Remote sensing and satellite geodetic observations are capable of hydrologic monitoring of freshwater resources. Although satellite radar altimetry has been used in monitoring water level or discharge, its use is often limited to monitoring large rivers (>1 km) with longer interval periods (>1 week) because of its low temporal and spatial resolutions (i.e., satellite revisit period). Several studies have reported successful retrieval of water levels for small rivers as narrow as 40 m. However, processing current satellite altimetry signals for such small water bodies to retrieve water levels accurately remains challenging. Physically, the radar signal returned by water bodies smaller than the satellite footprint is most likely contaminated by non-water surfaces, which may degrade the measurement quality. In order to address this scientific challenge, we carefully selected the waveform shapes corresponding to the range measurement resulting from standard retrackers for the European Space Agency's (ESA's) Envisat (Environmental Satellite) radar altimetry. We applied this approach to small (40–200 m in width) and medium-sized (200–800 m in width) rivers and small lakes (extent <1000 km2) in the humid tropics of Southeast Asia, specifically in Indonesia. This is the first study that explored the ability of satellite altimetry to monitor small water bodies in Indonesia. The major challenges in this study include the size of the water bodies that are much smaller than the nominal extent of the Envisat satellite footprint (e.g., ~250 m compared to ~1.7 km, respectively) and slightly smaller than the along-track distance (i.e., ~370 m). We addressed this challenge by optimally using geospatial information and optical remote sensing data to define the water bodies accurately, thus minimizing the probability of non-water contamination in the altimetry measurement. Considering that satellite altimetry processing may vary with different geographical regions, meteorological conditions, or hydrologic dynamic, we further evaluated the performance of all four Envisat standard retracking procedures. We found that satellite altimetry provided a good alternative or the only means in some regions of measuring the water level of medium-sized rivers and small lakes with high accuracy (root mean square error (RMSE) of 0.21–0.69 m and a correlation coefficient of 0.94–0.97). In contrast to previous studies, we found that the commonly used Ice-1 retracking algorithm was not necessarily the best retracker among the four standard waveform retracking algorithms for Envisat radar altimetry observing inland water bodies. As a recommendation, we propose to include the identification and selection of standard waveform shapes to complete the use of standard waveform retracking algorithms for Envisat radar altimetry data over small and medium-sized rivers and small lakes.

Seftianingrum, R.; Suwasono, R.A.; Sulistioadi, Y.B.; Suhardiman, A., and Diana, R., 2020. Floral composition of the Kayan-Sembakung Delta in North Kalimantan (Indonesia) in different disturbance regimes. Journal of Coastal Research, 36(4), 741–751. Coconut Creek (Florida), ISSN 0749-0208.This paper presents the results of a preliminary study that identify the floral composition of the Kayan-Sembakung Delta in North Kalimantan under different disturbance regimes. The landforms described in the Regional Physical Planning Project for Transmigration in Indonesia land-system map were evaluated to define the mangrove ecosystem boundary. The mangrove ecosystem disturbance levels distinguished by reclassifying the land-cover map of the mangrove ecosystem into four categories, i.e. heavily disturbed mangrove (represented by shrimp ponds abandoned five years ago or less), moderately disturbed mangrove (represented by shrimp ponds abandoned more than five years ago), regrown (secondary) mangrove forest (that may have been logged for wood but did not experience terrain alteration for fishpond development), and relatively undisturbed (primary) mangrove forest. A vegetation survey was conducted in several transects for each of these categories. Basal area, density, species diversity, and abundance of species in each location were calculated to understand the floral composition of the mangrove ecosystem. The study reveals that the mangrove forest in the Simanggaris River has the highest total basal area of 44.50 m2/ha, as well as the highest tree density of 1635 individual/ha, which indicates the most dense and mature remaining mangrove forest followed by the Sikang River near Liago Village with 85.75% of canopy cover. The heavily degraded mangrove ecosystem in Ibus Island has the highest importance value index of 278.89%, with Sonneratia sp. as the dominant species. The Kayan-Sembakung Delta hosts at least six original mangrove species, i.e. Rhizophora apiculata, Osbornia octodonta, Lumnitzera littorea, Avicennia sp, Rhizophora sp, and Sonneratia sp. In addition to the six original mangrove species listed, the inner part of the Kayan Delta also has several forest patches dominated by Nypa fruticans, whereas some parts of the Delta host a mixed vegetation composition between freshwater and brackish water ecosystems.

Remote sensing and satellite geodetic observations are capable for hydrologic monitoring of freshwater resources. For the case of satellite radar altimetry, limited temporal resolutions (e.g., satellite revisit period) prohibit the use of this method for a short (< weekly) interval monitoring of water level or discharge. On the other hand, the current satellite radar altimeter footprints limit the water level measurement for rivers wider than 1 km. Some studies indeed reported successful retrieval of water level for small-size rivers as narrow as 80 m; however, the processing of current satellite altimetry signals for small water bodies to retrieve accurate water levels, remains challenging. To address this scientific challenge, this study tries to monitor small (40–200 m width) and medium-sized (200–800 m width) rivers and lakes using satellite altimetry through identification and choice of the over-water radar waveforms corresponding to the appropriately waveform-retracked water level. This study addresses the humid tropics of Southeast Asia, specifically in Indonesia, where similar studies do not yet exist and makes use Level 2 radar altimeter measurements generated by European Space Agency's (ESA's) Envisat (Environmental Satellite) mission. This experiment proves that satellite altimetry provides a good alternative, or the only means in some regions, to measure the water level of medium-sized river (200–800 m width) and small lake (extent < 1000 km2) in Southeast Asia humid tropic with reasonable accuracy. In addition, the procedure to choose retracked Envisat altimetry water level heights via identification or selection of standard waveform shapes for inland water is recommended and should be a standard measure especially over small rivers and lakes. This study also found that Ice-1 is not necessarily the best retracker as reported by previous studies, among the four standard waveform retracking algorithms for Envisat radar altimetry observing inland water bodies.

Measurement of forest carbon on the ground in a large scale is time-consuming and expensive. This research utilized remote sensing with Sentinel-2B image through its Vegetation Index and related those values with those obtained from field measurements and Terrestrial Laser Scanner (TLS). This study specifically compares the forest stand carbon value using TLS and field measurements and link it to the vegetation index. This study transforms the vegetation indices, i.e.: Transformed Vegetation Index (TVI), Normalized Difference Vegetation Index (NDVI) and Simple Ratio (SR) from Sentinel-2B imagery and relates them with field measurements and TLS. The calculation of carbon in forest stands using TLS revealed an average carbon of 151.35 ton/Ha. In contrast, in the field measurements, carbon was measured at 149.81 ton/Ha, and statistically, there was no difference between these two measurements. The relationship between the vegetation index and field measurements showed the best coefficient of correlation of TVI with (r) = 0.784 and (R2) = 0.524. The relationship between the vegetation index and TLS measurements showed the best coefficient of correlation of TVI with (r) = 0.759 and the coefficient of determination (R2) = 0.577.