Comparing canopy density measurement from UAV and hemispherical photography: an evaluation for medium resolution of remote sensing-based mapping

UAV and hemispherical photography are common methods used in canopy density measurement. These two methods have opposite viewing angles where hemispherical photography measures canopy density upwardly, while UAV captures images downwardly. This study aims to analyze and compare both methods to be used as the input data for canopy density estimation when linked with a lower spatial resolution of remote sensing data i.e. Landsat image. We correlated the field data of canopy density with vegetation indices (NDVI, MSAVI, and AFRI) from Landsat-8. The canopy density values measured from UAV and hemispherical photography displayed a strong relationship with 0.706 coefficient of correlation. Further results showed that both measurements can be used in canopy density estimation using satellite imagery based on their high correlations with Landsat-based vegetation indices. The highest correlation from downward and upward measurement appeared when linked with NDVI with a correlation of 0.962 and 0.652, respectively. Downward measurement using UAV exhibited a higher relationship compared to hemispherical photography. The strong correlation between UAV data and Landsat data is because both are captured from the vertical direction, and 30 m pixel of Landsat is a downscaled image of the aerial photograph. Moreover, field data collection can be easily conducted by deploying drone to cover inaccessible sample plots

Monitoring forest gain and loss based on LandTrendr algorithm and Landsat images in KTH Pati social forestry area, Indonesia

Social forestry schemes are now being implemented in numerous state forest areas in Indonesia, aiming to reduce deforestation and improve the community’s livelihood. However, spatial monitoring in the social forestry area is still limited to see how the implementation progresses. The present study aimed to identify the change of forest taking a case in Pati Forest Farmer Communities (KTH Pati) social forestry area from 1996 to 2022 using the LandTrendr algorithm based on Normalized Burn Ratio (NBR) value of Landsat image series. The results detected forest loss and gain covering an area of 453.97 ha and 494.18 ha, respectively. Two main reasons causing the forest loss are the country’s financial and political situation from 1997 to 2003 and the harvest of forest plantations in 2017–2018. However, it was found that the study area had a positive forest gain with the current continuous growth of 292.32 ha (20.16% of the total area). Even though the social forestry policy has not significantly shown a positive impact on forest growth, spatial monitoring through remote sensing can be a great tool for observing the progress

Can iPhone/iPad LiDAR data improve canopy height model derived from UAV?

Aerial images resulting from unmanned aerial vehicle (UAV) are widely used to estimate tree height. The filtering method is required to distinguish between ground and off-ground point clouds to generate a canopy height model. However, the filtering method is not always perfect since UAV data cannot penetrate canopies into the forest floor. The release of iPhone/iPad devices with built-in LiDAR sensors enables the more affordable use of LiDAR for forestry study, including the measurement of local topography below forest stands. This study investigates to what extent iPhone/iPad LiDAR can improve the accuracy of canopy height model from the UAV. The integration of UAV and iPhone/iPad LiDAR data managed to increase the accuracy of tree height model with a mean absolute error (MAE) of 2.188 m, compared to UAV data (MAE = 2.446 m). This preliminary study showed the potential of combining UAV and iPhone/iPad LiDAR data for estimating tree height

Monitoring Slope Creep Motion using Multi Temporal Interferometry Synthetic Aperture Radar in Semarang, Indonesia

Longsor merupakan salah satu jenis pergerakan lereng, dimana pergerakan lereng tersebut termasuk rayapan. Meskipun rayapan tidak berdampak pada resiko korban jiwa, namun pergerakan lereng ini berlangsung secara konstan dan tak terlihat yang berdampak pada kerugian ekonomi. Pada penelitain ini dilakukan suatu pemantauan seri waktu (time-series monitoring) dari tahun 2018 hingga 2020 untuk melihat pergerakan lereng pada daerah penelitian dengan menggunakan metode seri waktu Multi-Temporal Interferometry Synthetic Aperture Radar (MTInSAR) dari data Sentinel 1A/B, yang mencakup Perumahan Trangkil Sejahtera (PTS), Universitas Katolik Soegijapranata (UNIKA), dan Universitas 17 Agustus 1945 (UNTAG) di Kota Semarang, Indonesia. Hasil pengolahan data menunjukkan bahwa terdapat rayapan pada target lokasi, yaitu Perumahan Trangkil Sejahtera dan Selorejo (sebelah Baratdaya UNIKA). Berdasarkan data BPBD 2021, terjadi longsor di Perumahan Trangkil Baru (sebelah Utara PTS) dan talud longsor Sungai Garang sebelah barat Selorejo. Hal ini menunjukkan adanya keterkaitan antara rayapan tahun 2018-2020 dengan kejadian longsor pada tahun 2021. Meskipun penggunaan data satelit memiliki beberapa kekurangan, hasilnya dapat menjadi bahan pertimbangan dalam membangun sistem peringatan dini dan mengurangi kerugian akibat longsor.Landslide is one type of slope movement, where the slope movement includes creep. Although creep movement does not have an impact on the risk of loss of life, this creep movement takes place constantly and  invisible which has an impact on economic losses. In this study, a time-series monitoring was carried out from 2018 to 2020 to see the movement of the slopes in the study area using the Multi-Temporal Interferometry Synthetic Aperture Radar (MTInSAR). A time series method from Sentinel 1A/B data, which includes Trangkil Sejahtera Housing (PTS), Soegijapranata Catholic University (UNIKA), and 17 August 1945 University (UNTAG) in Semarang City, Indonesia. The results of data processing indicate that there are slope movement in the target location, namely Trangkil Sejahtera and Selorejo Housing (southwest of UNIKA). Based on BPBD 2021 data, landslides occurred in the Trangkil Baru Housing Center (to the north of PTS) and the Garang River landslide channel west of Selorejo. This shows that there is a link between crawling in 2018-2020 and landslides in 2021. Although the use of satellite data has some drawbacks, the results can be taken into consideration in building an early warning system and reducing losses due to landslides

