this period is the only possible time for having cloud-free satellite images. The following four indices “Normalized Difference Vegetation Index” (NDVI), “Enhanced Vegetation Index” (EVI), and “Normalized Difference Water Index” (NDWI1 and NDWI2) were calculated using blue, red, near-infrared, shortwave infrared 1, and shortwave infrared 2 bands. Support-Vector-Machine (SVM) and Random Forest (RF) classification methods were used to generate the main crop type maps. As a result, the Overall Accuracy (OA) of all indices was above 84% and the highest OA of 92% was achieved together with EVI-NDVI and the RF method of L8 sensor data. The highest Kappa Accuracy (KA) was found with the RF method of L8 data when EVI (KA of 88%) and EVI-NDVI (KA of 87%) indices were used. A comparison of the classified crop type area with Official State Statistics (OSS) data about sown crops area demonstrated that the smallest absolute weighted average (WA) value difference (0.2 thousand ha) was obtained using EVI-NDVI with RF method and NDVI with SVM method of L8 sensor data. For S2-sensor data, the smallest absolute value difference result (0.1 thousand ha) was obtained using EVI with RF method and 0.4 thousand ha using NDVI with SVM method. Therefore, it can be concluded that the results demonstrate new opportunities in the joint use of Landsat and Sentinel data in the future to capture high temporal resolution during the vegetation growth period for crop type mapping. We believe that the joint use of S2 and L8 data enables the separation of crop types and increases the classification accuracy. Full article

(This article belongs to the Special Issue Remote Sensing Satellites Data Analysis for Land Use / Land Cover (LULC) and Vegetation Monitoring)

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18 pages, 3746 KiB

Open AccessArticle

Modelling of Land Use/Cover and LST Variations by Using GIS and Remote Sensing: A Case Study of the Northern Pakhtunkhwa Mountainous Region, Pakistan

by Akhtar Rehman, Jun Qin, Sedra Shafi, Muhammad Sadiq Khan, Siddique Ullah, Khalid Ahmad, Nazir Ur Rehman and Muhammad Faheem

Sensors 2022, 22(13), 4965; https://doi.org/10.3390/s22134965 - 30 Jun 2022

Cited by 16 | Viewed by 3378

Abstract

Alteration in Land Use/Cover (LULC) considered a major challenge over the recent decades, as it plays an important role in diminishing biodiversity, altering the macro and microclimate. Therefore, the current study was designed to examine the past 30 years (1987–2017) changes in LULC and Land Surface Temperature (LST) and also simulated for next 30 years (2047). The LULC maps were developed based on maximum probability classification while the LST was retrieved from Landsat thermal bands and Radiative Transfer Equation (RTE) method for the respective years. Different approaches were used, such as Weighted Evidence (WE), Cellular Automata (CA) and regression prediction model for the year 2047. Resultantly, the LULC classification showed increasing trend in built-up and bare soil classes (13 km² and 89 km²), and the decreasing trend in vegetation class (−144 km²) in the study area. In the next 30 years, the built-up and bare soil classes would further rise with same speed (25 km² and 36.53 km²), and the vegetation class would further decline (−147 km²) until 2047. Similarly for LST, the temperature range for higher classes (27 -< 30 °C) increased by about 140 km² during 1987–2017, which would further enlarge (409 km²) until 2047. The lower LST range (15 °C to <21 °C) showed a decreasing trend (−54.94 km²) and would further decline to (−20 km²) until 2047 if it remained at the same speed. Prospective findings will be helpful for land use planners, climatologists and other scientists in reducing the increasing LST associated with LULC changes. Full article

(This article belongs to the Special Issue Remote Sensing Satellites Data Analysis for Land Use / Land Cover (LULC) and Vegetation Monitoring)

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19 pages, 7812 KiB

Open AccessArticle

Assessment of Climate Change and Human Activities on Vegetation Development in Northeast China

by Lin Xue, Martin Kappas, Daniel Wyss, Chaoqun Wang, Birgitta Putzenlechner, Nhung Pham Thi and Jiquan Chen

Sensors 2022, 22(7), 2509; https://doi.org/10.3390/s22072509 - 25 Mar 2022

Cited by 23 | Viewed by 3672

Abstract

Vegetation in Northeast China (NEC) has faced dual challenges posed by climate change and human activities. However, the factors dominating vegetation development and their contribution remain unclear. In this study, we conducted a comprehensive evaluation of the response of vegetation in different land cover types, climate regions, and time scales to water availability from 1990 to 2018 based on the relationship between normalized difference vegetation index (NDVI) and the standardized precipitation evapotranspiration index (SPEI). The effects of human activities and climate change on vegetation development were quantitatively evaluated using the residual analysis method. We found that the area percentage with positive correlation between NDVI and SPEI increased with time scales. NDVI of grass, sparse vegetation, rain-fed crop, and built-up land as well as sub-humid and semi-arid areas (drylands) correlated positively with SPEI, and the correlations increased with time scales. The negatively correlated area was concentrated in humid areas or areas covered by forests and shrubs. Vegetation water surplus in humid areas weakens with warming, and vegetation water constraints in drylands enhance. Moreover, potential evapotranspiration had an overall negative effect on vegetation, and precipitation was a controlling factor for vegetation development in semi-arid areas. A total of 53% of the total area in NEC showed a trend of improvement, which is mainly attributed to human activities (93%), especially through the implementation of ecological restoration projects in NEC. The relative role of human activities and climate change in vegetation degradation areas were 56% and 44%, respectively. Our findings highlight that the government should more explicitly consider the spatiotemporal heterogeneity of the influence of human activities and water availability on vegetation under changing climate and improve the resilience of regional water resources. The relative proportions and roles map of climate change and human activities in vegetation change areas provide a basis for government to formulate local-based management policies. Full article

(This article belongs to the Special Issue Remote Sensing Satellites Data Analysis for Land Use / Land Cover (LULC) and Vegetation Monitoring)

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