Agriculture must now feed the planet with the lowest environmental impact. Landscape management is a means to protect natural resources from the adverse impacts. In particular, the adequate management of ditches could improve crop quality. Here, we review ditch design and maintenance. We found the following major points: (1) ditch networks have been primarily designed for waterlogging control and erosion prevention. Nonetheless, when properly managed, farm ditches provide other important ecosystem services, namely groundwater recharge, flood attenuation, water purification, or biodiversity conservation. (2) All ditch ecosystem services depend on many geochemical, geophysical, and biological processes, whose occurrence and intensity vary largely with ditch characteristics. (3) The major ruling characteristics are vegetative cover; ditch morphology; slope orientation; reach connections such as piped sections and weirs, soil, sediment and litter properties, biota, and biofilms; and network topology. (4) Ditch maintenance is an efficient engineering tool to optimize ecosystem services because several ditch characteristics change widely with ditch maintenance. For instance, maintenance operations, dredging, chemical weeding, and burning improve waterlogging and soil erosion control, but they are negative for biodiversity conservation. Mowing has low adverse effects on biodiversity conservation and water purification when mowing is performed at an adequate season. The effects of burning have been poorly investigated.

Remote sensing has been used to map river bathymetry for several decades. Non-contact methods are necessary in several cases: inaccessible rivers, large-scale depth mapping, very shallow rivers. The remote sensing techniques used for river bathymetry are reviewed. Frequently, these techniques have been developed for marine environment and have then been transposed to riverine environments. These techniques can be divided into two types: active remote sensing, such as ground penetrating radar and bathymetric lidar; or passive remote sensing, such as through-water photogrammetry and radiometric models. This last technique — which consists of finding a logarithmic relationship between river depth and image values — appears to be the most used. Fewer references exist for the other techniques, but lidar is an emerging technique. For each depth measurement method, we detail the physical principles and then a review of the results obtained in the field. This review shows a lack of data for very shallow rivers, where a very high spatial resolution is needed. Moreover, the cost related to aerial image acquisition is often huge. Hence we propose an application of two techniques, radiometric models and through-water photogrammetry, with very- high-resolution passive optical imagery, light platforms, and off-the-shelf cameras. We show that, in the case of the radiometric models, measurement is possible with a spatial filtering of about 1 m and a homogeneous river bottom. In contrast, with through-water photogrammetry, fine ground resolution and bottom textures are necessary.

A simulator (Wa-LiD) was developed to simulate the reflection of LiDAR waveforms from water across visible wavelengths. The specific features of the simulator include 1) a geometrical representation of the water surface properties; 2) the use of laws of radiative transfer in water adjusted for wavelength and the water's physical properties; and 3) modeling of detection noise and signal level due to solar radiation. A set of simulated waveforms was compared with observed LiDAR waveforms acquired by the HawkEye airborne and Geoscience Laser Altimeter System (GLAS) satellite systems in the near infrared or green wavelengths and across inland or coastal waters. Signal-to-noise ratio (SNR) distributions for the water surface and bottom waveform peaks are compared with simulated and observed waveforms. For both systems (GLAS and HawkEye), Wa-LiD simulated SNR conform to the observed SNR distributions. Moreover, Wa-LiD showed a good ability to reproduce observed waveforms according to some realistic water parameters fitting.

The Global Ecosystem Dynamics Investigation LiDAR (GEDI) is a new full waveform (FW) based LiDAR system that presents a new opportunity for the observation of forest structures globally. The backscattered GEDI signals, as all FW systems, are distorted by topographic conditions within their footprint, leading to uncertainties on the measured forest variables. In this study, we explore how well several approaches based on waveform metrics and ancillary digital elevation model (DEM) data perform on the estimation of stand dominant heights ($H_{dom}$) and wood volume (V) across different sites of Eucalyptus plantations with varying terrain slopes. In total, five models were assessed on their ability to estimate $H_{dom}$ and four models for V. Results showed that the models using the GEDI metrics, such as the height at different energy quantiles with terrain data from the shuttle radar topography mission’s (SRTM) digital elevation model (DEM) were still dependent on the topographic slope. For $H_{dom}$, an RMSE increase of 14% was observed for data acquired over slopes higher than 20% in comparison to slopes between 10 and 20%. For V, a 74% increase in RMSE was reported between GEDI data acquired over slopes between 0–10% and those acquired over slopes higher than 10%. Next, a model relying on the height at different energy quantiles of the entire waveform ($H{T}_n$) and the height at different energy quartiles of the bare ground waveform ($H{G}_n$) was assessed. Two sets of the $H{G}_n$ metrics were generated, the first one was obtained using a simulated waveform representing the echo from a bare ground, while the second one relied on the actual ground return from the waveform by means of Gaussian fitting. Results showed that both the simulated and fitted models provide the most accurate estimates of $H_{dom}$ and V for all slope ranges. The simulation-based model showed an RMSE that ranged between 1.39 and 1.66 m (between 26.76 and 39.26 m³·ha⁻¹ for V) while the fitting-based method showed an RMSE that ranged between 1.26 and 1.34 m (between 26.78 and 36.29 m³·ha⁻¹ for V). Moreover, the dependency of the GEDI metrics on slopes was greatly reduced using the two sets of metrics. As a conclusion, the effect of slopes on the 25-m GEDI footprints is rather low as the estimation on canopy heights from uncorrected waveforms degraded by a maximum of 1 m for slopes between 20 and 45%. Concerning the wood volume estimation, the effect of slopes was more pronounced, and a degradation on the accuracy (increased RMSE) of a maximum of 20 m³·ha⁻¹ was observed for slopes between 20 and 45%.