The Effect of Topographic Correction on Canopy Density Mapping Using Satellite Imagery in Mountainous Area

One of the main factors contributing to radiometric distortion on remote sensing data is topographic effect, but it can be reduced by applying topographic correction. This study identifies the effect of topographic correction on canopy density mapping in Menoreh Mountains, Indonesia. Topographic correction methods examined in this research are C-Correction, Minnaert, and Sun-Canopy-Sensor+C (SCS+C). Canopy density estimation was approached using vegetation indices, i.e. Normalized Difference Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index (MSAVI), Aerosol Free Vegetation Index (AFRI) 1.6, and AFRI 2.1 derived from Landsat-8 OLI imagery. We evaluated the performance of topographic correction by visual and statistical analysis prior to comparing the accuracy of canopy density estimation of different vegetation indices and correction methods. The results showed that topographic normalization is able to increase the accuracy of canopy density mapping. The most significant improvement is the model using MSAVI which increases 1.207% using Minnaert method to reach 86.692% accuracy. Meanwhile, NDVI, AFRI 1.6, and AFRI 2.1 have less significant improvement with the maximum increase of 0.241%, 0.057%, and 0.032% using SCS+C method, reaching the accuracy of 88.980%, 83.303%, and 82.308%, respectively. The accuracies were slightly improved since the algorithms have already reduced the effect of topography which are categorized as ratio vegetation indices. SCS+C is the best topographic correction method, because of not only the appropriate assumption of canopy normalization but also its consistency and better accuracy in canopy density estimation among other methods

Impact of COVID-19 Lockdown on the Fisheries Sector: A Case Study from Three Harbors in Western India

The COVID-19 related lockdowns have brought the planet to a standstill. It has severely shrunk the global economy in the year 2020, including India. The blue economy and especially the small-scale fisheries sector in India have dwindled due to disruptions in the fish catch, market, and supply chain. This research presents the applicability of satellite data to monitor the impact of COVID-19 related lockdown on the Indian fisheries sector. Three harbors namely Mangrol, Veraval, and Vankbara situated on the north-western coast of India were selected in this study based on characteristics like harbor’s age, administrative control, and availability of cloud-free satellite images. To analyze the impact of COVID in the fisheries sector, we utilized high-resolution PlanetScope data for monitoring and comparison of “area under fishing boats” during the pre-lockdown, lockdown, and post-lockdown phases. A support vector machine (SVM) classification algorithm was used to identify the area under the boats. The classification results were complemented with socio-economic data and ground-level information for understanding the impact of the pandemic on the three sites. During the peak of the lockdown, it was found that the “area under fishing boats” near the docks and those parked on the land area increased by 483%, 189%, and 826% at Mangrol, Veraval, and Vanakbara harbor, respectively. After phase-I of lockdown, the number of parked vessels decreased, yet those already moved out to the land area were not returned until the south-west monsoon was over. A quarter of the annual production is estimated to be lost at the three harbors due to lockdown. Our last observation (September 2020) result shows that regular fishing activity has already been re-established in all three locations. PlanetScope data with daily revisit time has a higher potential to be used in the future and can help policymakers in making informed decisions vis-à-vis the fishing industry during an emergency situation like COVID-19

Exploring Built-Up Indices and Machine Learning Regressions for Multi-Temporal Building Density Monitoring Based on Landsat Series

Uncontrolled built-up area expansion and building densification could bring some detrimental problems in social and economic aspects such as social inequality, urban heat islands, and disturbance in urban environments. This study monitored multi-decadal building density (1991– 2019) in the Yogyakarta urban area, Indonesia consisting of two stages, i.e., built-up area classification and building density estimation, therefore, both built-up expansion and the densification were quantified. Multi sensors of the Landsat series including Landsat 5, 7, and 8 were utilized with some prior corrections to harmonize the reflectance values. A support vector machine (SVM) classifier was used to distinguish between built-up and non built-up areas. Regression algorithms, i.e., linear regression (LR), support vector regression (SVR), and random forest regression (RFR) were explored to obtain the best model to estimate building density using the inputs of built-up indices: Urban Index (UI), Normalized Difference Built-up Index (NDBI), Index-based Built-up Index (IBI), and NIR-based built-up index based on the red (VrNIR-BI) and green band (VgNIR-BI). The best models were revealed by SVR with the inputs of UI-NDBI-IBI and LR with a single predictor of UI, for Landsat 8 (2013–2019) and Landsat 5/7 (1991–2009), respectively, using separate training samples. We found that machine learning regressions (SVM and RF) could perform best when the sample size is abundant, whereas LR could predict better for a limited sample size if a linear positive relationship was identified between the predictor(s) and building density. We conclude that expansion in the study area occurred first, followed by rapid building development in the subsequent years leading to an increase in building density

Sentinel-1 and Sentinel-2 data fusion to distinguish building damage level of the 2018 Lombok Earthquake

Following an earthquake, Building Damage Assessment (BDA) is crucial in detecting areas that need immediate rescue actions and planning for the best evacuation strategy. Remote sensing has been widely used for BDA. The availability of Sentinel-1 and Sentinel-2 images that are freely accessible could enhance remote sensing applications for building damage classification caused by natural disasters. The accuracy of using the two satellite images for BDA mapping in tropical regions is still uncertain. This research aims to assess Sentinel-1 and Sentinel-2 data fusion performance for BDA mapping following the catastrophic 2018 Lombok Earthquake. Pre- and post-earthquake images of Sentinel-1 and Sentinel-2 were selected, preprocessed, and integrated using the random forest classifier. Three data scenarios were used in this study, i.e., Sentinel-1, Sentinel-2, and the fusion of both datasets. The results demonstrated that all models showed excellent performance in classifying destroyed buildings compared to severely and moderately damaged buildings. This is due to the medium spatial resolution of the images could not identify the damage level in detail. Sentinel-1 and Sentinel-2 data fusion provided the highest overall accuracy value (62.4%) compared to other single dataset models. According to the feature importance analysis result, GLCM Mean, GLCM Variance, and NIR Band played crucial roles in the damage classification. In conclusion, the fusion of Sentinel-1 and Sentinel-2 can provide the best model for destroyed building mapping. However, severely and moderately building damaged mapping can only be accurately mapped using higher spatial resolution images

Cave Entrance Location Model Using Binary Logistic Regression: The Case Study of South Gombong Karst Region, Indonesia

Cave entrance data are crucial as the primary indicators in the underground water inventory of a karst area. The data collection was traditionally conducted by field survey, but it is very costly and not efficient. Remote sensing and Geographic Information System (GIS) can help estimate cave entrance locations more efficiently. In this study, variables for cave entrance identification were determined using remote sensing and GIS. In addition, the accuracy of the Cave Entrance Location Model (CELM) derived from binary logistic regression was examined. Several remote sensing and geological data were used including ALOS PALSAR Digital Elevation Model (DEM), Digital Elevation Model Nasional (DEMNAS), topographic and geological map. Topographic elements were extracted by using Toposhape and Topographic Position Index (TPI). Contours derived from the topographic map showed the highest accuracy for extraction of topographic elements compared to ALOS PALSAR DEM and DEMNAS, hence it was used for further analysis. Binary logistic regression was applied to estimate the probability of cave entrance locations based on the variables used. The result shows that three topographic variables: ravine, stream, and midslope drainage had a significant value for estimating cave entrance location. Using these variables, logit equation was formulated to generate a probability map. The result shows that cave entrances are likely to be located in a dry valley. The accuracy assessment using the field data showed that 52.77% of cave entrances are located in medium to high potential areas. This suggests that the moderatehigh potential area can indicate potential water resources in karst are

Kite Aerial Photography (KAP) for rip current identification in Parangtritis Beach

Rip current is being the major cause of the deadly accidents in Parangtritis Beach. This occasion can be prevented by mapping and monitoring the spatial pattern of rip currents at the location which rip currents are located. Rip current location can be identified by remote sensing data or aerial observations, such as Kite Aerial Photography (KAP). This platform is low cost and can be performed in coastal area due to the massive winds there. KAP has been widely used as the platform for mapping, and some of them are implemented in coastal area. This study aims to find out the ability of Kite aerial photography to identify the rip current location in Parangtritis Beach. From several flight tests, the photo mosaic of Parangtritis Beach has been generated after the KAP has flown at the minimum 3 m/s of the wind speed. KAP can be the great potentials in coastal monitoring, especially for rip current monitoring because it is low-cost, low-energy and provides actual information